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Feuerriegel S, Frauen D, Melnychuk V, Schweisthal J, Hess K, Curth A, Bauer S, Kilbertus N, Kohane IS, van der Schaar M. Causal machine learning for predicting treatment outcomes. Nat Med 2024; 30:958-968. [PMID: 38641741 DOI: 10.1038/s41591-024-02902-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 03/04/2024] [Indexed: 04/21/2024]
Abstract
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. Causal ML can be used in combination with both clinical trial data and real-world data, such as clinical registries and electronic health records, but caution is needed to avoid biased or incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps. Finally, we provide recommendations for the reliable use of causal ML and effective translation into the clinic.
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Affiliation(s)
- Stefan Feuerriegel
- LMU Munich, Munich, Germany.
- Munich Center for Machine Learning, Munich, Germany.
| | - Dennis Frauen
- LMU Munich, Munich, Germany
- Munich Center for Machine Learning, Munich, Germany
| | - Valentyn Melnychuk
- LMU Munich, Munich, Germany
- Munich Center for Machine Learning, Munich, Germany
| | - Jonas Schweisthal
- LMU Munich, Munich, Germany
- Munich Center for Machine Learning, Munich, Germany
| | - Konstantin Hess
- LMU Munich, Munich, Germany
- Munich Center for Machine Learning, Munich, Germany
| | - Alicia Curth
- Department of Applied Mathematics & Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Stefan Bauer
- School of Computation, Information and Technology, TU Munich, Munich, Germany
- Helmholtz Munich, Munich, Germany
| | - Niki Kilbertus
- Munich Center for Machine Learning, Munich, Germany
- School of Computation, Information and Technology, TU Munich, Munich, Germany
- Helmholtz Munich, Munich, Germany
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Mihaela van der Schaar
- Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
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2
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Beaulieu-Jones BK, Frau F, Bozzi S, Chandross KJ, Peterschmitt MJ, Cohen C, Coulovrat C, Kumar D, Kruger MJ, Lipnick SL, Fitzsimmons L, Kohane IS, Scherzer CR. Disease progression strikingly differs in research and real-world Parkinson's populations. NPJ Parkinsons Dis 2024; 10:58. [PMID: 38480700 PMCID: PMC10937726 DOI: 10.1038/s41531-024-00667-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 02/23/2024] [Indexed: 03/17/2024] Open
Abstract
Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data, and advances in natural language processing, particularly large language models, allow for a more granular comparison of populations than previously possible. This study includes two research populations and two real-world data-derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using a manually validated natural language processing with a large language model to extract measurements of PD progression. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p < 0.001; mini-mental state exam median decline 0.28 vs. 0.11, p < 0.001; and clinically recognized cognitive decline, p = 0.001). In real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p < 0.001). After diagnosis, in real-world cohorts, treatment with PD medications has initiated an average of 2.3 years later (95% CI: [2.1-2.4]; p < 0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real-world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.
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Affiliation(s)
- Brett K Beaulieu-Jones
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
- APDA Center for Advanced Parkinson Research of Harvard Medical School and Brigham and Women's Hospital, Boston, MA, 02115, USA.
- Precision Neurology Program of Brigham & Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- Department of Medicine, University of Chicago, Chicago, IL, 60615, USA.
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.
| | | | - Sylvie Bozzi
- Sanofi Health Economics and Value Assessment, Sanofi, Paris, France
| | | | | | | | | | - Dinesh Kumar
- Sanofi Translational Sciences, Framingham, MA, 01701, USA
| | - Mark J Kruger
- Sanofi Genzyme, Clinical Development Neurology, Cambridge, MA, USA
| | - Scott L Lipnick
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Lane Fitzsimmons
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Clemens R Scherzer
- APDA Center for Advanced Parkinson Research of Harvard Medical School and Brigham and Women's Hospital, Boston, MA, 02115, USA.
- Precision Neurology Program of Brigham & Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.
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3
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Goldberg CB, Adams L, Blumenthal D, Brennan PF, Brown N, Butte AJ, Cheatham M, deBronkart D, Dixon J, Drazen J, Evans BJ, Hoffman SM, Holmes C, Lee P, Manrai AK, Omenn GS, Perlin JB, Ramoni R, Sapiro G, Sarkar R, Sood H, Vayena E, Kohane IS. To do no harm - and the most good - with AI in health care. Nat Med 2024; 30:623-627. [PMID: 38388841 DOI: 10.1038/s41591-024-02853-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Affiliation(s)
| | - Laura Adams
- National Academy of Medicine, Washington, DC, USA
| | - David Blumenthal
- Department of Health Policy and Management, Harvard University, T.H. Chan School of Public Health, Boston, MA, USA
| | - Patricia Flatley Brennan
- National Library of Medicine, Bethesda, MD, USA
- University of Wisconsin-Madison, Madison, WI, USA
| | - Noah Brown
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Atul J Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Morgan Cheatham
- Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Dave deBronkart
- e-Patient Dave, LLC, Nashua, NH, USA
- Society for Participatory Medicine, Pembroke, MA, USA
| | | | | | - Barbara J Evans
- Levin College of Law and Wertheim College of Engineering, University of Florida, Gainesville, FL, USA
| | - Sara M Hoffman
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Chris Holmes
- Department of Statistics and Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - Peter Lee
- Microsoft Corporation, Redmond, WA, USA
| | - Arjun Kumar Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- NEJM Group, Waltham, MA, USA
| | - Gilbert S Omenn
- University of Michigan Health System, University of Michigan, Ann Arbor, MI, USA
| | | | - Rachel Ramoni
- U.S. Department of Veterans Affairs, Washington, DC, USA
| | | | - Rupa Sarkar
- The Lancet Ltd., Lancet Digital Health, London, UK
| | - Harpreet Sood
- National Health Service England, Hurley Group, Redditch, UK
- Huma, London, UK
| | | | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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4
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Beaulieu-Jones BK, Frau F, Bozzi S, Chandross KJ, Peterschmitt MJ, Cohen C, Coulovrat C, Kumar D, Kruger MJ, Lipnick SL, Fitzsimmons L, Kohane IS, Scherzer CR. Disease progression strikingly differs in research and real-world Parkinson's populations. medRxiv 2024:2024.02.17.24302981. [PMID: 38405736 PMCID: PMC10889035 DOI: 10.1101/2024.02.17.24302981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data and recent advances in natural language processing, particularly large language models, allow for a more granular comparison of populations and the methods of data collection describing these populations than previously possible. This study includes two research populations and two real-world data derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using natural language processing with a large language model to extract measurements of PD progression. This extraction process is manually validated for accuracy. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p<0.001; mini-mental state exam median decline 0.28 vs. 0.11, p<0.001; and clinically recognized cognitive decline, p=0.001). In the real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p<0.001). After diagnosis, in real-world cohorts, treatment with PD medications is initiated 2.3 years later on average (95% CI: [2.1-2.4]; p<0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging using existing data. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.
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Affiliation(s)
- Brett K Beaulieu-Jones
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
- APDA Center for Advanced Parkinson Research of Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
- Precision Neurology Program of Brigham & Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Medicine, University of Chicago, Chicago, IL 60615 USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase MD, 20815
| | | | - Sylvie Bozzi
- Sanofi Health Economics and Value Assessment, Sanofi, Paris, France
| | | | | | | | | | - Dinesh Kumar
- Sanofi Translational Sciences, Framingham, 01701 USA
| | - Mark J Kruger
- Sanofi Genzyme, Clinical Development Neurology, Cambridge, MA, United States
| | - Scott L Lipnick
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Lane Fitzsimmons
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Clemens R Scherzer
- APDA Center for Advanced Parkinson Research of Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
- Precision Neurology Program of Brigham & Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase MD, 20815
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5
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Nadimpalli Kobren S, Moldovan MA, Reimers R, Traviglia D, Li X, Barnum D, Veit A, Willett J, Berselli M, Ronchetti W, Sherwood R, Krier J, Kohane IS, Sunyaev SR. Joint, multifaceted genomic analysis enables diagnosis of diverse, ultra-rare monogenic presentations. bioRxiv 2024:2024.02.13.580158. [PMID: 38405764 PMCID: PMC10888768 DOI: 10.1101/2024.02.13.580158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Genomics for rare disease diagnosis has advanced at a rapid pace due to our ability to perform "N-of-1" analyses on individual patients. The increasing sizes of ultra-rare, "N-of-1" disease cohorts internationally newly enables cohort-wide analyses for new discoveries, but well-calibrated statistical genetics approaches for jointly analyzing these patients are still under development.1,2 The Undiagnosed Diseases Network (UDN) brings multiple clinical, research and experimental centers under the same umbrella across the United States to facilitate and scale N-of-1 analyses. Here, we present the first joint analysis of whole genome sequencing data of UDN patients across the network. We apply existing and introduce new, well-calibrated statistical methods for prioritizing disease genes with de novo recurrence and compound heterozygosity. We also detect pathways enriched with candidate and known diagnostic genes. Our computational analysis, coupled with a systematic clinical review, recapitulated known diagnoses and revealed new disease associations. We make our gene-level findings and variant-level information across the cohort available in a public-facing browser (https://dbmi-bgm.github.io/udn-browser/). These results show that N-of-1 efforts should be supplemented by a joint genomic analysis across cohorts.
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Affiliation(s)
| | | | | | - Daniel Traviglia
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Xinyun Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT
| | | | - Alexander Veit
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Julian Willett
- Department of Neurology, Massachusetts General Hospital, Boston, MA
| | - Michele Berselli
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - William Ronchetti
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Richard Sherwood
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Joel Krier
- Department of Genetics, Atrius Health, Boston, MA
| | - Isaac S. Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | | | - Shamil R Sunyaev
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
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6
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Kohane IS, Churchill S, Tan ALM, Vella M, Perry CL. The digital-physical divide for pathology research. Lancet Digit Health 2023; 5:e859-e861. [PMID: 38000870 DOI: 10.1016/s2589-7500(23)00184-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 09/01/2023] [Accepted: 09/13/2023] [Indexed: 11/26/2023]
Affiliation(s)
- Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
| | - Susanne Churchill
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Amelia Li Min Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Margaret Vella
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Cassandra L Perry
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
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7
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Alsentzer E, Finlayson SG, Li MM, Kobren SN, Kohane IS. Simulation of undiagnosed patients with novel genetic conditions. Nat Commun 2023; 14:6403. [PMID: 37828001 PMCID: PMC10570269 DOI: 10.1038/s41467-023-41980-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 09/26/2023] [Indexed: 10/14/2023] Open
Abstract
Rare Mendelian disorders pose a major diagnostic challenge and collectively affect 300-400 million patients worldwide. Many automated tools aim to uncover causal genes in patients with suspected genetic disorders, but evaluation of these tools is limited due to the lack of comprehensive benchmark datasets that include previously unpublished conditions. Here, we present a computational pipeline that simulates realistic clinical datasets to address this deficit. Our framework jointly simulates complex phenotypes and challenging candidate genes and produces patients with novel genetic conditions. We demonstrate the similarity of our simulated patients to real patients from the Undiagnosed Diseases Network and evaluate common gene prioritization methods on the simulated cohort. These prioritization methods recover known gene-disease associations but perform poorly on diagnosing patients with novel genetic disorders. Our publicly-available dataset and codebase can be utilized by medical genetics researchers to evaluate, compare, and improve tools that aid in the diagnostic process.
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Grants
- U01 HG007690 NHGRI NIH HHS
- U54 NS108251 NINDS NIH HHS
- U01 HG010219 NHGRI NIH HHS
- U01 HG007672 NHGRI NIH HHS
- U01 HG010233 NHGRI NIH HHS
- U01 HG010230 NHGRI NIH HHS
- U01 HG007943 NHGRI NIH HHS
- U01 HG010217 NHGRI NIH HHS
- U01 HG007942 NHGRI NIH HHS
- U01 HG010215 NHGRI NIH HHS
- U01 HG007708 NHGRI NIH HHS
- T32 HG002295 NHGRI NIH HHS
- T32 GM007753 NIGMS NIH HHS
- U01 HG007674 NHGRI NIH HHS
- U01 TR001395 NCATS NIH HHS
- U01 HG007709 NHGRI NIH HHS
- U54 NS093793 NINDS NIH HHS
- U01 HG007530 NHGRI NIH HHS
- U01 TR002471 NCATS NIH HHS
- U01 HG007703 NHGRI NIH HHS
- UDN research reported in this manuscript was supported by the NIH Common Fund, through the Office of Strategic Coordination/Office of the NIH Director under Award Number(s) U01HG007709, U01HG010219, U01HG010230, U01HG010217, U01HG010233, U01HG010215, U01HG007672, U01HG007690, U01HG007708, U01HG007703, U01HG007674, U01HG007530, U01HG007942, U01HG007943, U01TR001395, U01TR002471, U54NS108251, and U54NS093793.
- E.A. is supported by a Microsoft Research PhD Fellowship.
- S.F. is supported by award Number T32GM007753 from the National Institute of General Medical Sciences.
- M.L. is supported by T32HG002295 from the National Human Genome Research Institute and a National Science Foundation Graduate Research Fellowship.
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Affiliation(s)
- Emily Alsentzer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
- Program in Health Sciences and Technology, MIT, Cambridge, MA, 02139, USA
| | - Samuel G Finlayson
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
- Program in Health Sciences and Technology, MIT, Cambridge, MA, 02139, USA
- Department of Pediatrics, Division of Genetic Medicine, Seattle Children's Hospital, Seattle, WA, 98105, USA
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, 98105, USA
| | - Michelle M Li
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
- Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, 02115, USA
| | - Shilpa N Kobren
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
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8
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Argaw PN, Kushner JA, Kohane IS. Unsupervised Anomaly Detection to Characterize Heterogeneity in Type 2 Diabetes. AMIA Jt Summits Transl Sci Proc 2023; 2023:32-41. [PMID: 37350904 PMCID: PMC10283093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
Diabetes is associated with heterogeneous behaviors affecting patients' clinical characteristics and trajectories. This study includes 21,288 patients with type 2 diabetes (women, ages 30 to 65). The cohort was filtered through a set of preprocessing heuristics in order to assure the cohort exhibited a similar clinical trajectory. Anomalous characteristics were then identified using dimensionality reduction and anomaly detection methods. Compared to the majority of the cohort, patients classified as anomalous were twice as likely to be admitted into the hospital (7.94[7.59 8.28] versus 3.12[3.06 3.17] times), have a higher incidence of comorbidities (2[1.64 2.36] times more), and be prescribed more insulin and less new and more expensive diabetes medications (such as Sodium glucose co-transporter 2 inhibitors). Patients with these anomalous characteristics may benefit from additional or specialized interventions to avert their risk for adverse outcomes.
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Affiliation(s)
- Peniel N Argaw
- Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA
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9
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Zhang HG, Honerlaw JP, Maripuri M, Samayamuthu MJ, Beaulieu-Jones BR, Baig HS, L'Yi S, Ho YL, Morris M, Panickan VA, Wang X, Weber GM, Liao KP, Visweswaran S, Tan BWQ, Yuan W, Gehlenborg N, Muralidhar S, Ramoni RB, Kohane IS, Xia Z, Cho K, Cai T, Brat GA. Potential pitfalls in the use of real-world data for studying long COVID. Nat Med 2023; 29:1040-1043. [PMID: 37055567 PMCID: PMC10205658 DOI: 10.1038/s41591-023-02274-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Affiliation(s)
- Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA, USA
| | - Jacqueline P Honerlaw
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Monika Maripuri
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | | | | | - Huma S Baig
- Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Katherine P Liao
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bryce W Q Tan
- Department of Medicine, National University Hospital, Singapore, Singapore, Singapore
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sumitra Muralidhar
- Office of Research and Development, US Department of Veterans Affairs, Washington DC, USA
| | - Rachel B Ramoni
- Office of Research and Development, US Department of Veterans Affairs, Washington DC, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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Srivastava S, Shaked HM, Gable K, Gupta SD, Pan X, Somashekarappa N, Han G, Mohassel P, Gotkine M, Doney E, Goldenberg P, Tan QKG, Gong Y, Kleinstiver B, Wishart B, Cope H, Pires CB, Stutzman H, Spillmann RC, Sadjadi R, Elpeleg O, Lee CH, Bellen HJ, Edvardson S, Eichler F, Dunn TM, Dai H, Dhar SU, Emrick LT, Goldman AM, Hanchard NA, Jamal F, Karaviti L, Lalani SR, Lee BH, Lewis RA, Marom R, Moretti PM, Murdock DR, Nicholas SK, Orengo JP, Posey JE, Potocki L, Rosenfeld JA, Samson SL, Scott DA, Tran AA, Vogel TP, Wangler MF, Yamamoto S, Eng CM, Liu P, Ward PA, Behrens E, Deardorff M, Falk M, Hassey K, Sullivan K, Vanderver A, Goldstein DB, Cope H, McConkie-Rosell A, Schoch K, Shashi V, Smith EC, Spillmann RC, Sullivan JA, Tan QKG, Walley NM, Agrawal PB, Beggs AH, Berry GT, Briere LC, Cobban LA, Coggins M, Cooper CM, Fieg EL, High F, Holm IA, Korrick S, Krier JB, Lincoln SA, Loscalzo J, Maas RL, MacRae CA, Pallais JC, Rao DA, Rodan LH, Silverman EK, Stoler JM, Sweetser DA, Walker M, Walsh CA, Esteves C, Kelley EG, Kohane IS, LeBlanc K, McCray AT, Nagy A, Dasari S, Lanpher BC, Lanza IR, Morava E, Oglesbee D, Bademci G, Barbouth D, Bivona S, Carrasquillo O, Chang TCP, Forghani I, Grajewski A, Isasi R, Lam B, Levitt R, Liu XZ, McCauley J, Sacco R, Saporta M, Schaechter J, Tekin M, Telischi F, Thorson W, Zuchner S, Colley HA, Dayal JG, Eckstein DJ, Findley LC, Krasnewich DM, Mamounas LA, Manolio TA, Mulvihill JJ, LaMoure GL, Goldrich MP, Urv TK, Doss AL, Acosta MT, Bonnenmann C, D’Souza P, Draper DD, Ferreira C, Godfrey RA, Groden CA, Macnamara EF, Maduro VV, Markello TC, Nath A, Novacic D, Pusey BN, Toro C, Wahl CE, Baker E, Burke EA, Adams DR, Gahl WA, Malicdan MCV, Tifft CJ, Wolfe LA, Yang J, Power B, Gochuico B, Huryn L, Latham L, Davis J, Mosbrook-Davis D, Rossignol F, Solomon B, MacDowall J, Thurm A, Zein W, Yousef M, Adam M, Amendola L, Bamshad M, Beck A, Bennett J, Berg-Rood B, Blue E, Boyd B, Byers P, Chanprasert S, Cunningham M, Dipple K, Doherty D, Earl D, Glass I, Golden-Grant K, Hahn S, Hing A, Hisama FM, Horike-Pyne M, Jarvik GP, Jarvik J, Jayadev S, Lam C, Maravilla K, Mefford H, Merritt JL, Mirzaa G, Nickerson D, Raskind W, Rosenwasser N, Scott CR, Sun A, Sybert V, Wallace S, Wener M, Wenger T, Ashley EA, Bejerano G, Bernstein JA, Bonner D, Coakley TR, Fernandez L, Fisher PG, Fresard L, Hom J, Huang Y, Kohler JN, Kravets E, Majcherska MM, Martin BA, Marwaha S, McCormack CE, Raja AN, Reuter CM, Ruzhnikov M, Sampson JB, Smith KS, Sutton S, Tabor HK, Tucker BM, Wheeler MT, Zastrow DB, Zhao C, Byrd WE, Crouse AB, Might M, Nakano-Okuno M, Whitlock J, Brown G, Butte MJ, Dell’Angelica EC, Dorrani N, Douine ED, Fogel BL, Gutierrez I, Huang A, Krakow D, Lee H, Loo SK, Mak BC, Martin MG, Martínez-Agosto JA, McGee E, Nelson SF, Nieves-Rodriguez S, Palmer CGS, Papp JC, Parker NH, Renteria G, Signer RH, Sinsheimer JS, Wan J, Wang LK, Perry KW, Woods JD, Alvey J, Andrews A, Bale J, Bohnsack J, Botto L, Carey J, Pace L, Longo N, Marth G, Moretti P, Quinlan A, Velinder M, Viskochi D, Bayrak-Toydemir P, Mao R, Westerfield M, Bican A, Brokamp E, Duncan L, Hamid R, Kennedy J, Kozuira M, Newman JH, PhillipsIII JA, Rives L, Robertson AK, Solem E, Cogan JD, Cole FS, Hayes N, Kiley D, Sisco K, Wambach J, Wegner D, Baldridge D, Pak S, Schedl T, Shin J, Solnica-Krezel L, Sadjadi R, Elpeleg O, Lee CH, Bellen HJ, Edvardson S, Eichler F, Dunn TM. SPTSSA variants alter sphingolipid synthesis and cause a complex hereditary spastic paraplegia. Brain 2023; 146:1420-1435. [PMID: 36718090 PMCID: PMC10319774 DOI: 10.1093/brain/awac460] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 11/03/2022] [Accepted: 11/19/2022] [Indexed: 02/01/2023] Open
Abstract
Sphingolipids are a diverse family of lipids with critical structural and signalling functions in the mammalian nervous system, where they are abundant in myelin membranes. Serine palmitoyltransferase, the enzyme that catalyses the rate-limiting reaction of sphingolipid synthesis, is composed of multiple subunits including an activating subunit, SPTSSA. Sphingolipids are both essential and cytotoxic and their synthesis must therefore be tightly regulated. Key to the homeostatic regulation are the ORMDL proteins that are bound to serine palmitoyltransferase and mediate feedback inhibition of enzymatic activity when sphingolipid levels become excessive. Exome sequencing identified potential disease-causing variants in SPTSSA in three children presenting with a complex form of hereditary spastic paraplegia. The effect of these variants on the catalytic activity and homeostatic regulation of serine palmitoyltransferase was investigated in human embryonic kidney cells, patient fibroblasts and Drosophila. Our results showed that two different pathogenic variants in SPTSSA caused a hereditary spastic paraplegia resulting in progressive motor disturbance with variable sensorineural hearing loss and language/cognitive dysfunction in three individuals. The variants in SPTSSA impaired the negative regulation of serine palmitoyltransferase by ORMDLs leading to excessive sphingolipid synthesis based on biochemical studies and in vivo studies in Drosophila. These findings support the pathogenicity of the SPTSSA variants and point to excessive sphingolipid synthesis due to impaired homeostatic regulation of serine palmitoyltransferase as responsible for defects in early brain development and function.
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Affiliation(s)
- Siddharth Srivastava
- Department of Neurology, Rosamund Stone Zander Translational Neuroscience Center, BostonChildren's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Hagar Mor Shaked
- Department of Genetics, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel
| | - Kenneth Gable
- Department of Biochemistry and Molecular Biology, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
| | - Sita D Gupta
- Department of Biochemistry and Molecular Biology, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
| | - Xueyang Pan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.,Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA
| | - Niranjanakumari Somashekarappa
- Department of Biochemistry and Molecular Biology, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
| | - Gongshe Han
- Department of Biochemistry and Molecular Biology, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
| | - Payam Mohassel
- Neuromuscular and Neurogenetic Disorders of Childhood Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA
| | - Marc Gotkine
- Department of Genetics, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel
| | | | - Paula Goldenberg
- Department of Pediatrics, Section on Medical Genetics, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Queenie K G Tan
- Department of Pediatrics, Division of Medical Genetics, Duke University School of Medicine, Durham, NC 27710, USA
| | - Yi Gong
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.,Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Benjamin Kleinstiver
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.,Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA.,Department of Pathology, Harvard Medical School, Boston, MA 02115, USA
| | - Brian Wishart
- Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Heidi Cope
- Department of Pediatrics, Division of Medical Genetics, Duke University School of Medicine, Durham, NC 27710, USA
| | - Claudia Brito Pires
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.,Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Hannah Stutzman
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.,Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Rebecca C Spillmann
- Department of Pediatrics, Division of Medical Genetics, Duke University School of Medicine, Durham, NC 27710, USA
| | | | - Reza Sadjadi
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Orly Elpeleg
- Department of Genetics, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel
| | - Chia-Hsueh Lee
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Hugo J Bellen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.,Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA
| | - Simon Edvardson
- Pediatric Neurology Unit, Hadassah University Hospital, Mount Scopus, Jerusalem 91240, Israel
| | - Florian Eichler
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.,Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Teresa M Dunn
- Department of Biochemistry and Molecular Biology, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
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- Department of Neurology, Massachusetts General Hospital, Harvard Medical School , Boston, MA 02114 , USA
| | - Orly Elpeleg
- Department of Genetics, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem , Jerusalem 91120 , Israel
| | - Chia-Hsueh Lee
- Department of Structural Biology, St. Jude Children’s Research Hospital , Memphis, TN 38105 , USA
| | - Hugo J Bellen
- Department of Molecular and Human Genetics, Baylor College of Medicine , Houston, TX 77030 , USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital , Houston, TX 77030 , USA
| | - Simon Edvardson
- Pediatric Neurology Unit, Hadassah University Hospital, Mount Scopus , Jerusalem 91240 , Israel
| | - Florian Eichler
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School , Boston, MA 02114 , USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School , Boston, MA 02114 , USA
| | - Teresa M Dunn
- Department of Biochemistry and Molecular Biology, Uniformed Services University of the Health Sciences , Bethesda, MD 20814 , USA
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11
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Affiliation(s)
- Andrew L Beam
- From the Department of Epidemiology, Harvard T.H. Chan School of Public Health (A.L.B.), and the Department of Biomedical Informatics, Harvard Medical School (I.S.K., A.K.M.) - both in Boston; and the School of Computing, National University of Singapore, Singapore (T.-Y.L.)
| | - Jeffrey M Drazen
- From the Department of Epidemiology, Harvard T.H. Chan School of Public Health (A.L.B.), and the Department of Biomedical Informatics, Harvard Medical School (I.S.K., A.K.M.) - both in Boston; and the School of Computing, National University of Singapore, Singapore (T.-Y.L.)
| | - Isaac S Kohane
- From the Department of Epidemiology, Harvard T.H. Chan School of Public Health (A.L.B.), and the Department of Biomedical Informatics, Harvard Medical School (I.S.K., A.K.M.) - both in Boston; and the School of Computing, National University of Singapore, Singapore (T.-Y.L.)
| | - Tze-Yun Leong
- From the Department of Epidemiology, Harvard T.H. Chan School of Public Health (A.L.B.), and the Department of Biomedical Informatics, Harvard Medical School (I.S.K., A.K.M.) - both in Boston; and the School of Computing, National University of Singapore, Singapore (T.-Y.L.)
| | - Arjun K Manrai
- From the Department of Epidemiology, Harvard T.H. Chan School of Public Health (A.L.B.), and the Department of Biomedical Informatics, Harvard Medical School (I.S.K., A.K.M.) - both in Boston; and the School of Computing, National University of Singapore, Singapore (T.-Y.L.)
| | - Eric J Rubin
- From the Department of Epidemiology, Harvard T.H. Chan School of Public Health (A.L.B.), and the Department of Biomedical Informatics, Harvard Medical School (I.S.K., A.K.M.) - both in Boston; and the School of Computing, National University of Singapore, Singapore (T.-Y.L.)
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12
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Sheu YH, Sun J, Lee H, Castro VM, Barak-Corren Y, Song E, Madsen EM, Gordon WJ, Kohane IS, Churchill SE, Reis BY, Cai T, Smoller JW. An efficient landmark model for prediction of suicide attempts in multiple clinical settings. Psychiatry Res 2023; 323:115175. [PMID: 37003169 DOI: 10.1016/j.psychres.2023.115175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 03/16/2023] [Accepted: 03/18/2023] [Indexed: 04/03/2023]
Abstract
Growing evidence has shown that applying machine learning models to large clinical data sources may exceed clinician performance in suicide risk stratification. However, many existing prediction models either suffer from "temporal bias" (a bias that stems from using case-control sampling) or require training on all available patient visit data. Here, we adopt a "landmark model" framework that aligns with clinical practice for prediction of suicide-related behaviors (SRBs) using a large electronic health record database. Using the landmark approach, we developed models for SRB prediction (regularized Cox regression and random survival forest) that establish a time-point (e.g., clinical visit) from which predictions are made over user-specified prediction windows using historical information up to that point. We applied this approach to cohorts from three clinical settings: general outpatient, psychiatric emergency department, and psychiatric inpatients, for varying prediction windows and lengths of historical data. Models achieved high discriminative performance (area under the Receiver Operating Characteristic curve 0.74-0.93 for the Cox model) across different prediction windows and settings, even with relatively short periods of historical data. In short, we developed accurate, dynamic SRB risk prediction models with the landmark approach that reduce bias and enhance the reliability and portability of suicide risk prediction models.
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Affiliation(s)
- Yi-Han Sheu
- Center for Precision Psychiatry, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA
| | - Jiehuan Sun
- Department of Epidemiology and Biostatistics, University of Illinois Chicago, 1603W. Taylor St., Chicago, IL 60612, USA
| | - Hyunjoon Lee
- Center for Precision Psychiatry, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA
| | - Victor M Castro
- Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA; Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Yuval Barak-Corren
- Department of Pediatrics, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA; Schneider Children's Medical Center of Israel, 14 Kaplan Street, Petaẖ Tiqwa, Central, Israel
| | - Eugene Song
- Center for Precision Psychiatry, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA
| | - Emily M Madsen
- Center for Precision Psychiatry, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA
| | - William J Gordon
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA; Department of Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA; Department of Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA
| | - Susanne E Churchill
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA; Department of Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA
| | - Ben Y Reis
- Department of Pediatrics, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA; Translational Data Science Center for a Learning Health System, Harvard University, 677 Huntington Avenue, Boston, MA, USA
| | - Jordan W Smoller
- Center for Precision Psychiatry, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA.
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13
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Morimoto M, Bhambhani V, Gazzaz N, Davids M, Sathiyaseelan P, Macnamara EF, Lange J, Lehman A, Zerfas PM, Murphy JL, Acosta MT, Wang C, Alderman E, Reichert S, Thurm A, Adams DR, Introne WJ, Gorski SM, Boerkoel CF, Gahl WA, Tifft CJ, Malicdan MCV, Baldridge D, Bale J, Bamshad M, Barbouth D, Bayrak-Toydemir P, Beck A, Beggs AH, Behrens E, Bejerano G, Bellen HJ, Bennett J, Berg-Rood B, Bernstein JA, Berry GT, Bican A, Bivona S, Blue E, Bohnsack J, Bonner D, Botto L, Boyd B, Briere LC, Brokamp E, Brown G, Burke EA, Burrage LC, Butte MJ, Byers P, Byrd WE, Carey J, Carrasquillo O, Cassini T, Chang TCP, Chanprasert S, Chao HT, Clark GD, Coakley TR, Cobban LA, Cogan JD, Coggins M, Cole FS, Colley HA, Cooper CM, Cope H, Craigen WJ, Crouse AB, Cunningham M, D’Souza P, Dai H, Dasari S, Davis J, Dayal JG, Dell’Angelica EC, Dipple K, Doherty D, Dorrani N, Doss AL, Douine ED, Duncan L, Earl D, Eckstein DJ, Emrick LT, Eng CM, Esteves C, Falk M, Fieg EL, Fisher PG, Fogel BL, Forghani I, Glass I, Gochuico B, Goddard PC, Godfrey RA, Golden-Grant K, Grajewski A, Gutierrez I, Hadley D, Hahn S, Halley MC, Hamid R, Hassey K, Hayes N, High F, Hing A, Hisama FM, Holm IA, Hom J, Horike-Pyne M, Huang A, Hutchison S, Introne WJ, Isasi R, Izumi K, Jamal F, Jarvik GP, Jarvik J, Jayadev S, Jean-Marie O, Jobanputra V, Karaviti L, Kennedy J, Ketkar S, Kiley D, Kilich G, Kobren SN, Kohane IS, Kohler JN, Korrick S, Kozuira M, Krakow D, Krasnewich DM, Kravets E, Lalani SR, Lam B, Lam C, Lanpher BC, Lanza IR, LeBlanc K, Lee BH, Levitt R, Lewis RA, Liu P, Liu XZ, Longo N, Loo SK, Loscalzo J, Maas RL, MacRae CA, Maduro VV, Mahoney R, Mak BC, Mamounas LA, Manolio TA, Mao R, Maravilla K, Marom R, Marth G, Martin BA, Martin MG, Martínez-Agosto JA, Marwaha S, McCauley J, McConkie-Rosell A, McCray AT, McGee E, Mefford H, Merritt JL, Might M, Mirzaa G, Morava E, Moretti P, Nakano-Okuno M, Nelson SF, Newman JH, Nicholas SK, Nickerson D, Nieves-Rodriguez S, Novacic D, Oglesbee D, Orengo JP, Pace L, Pak S, Pallais JC, Palmer CGS, Papp JC, Parker NH, Phillips JA, Posey JE, Potocki L, Pusey Swerdzewski BN, Quinlan A, Rao DA, Raper A, Raskind W, Renteria G, Reuter CM, Rives L, Robertson AK, Rodan LH, Rosenfeld JA, Rosenwasser N, Rossignol F, Ruzhnikov M, Sacco R, Sampson JB, Saporta M, Schaechter J, Schedl T, Schoch K, Scott DA, Scott CR, Shashi V, Shin J, Silverman EK, Sinsheimer JS, Sisco K, Smith EC, Smith KS, Solem E, Solnica-Krezel L, Solomon B, Spillmann RC, Stoler JM, Sullivan K, Sullivan JA, Sun A, Sutton S, Sweetser DA, Sybert V, Tabor HK, Tan QKG, Tan ALM, Tekin M, Telischi F, Thorson W, Toro C, Tran AA, Ungar RA, Urv TK, Vanderver A, Velinder M, Viskochil D, Vogel TP, Wahl CE, Walker M, Wallace S, Walley NM, Wambach J, Wan J, Wang LK, Wangler MF, Ward PA, Wegner D, Weisz Hubshman M, Wener M, Wenger T, Wesseling Perry K, Westerfield M, Wheeler MT, Whitlock J, Wolfe LA, Worley K, Xiao C, Yamamoto S, Yang J, Zhang Z, Zuchner S, Reichert S, Thurm A, Adams DR, Introne WJ, Gorski SM, Boerkoel CF, Gahl WA, Tifft CJ, Malicdan MCV. Bi-allelic ATG4D variants are associated with a neurodevelopmental disorder characterized by speech and motor impairment. NPJ Genom Med 2023; 8:4. [PMID: 36765070 PMCID: PMC9918471 DOI: 10.1038/s41525-022-00343-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 12/06/2022] [Indexed: 02/12/2023] Open
Abstract
Autophagy regulates the degradation of damaged organelles and protein aggregates, and is critical for neuronal development, homeostasis, and maintenance, yet few neurodevelopmental disorders have been associated with pathogenic variants in genes encoding autophagy-related proteins. We report three individuals from two unrelated families with a neurodevelopmental disorder characterized by speech and motor impairment, and similar facial characteristics. Rare, conserved, bi-allelic variants were identified in ATG4D, encoding one of four ATG4 cysteine proteases important for autophagosome biogenesis, a hallmark of autophagy. Autophagosome biogenesis and induction of autophagy were intact in cells from affected individuals. However, studies evaluating the predominant substrate of ATG4D, GABARAPL1, demonstrated that three of the four ATG4D patient variants functionally impair ATG4D activity. GABARAPL1 is cleaved or "primed" by ATG4D and an in vitro GABARAPL1 priming assay revealed decreased priming activity for three of the four ATG4D variants. Furthermore, a rescue experiment performed in an ATG4 tetra knockout cell line, in which all four ATG4 isoforms were knocked out by gene editing, showed decreased GABARAPL1 priming activity for the two ATG4D missense variants located in the cysteine protease domain required for priming, suggesting that these variants impair the function of ATG4D. The clinical, bioinformatic, and functional data suggest that bi-allelic loss-of-function variants in ATG4D contribute to the pathogenesis of this syndromic neurodevelopmental disorder.
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Affiliation(s)
- Marie Morimoto
- grid.94365.3d0000 0001 2297 5165National Institutes of Health Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, MD 20892 USA
| | - Vikas Bhambhani
- grid.418506.e0000 0004 0629 5022Department of Medical Genetics, Children’s Hospitals and Clinics of Minnesota, Minneapolis, MN 55404 USA
| | - Nour Gazzaz
- grid.17091.3e0000 0001 2288 9830Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1 Canada ,grid.414137.40000 0001 0684 7788Provincial Medical Genetics Program, British Columbia Women’s and Children’s Hospital, Vancouver, BC V6H 3N1 Canada ,grid.412125.10000 0001 0619 1117Department of Pediatrics, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mariska Davids
- grid.94365.3d0000 0001 2297 5165National Institutes of Health Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, MD 20892 USA
| | - Paalini Sathiyaseelan
- grid.434706.20000 0004 0410 5424Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 1L3 Canada ,grid.61971.380000 0004 1936 7494Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6 Canada
| | - Ellen F. Macnamara
- grid.94365.3d0000 0001 2297 5165National Institutes of Health Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, MD 20892 USA
| | | | - Anna Lehman
- grid.17091.3e0000 0001 2288 9830Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1 Canada
| | - Patricia M. Zerfas
- grid.94365.3d0000 0001 2297 5165Diagnostic and Research Services Branch, Office of Research Services, National Institutes of Health, Bethesda, MD 20892 USA
| | - Jennifer L. Murphy
- grid.94365.3d0000 0001 2297 5165National Institutes of Health Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, MD 20892 USA
| | - Maria T. Acosta
- grid.94365.3d0000 0001 2297 5165National Institutes of Health Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, MD 20892 USA
| | - Camille Wang
- grid.94365.3d0000 0001 2297 5165National Institutes of Health Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, MD 20892 USA
| | - Emily Alderman
- grid.17091.3e0000 0001 2288 9830Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1 Canada ,grid.414137.40000 0001 0684 7788Provincial Medical Genetics Program, British Columbia Women’s and Children’s Hospital, Vancouver, BC V6H 3N1 Canada
| | | | - Sara Reichert
- grid.418506.e0000 0004 0629 5022Department of Medical Genetics, Children’s Hospitals and Clinics of Minnesota, Minneapolis, MN 55404 USA
| | - Audrey Thurm
- grid.94365.3d0000 0001 2297 5165Neurodevelopmental and Behavioral Phenotyping Service, Office of the Clinical Director, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892 USA
| | - David R. Adams
- grid.94365.3d0000 0001 2297 5165National Institutes of Health Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, MD 20892 USA ,grid.94365.3d0000 0001 2297 5165Office of the Clinical Director, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - Wendy J. Introne
- grid.94365.3d0000 0001 2297 5165National Institutes of Health Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, MD 20892 USA ,grid.94365.3d0000 0001 2297 5165Office of the Clinical Director, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA ,grid.94365.3d0000 0001 2297 5165Human Biochemical Genetics Section, Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - Sharon M. Gorski
- grid.17091.3e0000 0001 2288 9830Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1 Canada ,grid.434706.20000 0004 0410 5424Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 1L3 Canada ,grid.61971.380000 0004 1936 7494Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6 Canada
| | - Cornelius F. Boerkoel
- grid.17091.3e0000 0001 2288 9830Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1 Canada ,grid.414137.40000 0001 0684 7788Provincial Medical Genetics Program, British Columbia Women’s and Children’s Hospital, Vancouver, BC V6H 3N1 Canada
| | - William A. Gahl
- grid.94365.3d0000 0001 2297 5165National Institutes of Health Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, MD 20892 USA ,grid.94365.3d0000 0001 2297 5165Human Biochemical Genetics Section, Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - Cynthia J. Tifft
- grid.94365.3d0000 0001 2297 5165National Institutes of Health Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, MD 20892 USA ,grid.94365.3d0000 0001 2297 5165Office of the Clinical Director, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - May Christine V. Malicdan
- grid.94365.3d0000 0001 2297 5165National Institutes of Health Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, MD 20892 USA ,grid.94365.3d0000 0001 2297 5165Human Biochemical Genetics Section, Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
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14
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Diao JA, Wu GJ, Wang JK, Kohane IS, Taylor HA, Tighiouart H, Levey AS, Inker LA, Powe NR, Manrai AK. National Projections for Clinical Implications of Race-Free Creatinine-Based GFR Estimating Equations. J Am Soc Nephrol 2023; 34:309-321. [PMID: 36368777 PMCID: PMC10103103 DOI: 10.1681/asn.2022070818] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 09/28/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The National Kidney Foundation and American Society of Nephrology Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease recently recommended a new race-free creatinine-based equation for eGFR. The effect on recommended clinical care across race and ethnicity groups is unknown. METHODS We analyzed nationally representative cross-sectional questionnaires and medical examinations from 44,360 participants collected between 2001 and 2018 by the National Health and Nutrition Examination Survey. We quantified the number and proportion of Black, White, Hispanic, and Asian/Other adults with guideline-recommended changes in care. RESULTS The new equation, if applied nationally, could assign new CKD diagnoses to 434,000 (95% confidence interval [CI], 350,000 to 517,000) Black adults, reclassify 584,000 (95% CI, 508,000 to 667,000) to more advanced stages of CKD, restrict kidney donation eligibility for 246,000 (95% CI, 189,000 to 303,000), expand nephrologist referrals for 41,800 (95% CI, 19,800 to 63,800), and reduce medication dosing for 222,000 (95% CI, 169,000 to 275,000). Among non-Black adults, these changes may undo CKD diagnoses for 5.51 million (95% CI, 4.86 million to 6.16 million), reclassify 4.59 million (95% CI, 4.28 million to 4.92 million) to less advanced stages of CKD, expand kidney donation eligibility for 3.96 million (95% CI, 3.46 million to 4.46 million), reverse nephrologist referral for 75,800 (95% CI, 35,400 to 116,000), and reverse medication dose reductions for 1.47 million (95% CI, 1.22 million to 1.73 million). The racial and ethnic mix of the populations used to develop eGFR equations has a substantial effect on potential care changes. CONCLUSION The newly recommended 2021 CKD-EPI creatinine-based eGFR equation may result in substantial changes to recommended care for US patients of all racial and ethnic groups.
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Affiliation(s)
- James A. Diao
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
| | - Gloria J. Wu
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
| | - Jason K. Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
| | - Isaac S. Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Herman A. Taylor
- Cardiovascular Research Institute, Morehouse Medical School, Atlanta, Georgia
| | - Hocine Tighiouart
- Biostatistics Research Center, Tufts Clinical and Translational Science Institute, Boston, Massachusetts
| | - Andrew S. Levey
- Division of Nephrology, Tufts Medical Center, Boston, Massachusetts
| | - Lesley A. Inker
- Division of Nephrology, Tufts Medical Center, Boston, Massachusetts
| | - Neil R. Powe
- Department of Medicine, University of California San Francisco and the Priscilla Chan and Mark Zuckerberg San Francisco General Hospital, San Francisco, California
| | - Arjun K. Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
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15
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Miller IM, Yashar BM, Macnamara EF, Adams DR, Agrawal PB, Alvey J, Amendola L, Andrews A, Ashley EA, Azamian MS, Bacino CA, Bademci G, Baker E, Balasubramanyam A, Baldridge D, Bale J, Bamshad M, Barbouth D, Bayrak-Toydemir P, Beck A, Beggs AH, Behrens E, Bejerano G, Bellen HJ, Bennett J, Berg-Rood B, Bernstein JA, Berry GT, Bican A, Bivona S, Blue E, Bohnsack J, Bonnenmann C, Bonner D, Botto L, Boyd B, Briere LC, Brokamp E, Brown G, Burke EA, Burrage LC, Butte MJ, Byers P, Byrd WE, Carey J, Carrasquillo O, Chang TCP, Chanprasert S, Chao HT, Clark GD, Coakley TR, Cobban LA, Cogan JD, Coggins M, Cole FS, Colley HA, Cooper CM, Cope H, Craigen WJ, Crouse AB, Cunningham M, D’Souza P, Dai H, Dasari S, Davis J, Dayal JG, Dell’Angelica EC, Dipple K, Doherty D, Dorrani N, Doss AL, Douine ED, Draper DD, Duncan L, Earl D, Eckstein DJ, Emrick LT, Eng CM, Esteves C, Falk M, Fernandez L, Ferreira C, Fieg EL, Findley LC, Fisher PG, Fogel BL, Forghani I, Gahl WA, Glass I, Gochuico B, Godfrey RA, Golden-Grant K, Goldrich MP, Goldstein DB, Grajewski A, Groden CA, Gutierrez I, Hahn S, Hamid R, Hassey K, Hayes N, High F, Hing A, Hisama FM, Holm IA, Hom J, Horike-Pyne M, Huang Y, Huang A, Huryn L, Isasi R, Izumi K, Jamal F, Jarvik GP, Jarvik J, Jayadev S, Karaviti L, Kennedy J, Ketkar S, Kiley D, Kilich G, Kobren SN, Kohane IS, Kohler JN, Korrick S, Kozuira M, Krakow D, Krasnewich DM, Kravets E, Krier JB, Lalani SR, Lam B, Lam C, LaMoure GL, Lanpher BC, Lanza IR, Latham L, LeBlanc K, Lee BH, Lee H, Levitt R, Lewis RA, Lincoln SA, Liu P, Liu XZ, Longo N, Loo SK, Loscalzo J, Maas RL, MacDowall J, Macnamara EF, MacRae CA, Maduro VV, Mahoney R, Mak BC, Malicdan MCV, Mamounas LA, Manolio TA, Mao R, Maravilla K, Markello TC, Marom R, Marth G, Martin BA, Martin MG, Martfnez-Agosto JA, Marwaha S, McCauley J, McConkie-Rosell A, McCray AT, McGee E, Mefford H, Merritt JL, Might M, Mirzaa G, Morava E, Moretti PM, Moretti P, Mosbrook-Davis D, Mulvihill JJ, Nakano-Okuno M, Nath A, Nelson SF, Newman JH, Nicholas SK, Nickerson D, Nieves-Rodriguez S, Novacic D, Oglesbee D, Orengo JP, Pace L, Pak S, Pallais JC, Palmer CGS, Papp JC, Parker NH, Phillips JA, Posey JE, Potocki L, Power B, Pusey BN, Quinlan A, Raja AN, Rao DA, Raper A, Raskind W, Renteria G, Reuter CM, Rives L, Robertson AK, Rodan LH, Rosenfeld JA, Rosenwasser N, Rossignol F, Ruzhnikov M, Sacco R, Sampson JB, Saporta M, Schaechter J, Schedl T, Schoch K, Scott DA, Scott CR, Shashi V, Shin J, Signer RH, Silverman EK, Sinsheimer JS, Sisco K, Smith EC, Smith KS, Solem E, Solnica-Krezel L, Solomon B, Spillmann RC, Stoler JM, Sullivan K, Sullivan JA, Sun A, Sutton S, Sweetser DA, Sybert V, Tabor HK, Tan QKG, Tan ALM, Tekin M, Telischi F, Thorson W, Thurm A, Tifft CJ, Toro C, Tran AA, Tucker BM, Urv TK, Vanderver A, Velinder M, Viskochil D, Vogel TP, Wahl CE, Walker M, Wallace S, Walley NM, Walsh CA, Wambach J, Wan J, Wang LK, Wangler MF, Ward PA, Wegner D, Hubshman MW, Wener M, Wenger T, Perry KW, Westerfield M, Wheeler MT, Whitlock J, Wolfe LA, Woods JD, Worley K, Yamamoto S, Yang J, Yousef M, Zastrow DB, Zein W, Zhang Z, Zhao C, Zuchner S, Macnamara EF. Continuing a search for a diagnosis: the impact of adolescence and family dynamics. Orphanet J Rare Dis 2023; 18:6. [PMID: 36624503 PMCID: PMC9830697 DOI: 10.1186/s13023-022-02598-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 12/14/2022] [Indexed: 01/11/2023] Open
Abstract
The "diagnostic odyssey" describes the process those with undiagnosed conditions undergo to identify a diagnosis. Throughout this process, families of children with undiagnosed conditions have multiple opportunities to decide whether to continue or stop their search for a diagnosis and accept the lack of a diagnostic label. Previous studies identified factors motivating a family to begin searching, but there is limited information about the decision-making process in a prolonged search and how the affected child impacts a family's decision. This study aimed to understand how families of children with undiagnosed diseases decide whether to continue to pursue a diagnosis after standard clinical testing has failed. Parents who applied to the Undiagnosed Disease Network (UDN) at the National Institutes of Health (NIH) were recruited to participate in semi-structured interviews. The 2015 Supportive Care Needs model by Pelenstov, which defines critical needs in families with rare/undiagnosed diseases, provided a framework for interview guide development and transcript analysis (Pelentsov et al in Disabil Health J 8(4):475-491, 2015. https://doi.org/10.1016/J.DHJO.2015.03.009 ). A deductive, iterative coding approach was used to identify common unifying themes. Fourteen parents from 13 families were interviewed. The average child's age was 11 years (range 3-18) and an average 63% of their life had been spent searching for a diagnosis. Our analysis found that alignment or misalignment of parent and child needs impact the trajectory of the diagnostic search. When needs and desires align, reevaluation of a decision to pursue a diagnosis is limited. However, when there is conflict between parent and child desires, there is reevaluation, and often a pause, in the search. This tension is exacerbated when children are adolescents and attempting to balance their dependence on parents for medical care with a natural desire for independence. Our results provide novel insights into the roles of adolescents in the diagnostic odyssey. The tension between desired and realistic developmental outcomes for parents and adolescents impacts if, and how, the search for a diagnosis progresses.
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Affiliation(s)
- Ilana M. Miller
- grid.239560.b0000 0004 0482 1586Children’s National Medical Center, Rare Disease Institute, 7125 13th Place NW, DC 20012 Washington, USA ,grid.214458.e0000000086837370Department of Human Genetics, University of Michigan, 4909 Buhl Building, Catherine St, Ann Arbor, MI 48109 USA
| | - Beverly M. Yashar
- grid.214458.e0000000086837370Department of Human Genetics, University of Michigan, 4909 Buhl Building, Catherine St, Ann Arbor, MI 48109 USA
| | | | - Ellen F. Macnamara
- grid.453125.40000 0004 0533 8641National Institutes of Health Undiagnosed Diseases Program, Common Fund, Office of the Director, NIH, Bethesda, MD USA
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16
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Moal B, Orieux A, Ferté T, Neuraz A, Brat GA, Avillach P, Bonzel CL, Cai T, Cho K, Cossin S, Griffier R, Hanauer DA, Haverkamp C, Ho YL, Hong C, Hutch MR, Klann JG, Le TT, Loh NHW, Luo Y, Makoudjou A, Morris M, Mowery DL, Olson KL, Patel LP, Samayamuthu MJ, Sanz Vidorreta FJ, Schriver ER, Schubert P, Verdy G, Visweswaran S, Wang X, Weber GM, Xia Z, Yuan W, Zhang HG, Zöller D, Kohane IS, Boyer A, Jouhet V. Acute respiratory distress syndrome after SARS-CoV-2 infection on young adult population: International observational federated study based on electronic health records through the 4CE consortium. PLoS One 2023; 18:e0266985. [PMID: 36598895 DOI: 10.1371/journal.pone.0266985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 11/09/2022] [Indexed: 01/05/2023] Open
Abstract
PURPOSE In young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population. METHODS A retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS. RESULTS Among the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%). CONCLUSION Trough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor.
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Affiliation(s)
- Bertrand Moal
- IAM Unit, Bordeaux University Hospital, Bordeaux, France
| | - Arthur Orieux
- Medical Intensive Care Unit, Bordeaux University Hospital, Bordeaux, France
| | - Thomas Ferté
- Inserm Bordeaux Population Health Research Center UMR 1219, Inria BSO, Team SISTM, University of Bordeaux, Bordeaux, France
| | - Antoine Neuraz
- Department of Biomedical Informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris (APHP), University of Paris, Paris, France
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Kelly Cho
- Population Health and Data Science, MAVERIC, VA Boston Healthcare System, Boston, Massachusetts, United States of America
| | - Sébastien Cossin
- INSERM Bordeaux Population Health ERIAS TEAM, Bordeaux University Hospital / ERIAS - Inserm U1219 BPH, Bordeaux, France
| | - Romain Griffier
- Institute of Digitalization in Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - David A Hanauer
- IAM Unit, INSERM Bordeaux Population Health ERIAS TEAM, Bordeaux University Hospital / ERIAS - Inserm U1219 BPH, Bordeaux, France
| | - Christian Haverkamp
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Yuk-Lam Ho
- Institute of Digitalization in Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Meghan R Hutch
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America
| | - Jeffrey G Klann
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Trang T Le
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ne Hooi Will Loh
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Yuan Luo
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Adeline Makoudjou
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Michele Morris
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Danielle L Mowery
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Karen L Olson
- Department of Anaesthesia, National University Health System, Singapore, Singapore
| | - Lav P Patel
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Malarkodi J Samayamuthu
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Fernando J Sanz Vidorreta
- Computational Health Informatics Program, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Emily R Schriver
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, Kansas, United States of America
| | - Petra Schubert
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America
| | | | - Shyam Visweswaran
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Zongqi Xia
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States of America
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Daniela Zöller
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Alexandre Boyer
- Medical Intensive Care Unit, Bordeaux University Hospital, Bordeaux, France
| | - Vianney Jouhet
- Institute of Digitalization in Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
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17
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Tan BW, Tan BW, Tan AL, Schriver ER, Gutiérrez-Sacristán A, Das P, Yuan W, Hutch MR, García Barrio N, Pedrera Jimenez M, Abu-el-rub N, Morris M, Moal B, Verdy G, Cho K, Ho YL, Patel LP, Dagliati A, Neuraz A, Klann JG, South AM, Visweswaran S, Hanauer DA, Maidlow SE, Liu M, Mowery DL, Batugo A, Makoudjou A, Tippmann P, Zöller D, Brat GA, Luo Y, Avillach P, Bellazzi R, Chiovato L, Malovini A, Tibollo V, Samayamuthu MJ, Serrano Balazote P, Xia Z, Loh NHW, Chiudinelli L, Bonzel CL, Hong C, Zhang HG, Weber GM, Kohane IS, Cai T, Omenn GS, Holmes JH, Ngiam KY. Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study. EClinicalMedicine 2023; 55:101724. [PMID: 36381999 PMCID: PMC9640184 DOI: 10.1016/j.eclinm.2022.101724] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/12/2022] [Accepted: 10/12/2022] [Indexed: 11/09/2022] Open
Abstract
Background While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking. Methods A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021. Findings Advanced age (HR 2.77, 95%CI 2.53-3.04, p < 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p < 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p < 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p < 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p < 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p < 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p < 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p < 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p < 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI. Interpretation COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery. Funding Authors are supported by various funders, with full details stated in the acknowledgement section.
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Affiliation(s)
- Byorn W.L. Tan
- Department of Medicine, National University Hospital, 1E Kent Ridge Road, NUHS Tower Block Level 10, Singapore 119228
| | - Bryce W.Q. Tan
- Department of Medicine, National University Hospital, 1E Kent Ridge Road, NUHS Tower Block Level 10, Singapore 119228
| | - Amelia L.M. Tan
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Emily R. Schriver
- Data Analytics Center, University of Pennsylvania Health System, 3600 Civic Center Boulevard, Philadelphia, PA 19104, USA
| | - Alba Gutiérrez-Sacristán
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Priyam Das
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Meghan R. Hutch
- Department of Preventive Medicine, Northwestern University, 750 North Lake Shore Drive, Chicago, IL 60611, USA
| | - Noelia García Barrio
- Department of Health Informatics, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n 28041 Madrid, Spain
| | - Miguel Pedrera Jimenez
- Department of Health Informatics, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n 28041 Madrid, Spain
| | - Noor Abu-el-rub
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA 15206, USA
| | - Bertrand Moal
- IAM Unit, Bordeaux University Hospital, Place Amélie Rabat Léon, 33076 Bordeaux, France
| | - Guillaume Verdy
- IAM Unit, Bordeaux University Hospital, Place Amélie Rabat Léon, 33076 Bordeaux, France
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, 2 Avenue De Lafayette, Boston, MA 02130, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, 2 Avenue De Lafayette, Boston, MA 02130, USA
| | - Lav P. Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA
| | - Arianna Dagliati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy, Via Ferrata 5, 27100 Pavia, Italy
| | - Antoine Neuraz
- Department of Biomedical Informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris, University of Paris, 149 Rue de Sèvres, 75015 Paris, France
| | - Jeffrey G. Klann
- Department of Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Andrew M. South
- Department of Pediatrics-Section of Nephrology, Brenner Children's Hospital, Wake Forest School of Medicine, Medical Center Boulevard, Winston Salem, NC 27157, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA 15206, USA
| | - David A. Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA, 100-107 NCRC, 2800 Plymouth Road, Ann Arbor, MI 48109, USA
| | - Sarah E. Maidlow
- Michigan Institute for Clinical and Health Research (MICHR) Informatics, University of Michigan, NCRC Bldg 400, 2800 Plymouth Road, Ann Arbor, MI, United States
| | - Mei Liu
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA
| | - Danielle L. Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, 3700 Hamilton Walk, Richards Hall, A202, Philadelphia, PA 19104, USA
| | - Ashley Batugo
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, 401 Blockley Hall 423 Guardian Drive Philadelphia, PA 19104, USA
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Zinkmattenstraße 6a, DE79108 Freiburg, Germany
| | - Patric Tippmann
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Zinkmattenstraße 6a, DE79108 Freiburg, Germany
| | - Daniela Zöller
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Zinkmattenstraße 6a, DE79108 Freiburg, Germany
| | - Gabriel A. Brat
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, 750 North Lake Shore Drive, Chicago, IL 60611, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy, Via Ferrata 5, 27100 Pavia, Italy
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Via Maugeri 4, 27100 Pavia, Italy
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy., Via Maugeri 4, 27100 Pavia, Italy
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy., Via Maugeri 4, 27100 Pavia, Italy
| | | | - Pablo Serrano Balazote
- Department of Health Informatics, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n 28041 Madrid, Spain
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, 3501 5th Avenue, BST-3 Suite 7014, Pittsburgh, PA 15260, USA
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health System, 5 Lower Kent Ridge Road, Singapore 119074
| | - Lorenzo Chiudinelli
- UOC Ricerca, Innovazione e Brand reputation, ASST Papa Giovanni XXIII, Bergamo, P.zza OMS 1 - 24127 Bergamo, Italy
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
- Department of Biostatistics and Bioinformatics, Duke University, 2424 Erwin Road, Durham, NC, United States
| | - Harrison G. Zhang
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Griffin M. Weber
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Isaac S. Kohane
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Gilbert S. Omenn
- Department of Computational Medicine & Bioinformatics, University of Michigan, 2017B Palmer Commons, 100 Washtenaw, Ann Arbor, MI 48109-2218
| | - John H. Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, 3700 Hamilton Walk, Richards Hall, A202, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, 401 Blockley Hall 423 Guardian Drive Philadelphia, PA 19104, USA
| | - Kee Yuan Ngiam
- Department of Biomedical Informatics, WiSDM, National University Health Systems Singapore, 1E Kent Ridge Road, NUHS Tower Block Level 8, Singapore 119228
- Corresponding author. Department of Biomedical Informatics, WiSDM, National University Health Systems Singapore, 1E Kent Ridge Road, NUHS Tower Block Level 8, Singapore 119228.
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18
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Wang X, Zhang HG, Xiong X, Hong C, Weber GM, Brat GA, Bonzel CL, Luo Y, Duan R, Palmer NP, Hutch MR, Gutiérrez-Sacristán A, Bellazzi R, Chiovato L, Cho K, Dagliati A, Estiri H, García-Barrio N, Griffier R, Hanauer DA, Ho YL, Holmes JH, Keller MS, Klann MEng JG, L'Yi S, Lozano-Zahonero S, Maidlow SE, Makoudjou A, Malovini A, Moal B, Moore JH, Morris M, Mowery DL, Murphy SN, Neuraz A, Yuan Ngiam K, Omenn GS, Patel LP, Pedrera-Jiménez M, Prunotto A, Jebathilagam Samayamuthu M, Sanz Vidorreta FJ, Schriver ER, Schubert P, Serrano-Balazote P, South AM, Tan ALM, Tan BWL, Tibollo V, Tippmann P, Visweswaran S, Xia Z, Yuan W, Zöller D, Kohane IS, Avillach P, Guo Z, Cai T. SurvMaximin: Robust federated approach to transporting survival risk prediction models. J Biomed Inform 2022; 134:104176. [PMID: 36007785 PMCID: PMC9707637 DOI: 10.1016/j.jbi.2022.104176] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/18/2022] [Accepted: 08/15/2022] [Indexed: 10/15/2022]
Abstract
OBJECTIVE For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information. MATERIALS AND METHODS For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning. RESULTS Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations. CONCLUSIONS The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.
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Affiliation(s)
- Xuan Wang
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Xin Xiong
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Yuan Luo
- Department of Preventive Medicine Northwestern University, Chicago, IL, USA
| | - Rui Duan
- Department of Biostatistics, Harvard University, Boston, MA, USA
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Meghan R Hutch
- Department of Preventive Medicine Northwestern University, Chicago, IL, USA
| | | | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Kelly Cho
- Population Health and Data Science, VA Boston Healthcare System, Boston, MA, USA; Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Arianna Dagliati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Romain Griffier
- IAM unit, Bordeaux University Hospital, Bordeaux, France; INSERM Bordeaux Population Health ERIAS TEAM, ERIAS - Inserm U1219 BPH, Bordeaux, France
| | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mark S Keller
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sara Lozano-Zahonero
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Sarah E Maidlow
- Michigan Institute for Clinical and Health Research (MICHR) Informatics, University of Michigan, Ann Arbor, MI, USA
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Bertrand Moal
- IAM unit, Bordeaux University Hospital, Bordeaux, France
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Antoine Neuraz
- Department of biomedical informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris (APHP), University of Paris, Paris, France
| | - Kee Yuan Ngiam
- Department of Biomedical informatics, WiSDM, National University Health Systems, Singapore
| | - Gilbert S Omenn
- Depts of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, Public Health University of Michigan, Ann Arbor, MI, USA
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University Of Kansas Medical Center
| | | | - Andrea Prunotto
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | | | | | - Emily R Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, PA, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | | | - Andrew M South
- Department of Pediatrics-Section of Nephrology, Brenner Children's, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Byorn W L Tan
- Department of Medicine, National University Hospital, Singapore
| | - Valentina Tibollo
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Patric Tippmann
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Daniela Zöller
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Zijian Guo
- Department of Statistics, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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19
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Brociner E, Yu KH, Kohane IS, Crowley M. Association of Race and Socioeconomic Disadvantage With Missed Telemedicine Visits for Pediatric Patients During the COVID-19 Pandemic. JAMA Pediatr 2022; 176:933-935. [PMID: 35604679 PMCID: PMC9127707 DOI: 10.1001/jamapediatrics.2022.1510] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
This comparative effectiveness study examines whether race and ethnicity and socioeconomic disadvantage are factors associated with missing telemedicine visits during the COVID-19 pandemic among pediatric patients in Massachusetts.
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Affiliation(s)
- Evan Brociner
- Division of Gastroenterology, Hepatology, and Nutrition, Boston Children’s Hospital, Boston, Massachusetts
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Isaac S. Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - McGreggor Crowley
- Division of Gastroenterology, Hepatology, and Nutrition, Boston Children’s Hospital, Boston, Massachusetts
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20
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Valtchinov VI, Murphy SN, Lacson R, Ikonomov N, Zhai BK, Andriole K, Rousseau J, Hanson D, Kohane IS, Khorasani R. Analytics to monitor local impact of the Protecting Access to Medicare Act's imaging clinical decision support requirements. J Am Med Inform Assoc 2022; 29:1870-1878. [PMID: 35932187 PMCID: PMC9552289 DOI: 10.1093/jamia/ocac132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 05/19/2022] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE This study aimed is to: (1) extend the Integrating the Biology and the Bedside (i2b2) data and application models to include medical imaging appropriate use criteria, enabling it to serve as a platform to monitor local impact of the Protecting Access to Medicare Act's (PAMA) imaging clinical decision support (CDS) requirements, and (2) validate the i2b2 extension using data from the Medicare Imaging Demonstration (MID) CDS implementation. MATERIALS AND METHODS This study provided a reference implementation and assessed its validity and reliability using data from the MID, the federal government's predecessor to PAMA's imaging CDS program. The Star Schema was extended to describe the interactions of imaging ordering providers with the CDS. New ontologies were added to enable mapping medical imaging appropriateness data to i2b2 schema. z-Ratio for testing the significance of the difference between 2 independent proportions was utilized. RESULTS The reference implementation used 26 327 orders for imaging examinations which were persisted to the modified i2b2 schema. As an illustration of the analytical capabilities of the Web Client, we report that 331/1192 or 28.1% of imaging orders were deemed appropriate by the CDS system at the end of the intervention period (September 2013), an increase from 162/1223 or 13.2% for the first month of the baseline period, December 2011 (P = .0212), consistent with previous studies. CONCLUSIONS The i2b2 platform can be extended to monitor local impact of PAMA's appropriateness of imaging ordering CDS requirements.
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Affiliation(s)
- Vladimir I Valtchinov
- Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Shawn N Murphy
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,i2b2 tranSMART Foundation, Wakefield, Massachusetts, USA
| | - Ronilda Lacson
- Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Nikolay Ikonomov
- Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | - Bingxue K Zhai
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Katherine Andriole
- Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Justin Rousseau
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, USA
| | - Dick Hanson
- Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,i2b2 tranSMART Foundation, Wakefield, Massachusetts, USA
| | - Ramin Khorasani
- Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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21
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Yuan W, Marwaha JS, Rakowsky ST, Palmer NP, Kohane IS, Rubin DT, Brat GA, Feuerstein JD. Trends in Medical Management of Moderately to Severely Active Ulcerative Colitis: A Nationwide Retrospective Analysis. Inflamm Bowel Dis 2022; 29:695-704. [PMID: 35786768 PMCID: PMC10152283 DOI: 10.1093/ibd/izac134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Indexed: 12/09/2022]
Abstract
BACKGROUND With an increasing number of therapeutic options available for the management of ulcerative colitis (UC), the variability in treatment and prescribing patterns is not well known. While recent guidelines have provided updates on how these therapeutic options should be used, patterns of long-term use of these drugs over the past 2 decades remain unclear. METHODS We analyzed a retrospective, nationwide cohort of more than 1.7 million prescriptions for trends in prescribing behaviors and to evaluate practices suggested in guidelines relating to ordering biologics, step-up therapy, and combination therapy. The primary outcome was 30-day steroid-free remission and secondary outcomes included hospitalization, cost, and additional steroid usage. A pipeline was created to identify cohorts of patients under active UC medical management grouped by prescribing strategies to evaluate comparative outcomes between strategies. Cox proportional hazards and multivariate regression models were utilized to assess postexposure outcomes and adjust for confounders. RESULTS Among 6 major drug categories, we noted major baseline differences in patient characteristics at first exposure corresponding to disease activity. We noted earlier use of biologics in patient trajectories (762 days earlier relative to UC diagnosis, 2018 vs 2008; P < .001) and greater overall use of biologics over time (2.53× more in 2018 vs 2008; P < .00001) . Among biologic-naive patients, adalimumab was associated with slightly lower rates of remission compared with infliximab or vedolizumab (odds ratio, 0.92; P < .005). Comparisons of patients with early biologic initiation to patients who transitioned to biologics from 5-aminosalicylic acid suggest lower steroid consumption for early biologic initiation (-761 mg prednisone; P < .001). Combination thiopurine-biologic therapy was associated with higher odds of remission compared with biologic monotherapy (odds ratio, 1.36; P = .01). CONCLUSIONS As biologic drugs have become increasingly available for UC management, they have increasingly been used at earlier stages of disease management. Large-scale analyses of prescribing behaviors provide evidence supporting early use of biologics compared with step-up therapy and use of thiopurine and biologic combination therapy.
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Affiliation(s)
- William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
| | - Jayson S Marwaha
- Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Shana T Rakowsky
- Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - David T Rubin
- Section of Gastroenterology, Hepatology and Nutrition, University of Chicago Medicine, Chicago, IL, USA
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Joseph D Feuerstein
- Division of Gastroenterology and Center for Inflammatory Bowel Diseases, Beth Israel Deaconess Medical Center, Boston, MA, USA
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22
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Zhang HG, Dagliati A, Shakeri Hossein Abad Z, Xiong X, Bonzel CL, Xia Z, Tan BWQ, Avillach P, Brat GA, Hong C, Morris M, Visweswaran S, Patel LP, Gutiérrez-Sacristán A, Hanauer DA, Holmes JH, Samayamuthu MJ, Bourgeois FT, L'Yi S, Maidlow SE, Moal B, Murphy SN, Strasser ZH, Neuraz A, Ngiam KY, Loh NHW, Omenn GS, Prunotto A, Dalvin LA, Klann JG, Schubert P, Vidorreta FJS, Benoit V, Verdy G, Kavuluru R, Estiri H, Luo Y, Malovini A, Tibollo V, Bellazzi R, Cho K, Ho YL, Tan ALM, Tan BWL, Gehlenborg N, Lozano-Zahonero S, Jouhet V, Chiovato L, Aronow BJ, Toh EMS, Wong WGS, Pizzimenti S, Wagholikar KB, Bucalo M, Cai T, South AM, Kohane IS, Weber GM. International electronic health record-derived post-acute sequelae profiles of COVID-19 patients. NPJ Digit Med 2022; 5:81. [PMID: 35768548 PMCID: PMC9242995 DOI: 10.1038/s41746-022-00623-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 05/19/2022] [Indexed: 11/10/2022] Open
Abstract
The risk profiles of post-acute sequelae of COVID-19 (PASC) have not been well characterized in multi-national settings with appropriate controls. We leveraged electronic health record (EHR) data from 277 international hospitals representing 414,602 patients with COVID-19, 2.3 million control patients without COVID-19 in the inpatient and outpatient settings, and over 221 million diagnosis codes to systematically identify new-onset conditions enriched among patients with COVID-19 during the post-acute period. Compared to inpatient controls, inpatient COVID-19 cases were at significant risk for angina pectoris (RR 1.30, 95% CI 1.09–1.55), heart failure (RR 1.22, 95% CI 1.10–1.35), cognitive dysfunctions (RR 1.18, 95% CI 1.07–1.31), and fatigue (RR 1.18, 95% CI 1.07–1.30). Relative to outpatient controls, outpatient COVID-19 cases were at risk for pulmonary embolism (RR 2.10, 95% CI 1.58–2.76), venous embolism (RR 1.34, 95% CI 1.17–1.54), atrial fibrillation (RR 1.30, 95% CI 1.13–1.50), type 2 diabetes (RR 1.26, 95% CI 1.16–1.36) and vitamin D deficiency (RR 1.19, 95% CI 1.09–1.30). Outpatient COVID-19 cases were also at risk for loss of smell and taste (RR 2.42, 95% CI 1.90–3.06), inflammatory neuropathy (RR 1.66, 95% CI 1.21–2.27), and cognitive dysfunction (RR 1.18, 95% CI 1.04–1.33). The incidence of post-acute cardiovascular and pulmonary conditions decreased across time among inpatient cases while the incidence of cardiovascular, digestive, and metabolic conditions increased among outpatient cases. Our study, based on a federated international network, systematically identified robust conditions associated with PASC compared to control groups, underscoring the multifaceted cardiovascular and neurological phenotype profiles of PASC.
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Affiliation(s)
- Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Arianna Dagliati
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | | | - Xin Xiong
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bryce W Q Tan
- Department of Medicine, National University Hospital, Singapore, Singapore
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University Of Kansas Medical Center, Kansas City, MO, USA
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | | | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sarah E Maidlow
- Michigan Institute for Clinical and Health Research (MICHR) Informatics, University of Michigan, Ann Arbor, MI, USA
| | - Bertrand Moal
- IAM unit, Bordeaux University Hospital, Bordeaux, France
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Antoine Neuraz
- Department of biomedical informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris (APHP), University of Paris, Paris, France
| | - Kee Yuan Ngiam
- Department of Biomedical informatics, WiSDM, National University Health Systems Singapore, Singapore, Singapore
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health Systems Singapore, Singapore, Singapore
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Andrea Prunotto
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Lauren A Dalvin
- Department of Ophthalmology, Mayo Clinic, Rochester, NY, USA
| | - Jeffrey G Klann
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | | | - Vincent Benoit
- IT Department, Innovation & Data, APHP Greater Paris University Hospital, Paris, France
| | | | - Ramakanth Kavuluru
- Division of Biomedical Informatics (Department of Internal Medicine), University of Kentucky, Lexington, KY, USA
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA.,Population Health and Data Science, VA Boston Healthcare System, Boston, MA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Byorn W L Tan
- Department of Medicine, National University Hospital, Singapore, Singapore
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sara Lozano-Zahonero
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Vianney Jouhet
- IAM unit, INSERM Bordeaux Population Health ERIAS TEAM, Bordeaux University Hospital / ERIAS - Inserm, U1219 BPH, Bordeaux, France
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Bruce J Aronow
- Departments of Biomedical Informatics, Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Emma M S Toh
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Wei Gen Scott Wong
- Department of Medicine, National University Health Systems Singapore, Singapore, Singapore
| | - Sara Pizzimenti
- Scientific Direction, IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, Milan, Italy
| | | | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | | | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andrew M South
- Department of Pediatrics-Section of Nephrology, Brenner Children's, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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23
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Weber GM, Hong C, Xia Z, Palmer NP, Avillach P, L'Yi S, Keller MS, Murphy SN, Gutiérrez-Sacristán A, Bonzel CL, Serret-Larmande A, Neuraz A, Omenn GS, Visweswaran S, Klann JG, South AM, Loh NHW, Cannataro M, Beaulieu-Jones BK, Bellazzi R, Agapito G, Alessiani M, Aronow BJ, Bell DS, Benoit V, Bourgeois FT, Chiovato L, Cho K, Dagliati A, DuVall SL, Barrio NG, Hanauer DA, Ho YL, Holmes JH, Issitt RW, Liu M, Luo Y, Lynch KE, Maidlow SE, Malovini A, Mandl KD, Mao C, Matheny ME, Moore JH, Morris JS, Morris M, Mowery DL, Ngiam KY, Patel LP, Pedrera-Jimenez M, Ramoni RB, Schriver ER, Schubert P, Balazote PS, Spiridou A, Tan ALM, Tan BWL, Tibollo V, Torti C, Trecarichi EM, Wang X, Kohane IS, Cai T, Brat GA. International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality. NPJ Digit Med 2022; 5:74. [PMID: 35697747 PMCID: PMC9192605 DOI: 10.1038/s41746-022-00601-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 03/11/2022] [Indexed: 01/08/2023] Open
Abstract
Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.
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Affiliation(s)
- Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
- Department of Biostatistics and Bioinformatics, Duke University, Durham, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, USA
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Mark S Keller
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, USA
| | | | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Arnaud Serret-Larmande
- Department of biomedical informatics, Hôpital Européen Georges Pompidou, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Antoine Neuraz
- Department of biomedical informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris (APHP), University of Paris, Paris, France
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Jeffrey G Klann
- Department of Medicine, Massachusetts General Hospital, Boston, USA
| | - Andrew M South
- Department of Pediatrics-Section of Nephrology, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, USA
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health System, Singapore, Singapore, Singapore
| | - Mario Cannataro
- Department of Medical and Surgical Sciences, Data Analytics Research Center, University Magna Graecia of Catanzaro, Italy, Catanzaro, Italy
| | | | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy, Pavia, Italy
| | - Giuseppe Agapito
- Department of Legal, Economic and Social Sciences, University Magna Graecia of Catanzaro, Italy, Catanzaro, Italy
| | - Mario Alessiani
- Department of Surgery, ASST Pavia, Lombardia Region Health System, Pavia, Italy
| | - Bruce J Aronow
- Departments of Biomedical Informatics, Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, USA
| | - Douglas S Bell
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Vincent Benoit
- IT department, Innovation & Data, APHP Greater Paris University Hospital, Paris, France
| | | | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, USA
| | - Arianna Dagliati
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Italy, Pavia, Italy
| | - Scott L DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, USA
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, USA
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Richard W Issitt
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, UK, London, UK
| | - Molei Liu
- Department of Biostatistics, Harvard School of Public Health, Boston, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, USA
| | - Kristine E Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, USA
| | - Sarah E Maidlow
- Michigan Institute for Clinical and Health Research, University of Michigan, Ann Arbor, USA
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, USA
| | - Chengsheng Mao
- Department of Preventive Medicine, Northwestern University, Chicago, USA
| | - Michael E Matheny
- VA Informatics and Computing Infrastructure, Tennessee Valley Healthcare System Veterans Affairs Medical Center, Nashville, USA
| | - Jason H Moore
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Jeffrey S Morris
- Department of Biostatistics, Epidemiology, and Biostatistics, University of Pennysylvania Perelman School of Medicine, Philadelphia, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Kee Yuan Ngiam
- Department of Biomedical informatics, WiSDM, National University Health Systems Singapore, Singapore, Singapore
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, USA
| | | | - Rachel B Ramoni
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, USA
| | - Emily R Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, USA
| | | | - Anastasia Spiridou
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, UK, London, UK
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Byorn W L Tan
- Department of Medicine, National University Hospital, Singapore, Singapore, Singapore
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Carlo Torti
- Department of Medical and Surgical Sciences, Infectious and Tropical Disease Unit, University Magna Graecia of Catanzaro, Italy, Catanzaro, Italy
| | - Enrico M Trecarichi
- Department of Medical and Surgical Sciences, Infectious and Tropical Disease Unit, University Magna Graecia of Catanzaro, Italy, Catanzaro, Italy
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA.
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA.
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24
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Lu C, Jin D, Palmer N, Fox K, Kohane IS, Smoller JW, Yu KH. Large-scale real-world data analysis identifies comorbidity patterns in schizophrenia. Transl Psychiatry 2022; 12:154. [PMID: 35410453 PMCID: PMC9001711 DOI: 10.1038/s41398-022-01916-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 11/23/2022] Open
Abstract
Schizophrenia affects >3.2 million people in the USA. However, its comorbidity patterns have not been systematically characterized in real-world populations. To address this gap, we conducted an observational study using a cohort of 86 million patients in a nationwide health insurance dataset. We identified participants with schizophrenia and those without schizophrenia matched by age, sex, and the first three digits of zip code. For each phenotype encoded in phecodes, we compared their prevalence in schizophrenia patients and the matched non-schizophrenic participants, and we performed subgroup analyses stratified by age and sex. Results show that anxiety, posttraumatic stress disorder, and substance abuse commonly occur in adolescents and young adults prior to schizophrenia diagnoses. Patients aged 60 and above are at higher risks of developing delirium, alcoholism, dementia, pelvic fracture, and osteomyelitis than their matched controls. Type 2 diabetes, sleep apnea, and eating disorders were more prevalent in women prior to schizophrenia diagnosis, whereas acute renal failure, rhabdomyolysis, and developmental delays were found at higher rates in men. Anxiety and obesity are more commonly seen in patients with schizoaffective disorders compared to patients with other types of schizophrenia. Leveraging a large-scale insurance claims dataset, this study identified less-known comorbidity patterns of schizophrenia and confirmed known ones. These comorbidity profiles can guide clinicians and researchers to take heed of early signs of co-occurring diseases.
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Affiliation(s)
- Chenyue Lu
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Di Jin
- grid.116068.80000 0001 2341 2786Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA USA
| | - Nathan Palmer
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Kathe Fox
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Isaac S. Kohane
- grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Jordan W. Smoller
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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25
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Pain O, Hodgson K, Trubetskoy V, Ripke S, Marshe VS, Adams MJ, Byrne EM, Campos AI, Carrillo-Roa T, Cattaneo A, Als TD, Souery D, Dernovsek MZ, Fabbri C, Hayward C, Henigsberg N, Hauser J, Kennedy JL, Lenze EJ, Lewis G, Müller DJ, Martin NG, Mulsant BH, Mors O, Perroud N, Porteous DJ, Rentería ME, Reynolds CF, Rietschel M, Uher R, Wigmore EM, Maier W, Wray NR, Aitchison KJ, Arolt V, Baune BT, Biernacka JM, Bondolfi G, Domschke K, Kato M, Li QS, Liu YL, Serretti A, Tsai SJ, Turecki G, Weinshilboum R, McIntosh AM, Lewis CM, Kasper S, Zohar J, Souery D, Montgomery S, Albani D, Forloni G, Ferentinos P, Rujescu D, Mendlewicz J, Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, Adams MJ, Agerbo E, Air TM, Andlauer TF, Bacanu SA, Bækvad-Hansen M, Beekman AT, Bigdeli TB, Binder EB, Bryois J, Buttenschøn HN, Bybjerg-Grauholm J, Cai N, Castelao E, Christensen JH, Clarke TK, Coleman JR, Colodro-Conde L, Couvy-Duchesne B, Craddock N, Crawford GE, Davies G, Deary IJ, Degenhardt F, Derks EM, Direk N, Dolan CV, Dunn EC, Eley TC, Escott-Price V, Hassan Kiadeh FF, Finucane HK, Foo JC, Forstner AJ, Frank J, Gaspar HA, Gill M, Goes FS, Gordon SD, Grove J, Hall LS, Hansen CS, Hansen TF, Herms S, Hickie IB, Hoffmann P, Homuth G, Horn C, Hottenga JJ, Hougaard DM, Howard DM, Ising M, Jansen R, Jones I, Jones LA, Jorgenson E, Knowles JA, Kohane IS, Kraft J, Kretzschmar WW, Kutalik Z, Li Y, Lind PA, MacIntyre DJ, MacKinnon DF, Maier RM, Maier W, Marchini J, Mbarek H, McGrath P, McGuffin P, Medland SE, Mehta D, Middeldorp CM, Mihailov E, Milaneschi Y, Milani L, Mondimore FM, Montgomery GW, Mostafavi S, Mullins N, Nauck M, Ng B, Nivard MG, Nyholt DR, O’Reilly PF, Oskarsson H, Owen MJ, Painter JN, Pedersen CB, Pedersen MG, Peterson RE, Peyrot WJ, Pistis G, Posthuma D, Quiroz JA, Qvist P, Rice JP, Riley BP, Rivera M, Mirza SS, Schoevers R, Schulte EC, Shen L, Shi J, Shyn SI, Sigurdsson E, Sinnamon GC, Smit JH, Smith DJ, Stefansson H, Steinberg S, Streit F, Strohmaier J, Tansey KE, Teismann H, Teumer A, Thompson W, Thomson PA, Thorgeirsson TE, Traylor M, Treutlein J, Trubetskoy V, Uitterlinden AG, Umbricht D, Van der Auwera S, van Hemert AM, Viktorin A, Visscher PM, Wang Y, Webb BT, Weinsheimer SM, Wellmann J, Willemsen G, Witt SH, Wu Y, Xi HS, Yang J, Zhang F, Arolt V, Baune BT, Berger K, Boomsma DI, Cichon S, Dannlowski U, de Geus E, DePaulo JR, Domenici E, Domschke K, Esko T, Grabe HJ, Hamilton SP, Hayward C, Heath AC, Kendler KS, Kloiber S, Lewis G, Li QS, Lucae S, Madden PA, Magnusson PK, Martin NG, McIntosh AM, Metspalu A, Mors O, Mortensen PB, Müller-Myhsok B, Nordentoft M, Nöthen MM, O’Donovan MC, Paciga SA, Pedersen NL, Penninx BW, Perlis RH, Porteous DJ, Potash JB, Preisig M, Rietschel M, Schaefer C, Schulze TG, Smoller JW, Stefansson K, Tiemeier H, Uher R, Völzke H, Weissman MM, Werge T, Lewis CM, Levinson DF, Breen G, Børglum AD, Sullivan PF. Identifying the Common Genetic Basis of Antidepressant Response. Biol Psychiatry Glob Open Sci 2022; 2:115-126. [PMID: 35712048 PMCID: PMC9117153 DOI: 10.1016/j.bpsgos.2021.07.008] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/15/2021] [Accepted: 07/19/2021] [Indexed: 01/20/2023] Open
Abstract
Background Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction. Methods Genome-wide analysis of remission (n remit = 1852, n nonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism-based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA. Results Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism-based heritability was significantly different from zero for remission (h 2 = 0.132, SE = 0.056) but not for percentage improvement (h 2 = -0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response. Conclusions This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.
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26
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Barda N, Dagan N, Cohen C, Hernán MA, Lipsitch M, Kohane IS, Reis BY, Balicer RD. Effectiveness of a third dose of the BNT162b2 mRNA COVID-19 vaccine for preventing severe outcomes in Israel: an observational study. Lancet 2021; 398:2093-2100. [PMID: 34756184 PMCID: PMC8555967 DOI: 10.1016/s0140-6736(21)02249-2] [Citation(s) in RCA: 578] [Impact Index Per Article: 192.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 09/17/2021] [Accepted: 10/01/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Many countries are experiencing a resurgence of COVID-19, driven predominantly by the delta (B.1.617.2) variant of SARS-CoV-2. In response, these countries are considering the administration of a third dose of mRNA COVID-19 vaccine as a booster dose to address potential waning immunity over time and reduced effectiveness against the delta variant. We aimed to use the data repositories of Israel's largest health-care organisation to evaluate the effectiveness of a third dose of the BNT162b2 mRNA vaccine for preventing severe COVID-19 outcomes. METHODS Using data from Clalit Health Services, which provides mandatory health-care coverage for over half of the Israeli population, individuals receiving a third vaccine dose between July 30, 2020, and Sept 23, 2021, were matched (1:1) to demographically and clinically similar controls who did not receive a third dose. Eligible participants had received the second vaccine dose at least 5 months before the recruitment date, had no previous documented SARS-CoV-2 infection, and had no contact with the health-care system in the 3 days before recruitment. Individuals who are health-care workers, live in long-term care facilities, or are medically confined to their homes were excluded. Primary outcomes were COVID-19-related admission to hospital, severe disease, and COVID-19-related death. The third dose effectiveness for each outcome was estimated as 1 - risk ratio using the Kaplan-Meier estimator. FINDINGS 1 158 269 individuals were eligible to be included in the third dose group. Following matching, the third dose and control groups each included 728 321 individuals. Participants had a median age of 52 years (IQR 37-68) and 51% were female. The median follow-up time was 13 days (IQR 6-21) in both groups. Vaccine effectiveness evaluated at least 7 days after receipt of the third dose, compared with receiving only two doses at least 5 months ago, was estimated to be 93% (231 events for two doses vs 29 events for three doses; 95% CI 88-97) for admission to hospital, 92% (157 vs 17 events; 82-97) for severe disease, and 81% (44 vs seven events; 59-97) for COVID-19-related death. INTERPRETATION Our findings suggest that a third dose of the BNT162b2 mRNA vaccine is effective in protecting individuals against severe COVID-19-related outcomes, compared with receiving only two doses at least 5 months ago. FUNDING The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute.
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Affiliation(s)
- Noam Barda
- Clalit Research Institute, Innovation Division, Clalit Health Services, Tel Aviv, Israel; Software and Information Systems Engineering, Ben Gurion University of the Negev, Be'er Sheva, Israel; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA
| | - Noa Dagan
- Clalit Research Institute, Innovation Division, Clalit Health Services, Tel Aviv, Israel; Software and Information Systems Engineering, Ben Gurion University of the Negev, Be'er Sheva, Israel; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA
| | - Cyrille Cohen
- The Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | - Miguel A Hernán
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA; Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA; CAUSALab, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Marc Lipsitch
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA; Department of Immunology and Infectious Diseases, Harvard T H Chan School of Public Health, Boston, MA, USA; Center for Communicable Disease Dynamics, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Ben Y Reis
- The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA; Predictive Medicine Group, Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Ran D Balicer
- Clalit Research Institute, Innovation Division, Clalit Health Services, Tel Aviv, Israel; School of Public Health, Faculty of Health Sciences, Ben Gurion University of the Negev, Be'er Sheva, Israel; The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA.
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27
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Weber GM, Zhang HG, L'Yi S, Bonzel CL, Hong C, Avillach P, Gutiérrez-Sacristán A, Palmer NP, Tan ALM, Wang X, Yuan W, Gehlenborg N, Alloni A, Amendola DF, Bellasi A, Bellazzi R, Beraghi M, Bucalo M, Chiovato L, Cho K, Dagliati A, Estiri H, Follett RW, García Barrio N, Hanauer DA, Henderson DW, Ho YL, Holmes JH, Hutch MR, Kavuluru R, Kirchoff K, Klann JG, Krishnamurthy AK, Le TT, Liu M, Loh NHW, Lozano-Zahonero S, Luo Y, Maidlow S, Makoudjou A, Malovini A, Martins MR, Moal B, Morris M, Mowery DL, Murphy SN, Neuraz A, Ngiam KY, Okoshi MP, Omenn GS, Patel LP, Pedrera Jiménez M, Prudente RA, Samayamuthu MJ, Sanz Vidorreta FJ, Schriver ER, Schubert P, Serrano Balazote P, Tan BW, Tanni SE, Tibollo V, Visweswaran S, Wagholikar KB, Xia Z, Zöller D, Kohane IS, Cai T, South AM, Brat GA. Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study. J Med Internet Res 2021; 23:e34625. [PMID: 34889759 PMCID: PMC8672293 DOI: 10.2196/34625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 11/10/2021] [Indexed: 11/15/2022] Open
Affiliation(s)
- Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | | | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Amelia Li Min Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Anna Alloni
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | - Danilo F Amendola
- Clinical Research Unit, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Antonio Bellasi
- Division of Nephrology, Department of Medicine, Ente Ospedaliero Cantonale, Lugano, Switzerland
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Michele Beraghi
- Information Technology Department, Azienda Socio-Sanitaria Territoriale di Pavia, Pavia, Italy
| | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - Arianna Dagliati
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Robert W Follett
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Darren W Henderson
- Department of Biomedical Informatics, University of Kentucky, Lexington, KY, United States
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.,Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Meghan R Hutch
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Ramakanth Kavuluru
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, United States
| | - Katie Kirchoff
- Medical University of South Carolina, Charleston, SC, United States
| | - Jeffrey G Klann
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Ashok K Krishnamurthy
- Department of Computer Science, Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Trang T Le
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Molei Liu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health System, Singapore, Singapore
| | - Sara Lozano-Zahonero
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Sarah Maidlow
- Michigan Institute for Clinical & Health Research Informatics, University of Michigan, Ann Arbor, MI, United States
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | | | - Bertrand Moal
- Informatique et archivistique médicales unit, Bordeaux University Hospital, Bordeaux, France
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Antoine Neuraz
- Department of Biomedical Informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris, University of Paris, Paris, France
| | - Kee Yuan Ngiam
- Department of Biomedical Informatics, Institute for Digital Medicine, National University Health System, Singapore, Singapore
| | - Marina P Okoshi
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Lav P Patel
- Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, United States
| | | | - Robson A Prudente
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | | | - Fernando J Sanz Vidorreta
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Emily R Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, PA, United States
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | | | - Byorn Wl Tan
- Department of Medicine, National University Health System, Singapore, Singapore
| | - Suzana E Tanni
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | | | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Daniela Zöller
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
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- see Authors' Contributions,
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Andrew M South
- Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
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Ektefaie Y, Yuan W, Dillon DA, Lin NU, Golden JA, Kohane IS, Yu KH. Integrative multiomics-histopathology analysis for breast cancer classification. NPJ Breast Cancer 2021; 7:147. [PMID: 34845230 PMCID: PMC8630188 DOI: 10.1038/s41523-021-00357-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 11/03/2021] [Indexed: 02/07/2023] Open
Abstract
Histopathologic evaluation of biopsy slides is a critical step in diagnosing and subtyping breast cancers. However, the connections between histology and multi-omics status have never been systematically explored or interpreted. We developed weakly supervised deep learning models over hematoxylin-and-eosin-stained slides to examine the relations between visual morphological signal, clinical subtyping, gene expression, and mutation status in breast cancer. We first designed fully automated models for tumor detection and pathology subtype classification, with the results validated in independent cohorts (area under the receiver operating characteristic curve ≥ 0.950). Using only visual information, our models achieved strong predictive performance in estrogen/progesterone/HER2 receptor status, PAM50 status, and TP53 mutation status. We demonstrated that these models learned lymphocyte-specific morphological signals to identify estrogen receptor status. Examination of the PAM50 cohort revealed a subset of PAM50 genes whose expression reflects cancer morphology. This work demonstrates the utility of deep learning-based image models in both clinical and research regimes, through its ability to uncover connections between visual morphology and genetic statuses.
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Affiliation(s)
- Yasha Ektefaie
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
| | - Deborah A Dillon
- Department of Pathology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA
| | - Nancy U Lin
- Department of Medicine, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Jeffrey A Golden
- Department of Pathology, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
- Burns and Allen Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA.
- Department of Pathology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
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29
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Zhang HG, Hejblum BP, Weber GM, Palmer NP, Churchill SE, Szolovits P, Murphy SN, Liao KP, Kohane IS, Cai T. ATLAS: an automated association test using probabilistically linked health records with application to genetic studies. J Am Med Inform Assoc 2021; 28:2582-2592. [PMID: 34608931 DOI: 10.1093/jamia/ocab187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/14/2021] [Accepted: 08/22/2021] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE Large amounts of health data are becoming available for biomedical research. Synthesizing information across databases may capture more comprehensive pictures of patient health and enable novel research studies. When no gold standard mappings between patient records are available, researchers may probabilistically link records from separate databases and analyze the linked data. However, previous linked data inference methods are constrained to certain linkage settings and exhibit low power. Here, we present ATLAS, an automated, flexible, and robust association testing algorithm for probabilistically linked data. MATERIALS AND METHODS Missing variables are imputed at various thresholds using a weighted average method that propagates uncertainty from probabilistic linkage. Next, estimated effect sizes are obtained using a generalized linear model. ATLAS then conducts the threshold combination test by optimally combining P values obtained from data imputed at varying thresholds using Fisher's method and perturbation resampling. RESULTS In simulations, ATLAS controls for type I error and exhibits high power compared to previous methods. In a real-world genetic association study, meta-analysis of ATLAS-enabled analyses on a linked cohort with analyses using an existing cohort yielded additional significant associations between rheumatoid arthritis genetic risk score and laboratory biomarkers. DISCUSSION Weighted average imputation weathers false matches and increases contribution of true matches to mitigate linkage error-induced bias. The threshold combination test avoids arbitrarily choosing a threshold to rule a match, thus automating linked data-enabled analyses and preserving power. CONCLUSION ATLAS promises to enable novel and powerful research studies using linked data to capitalize on all available data sources.
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Affiliation(s)
- Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Biological Sciences, Columbia University, New York City, New York, USA
| | - Boris P Hejblum
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.,Bordeaux Population Health, Université de Bordeaux, Inserm U1219, Inria SISTM, Bordeaux, France
| | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Susanne E Churchill
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Peter Szolovits
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Research IS and Computing, Mass General Brigham HealthCare, Charlestown, Massachusetts, USA
| | - Katherine P Liao
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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30
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Le TT, Gutiérrez-Sacristán A, Son J, Hong C, South AM, Beaulieu-Jones BK, Loh NHW, Luo Y, Morris M, Ngiam KY, Patel LP, Samayamuthu MJ, Schriver E, Tan ALM, Moore J, Cai T, Omenn GS, Avillach P, Kohane IS, Visweswaran S, Mowery DL, Xia Z. Multinational characterization of neurological phenotypes in patients hospitalized with COVID-19. Sci Rep 2021; 11:20238. [PMID: 34642371 PMCID: PMC8510999 DOI: 10.1038/s41598-021-99481-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 09/23/2021] [Indexed: 01/08/2023] Open
Abstract
Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January-September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7-7.8%, pFDR < 0.001) and unspecified disorders of the brain (8.1%, 5.7-10.5%, pFDR < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19-25%), cerebrovascular diseases (24%, 13-35%), nontraumatic intracranial hemorrhage (34%, 20-50%), encephalitis and/or myelitis (37%, 17-60%) and myopathy (72%, 67-77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease.
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Affiliation(s)
- Trang T Le
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Jiyeon Son
- Department of Neurology, University of Pittsburgh, Biomedical Science Tower 3, Suite 7014, 3501 5th Avenue, Pittsburgh, PA, 15260, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andrew M South
- Department of Pediatrics, Wake Forest School of Medicine, Winston Salem, NC, USA
| | | | - Ne Hooi Will Loh
- Department of Critical Care, National University Health Systems, Singapore, Singapore
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kee Yuan Ngiam
- Department of Surgery, National University Health Systems, Singapore, Singapore
| | - Lav P Patel
- Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA
| | | | - Emily Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, PA, USA
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Jason Moore
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Biomedical Science Tower 3, Suite 7014, 3501 5th Avenue, Pittsburgh, PA, 15260, USA.
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31
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Weber GM, Zhang HG, L'Yi S, Bonzel CL, Hong C, Avillach P, Gutiérrez-Sacristán A, Palmer NP, Tan ALM, Wang X, Yuan W, Gehlenborg N, Alloni A, Amendola DF, Bellasi A, Bellazzi R, Beraghi M, Bucalo M, Chiovato L, Cho K, Dagliati A, Estiri H, Follett RW, García Barrio N, Hanauer DA, Henderson DW, Ho YL, Holmes JH, Hutch MR, Kavuluru R, Kirchoff K, Klann JG, Krishnamurthy AK, Le TT, Liu M, Loh NHW, Lozano-Zahonero S, Luo Y, Maidlow S, Makoudjou A, Malovini A, Martins MR, Moal B, Morris M, Mowery DL, Murphy SN, Neuraz A, Ngiam KY, Okoshi MP, Omenn GS, Patel LP, Pedrera Jiménez M, Prudente RA, Samayamuthu MJ, Sanz Vidorreta FJ, Schriver ER, Schubert P, Serrano Balazote P, Tan BW, Tanni SE, Tibollo V, Visweswaran S, Wagholikar KB, Xia Z, Zöller D, Kohane IS, Cai T, South AM, Brat GA. International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study. J Med Internet Res 2021; 23:e31400. [PMID: 34533459 PMCID: PMC8510151 DOI: 10.2196/31400] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/02/2021] [Accepted: 09/02/2021] [Indexed: 02/06/2023] Open
Abstract
Background Many countries have experienced 2 predominant waves of COVID-19–related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic. Objective In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic. Methods Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19. Results Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain. Conclusions Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.
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Affiliation(s)
- Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | | | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Amelia Li Min Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Anna Alloni
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | - Danilo F Amendola
- Clinical Research Unit, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Antonio Bellasi
- Division of Nephrology, Department of Medicine, Ente Ospedaliero Cantonale, Lugano, Switzerland
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Michele Beraghi
- Information Technology Department, Azienda Socio-Sanitaria Territoriale di Pavia, Pavia, Italy
| | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - Arianna Dagliati
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Robert W Follett
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Darren W Henderson
- Department of Biomedical Informatics, University of Kentucky, Lexington, KY, United States
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.,Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Meghan R Hutch
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Ramakanth Kavuluru
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, United States
| | - Katie Kirchoff
- Medical University of South Carolina, Charleston, SC, United States
| | - Jeffrey G Klann
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Ashok K Krishnamurthy
- Department of Computer Science, Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Trang T Le
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Molei Liu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health System, Singapore, Singapore
| | - Sara Lozano-Zahonero
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Sarah Maidlow
- Michigan Institute for Clinical & Health Research Informatics, University of Michigan, Ann Arbor, MI, United States
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | | | - Bertrand Moal
- Informatique et archivistique médicales unit, Bordeaux University Hospital, Bordeaux, France
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Antoine Neuraz
- Department of Biomedical Informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris, University of Paris, Paris, France
| | - Kee Yuan Ngiam
- Department of Biomedical Informatics, Institute for Digital Medicine, National University Health System, Singapore, Singapore
| | - Marina P Okoshi
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Lav P Patel
- Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, United States
| | | | - Robson A Prudente
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | | | - Fernando J Sanz Vidorreta
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Emily R Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, PA, United States
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | | | - Byorn Wl Tan
- Department of Medicine, National University Health System, Singapore, Singapore
| | - Suzana E Tanni
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | | | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Daniela Zöller
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | -
- see Authors' Contributions,
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Andrew M South
- Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
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Wang F, Yang S, Palmer N, Fox K, Kohane IS, Liao KP, Yu KH, Kou SC. Real-world data analyses unveiled the immune-related adverse effects of immune checkpoint inhibitors across cancer types. NPJ Precis Oncol 2021; 5:82. [PMID: 34508179 PMCID: PMC8433190 DOI: 10.1038/s41698-021-00223-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 08/20/2021] [Indexed: 12/17/2022] Open
Abstract
Immune checkpoint inhibitors have demonstrated significant survival benefits in treating many types of cancers. However, their immune-related adverse events (irAEs) have not been systematically evaluated across cancer types in large-scale real-world populations. To address this gap, we conducted real-world data analyses using nationwide insurance claims data with 85.97 million enrollees across 8 years. We identified a significantly increased risk of developing irAEs among patients receiving immunotherapy agents in all seven cancer types commonly treated with immune checkpoint inhibitors. By six months after treatment initialization, those receiving immunotherapy were 1.50-4.00 times (95% CI, lower bound from 1.15 to 2.16, upper bound from 1.69 to 20.36) more likely to develop irAEs in the first 6 months of treatment, compared to matched chemotherapy or targeted therapy groups, with a total of 92,858 patients. The risk of developing irAEs among patients using nivolumab is higher compared to those using pembrolizumab. These results confirmed the need for clinicians to assess irAEs among cancer patients undergoing immunotherapy as part of management. Our methods are extensible to characterizing the effectiveness and adverse effects of novel treatments in large populations in an efficient and economical fashion.
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Affiliation(s)
- Feicheng Wang
- Department of Statistics, Harvard University, Cambridge, MA, USA
| | - Shihao Yang
- Department of Statistics, Harvard University, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Nathan Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kathe Fox
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Katherine P Liao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
| | - S C Kou
- Department of Statistics, Harvard University, Cambridge, MA, USA.
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Feroe AG, Uppal N, Gutiérrez-Sacristán A, Mousavi S, Greenspun P, Surati R, Kohane IS, Avillach P. Medication Use in the Management of Comorbidities Among Individuals With Autism Spectrum Disorder From a Large Nationwide Insurance Database. JAMA Pediatr 2021; 175:957-965. [PMID: 34097007 PMCID: PMC8185632 DOI: 10.1001/jamapediatrics.2021.1329] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
IMPORTANCE Although there is no pharmacological treatment for autism spectrum disorder (ASD) itself, behavioral and pharmacological therapies have been used to address its symptoms and common comorbidities. A better understanding of the medications used to manage comorbid conditions in this growing population is critical; however, most previous efforts have been limited in size, duration, and lack of broad representation. OBJECTIVE To use a nationally representative database to uncover trends in the prevalence of co-occurring conditions and medication use in the management of symptoms and comorbidities over time among US individuals with ASD. DESIGN, SETTING, AND PARTICIPANTS This retrospective, population-based cohort study mined a nationwide, managed health plan claims database containing more than 86 million unique members. Data from January 1, 2014, to December 31, 2019, were used to analyze prescription frequency and diagnoses of comorbidities. A total of 26 722 individuals with ASD who had been prescribed at least 1 of 24 medications most commonly prescribed to treat ASD symptoms or comorbidities during the 6-year study period were included in the analysis. EXPOSURES Diagnosis codes for ASD based on International Classification of Diseases, Ninth Revision, and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision. MAIN OUTCOMES AND MEASURES Quantitative estimates of prescription frequency for the 24 most commonly prescribed medications among the study cohort and the most common comorbidities associated with each medication in this population. RESULTS Among the 26 722 individuals with ASD included in the analysis (77.7% male; mean [SD] age, 14.45 [9.40] years), polypharmacy was common, ranging from 28.6% to 31.5%. Individuals' prescription regimens changed frequently within medication classes, rather than between classes. The prescription frequency of a specific medication varied considerably, depending on the coexisting diagnosis of a given comorbidity. Of the 24 medications assessed, 15 were associated with at least a 15% prevalence of a mood disorder, and 11 were associated with at least a 15% prevalence of attention-deficit/hyperactivity disorder. For patients taking antipsychotics, the 2 most common comorbidities were combined type attention-deficit/hyperactivity disorder (11.6%-17.8%) and anxiety disorder (13.1%-30.1%). CONCLUSIONS AND RELEVANCE This study demonstrated considerable variability and transiency in the use of prescription medications by US clinicians to manage symptoms and comorbidities associated with ASD. These findings support the importance of early and ongoing surveillance of patients with ASD and co-occurring conditions and offer clinicians insight on the targeted therapies most commonly used to manage co-occurring conditions. Future research and policy efforts are critical to assess the extent to which pharmacological management of comorbidities affects quality of life and functioning in patients with ASD while continuing to optimize clinical guidelines, to ensure effective care for this growing population.
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Affiliation(s)
| | | | | | - Sajad Mousavi
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Philip Greenspun
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Rajeev Surati
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Isaac S. Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts,Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts,Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
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Kohane IS. Finding a new balance between a genetics-first or phenotype-first approach to the study of disease. Neuron 2021; 109:2216-2219. [PMID: 34293292 DOI: 10.1016/j.neuron.2021.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Successes in neuroscience using a genetics-first approach to characterizing disorders such as autism have eclipsed the scientific and clinical value of a comprehensive phenotype-first-clinical or molecular-approach. Recent high-throughput phenotyping techniques using machine learning, electronic medical records, and even administrative databases show the value of a synthesis between the two approaches.
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Affiliation(s)
- Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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35
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Klann JG, Estiri H, Weber GM, Moal B, Avillach P, Hong C, Tan ALM, Beaulieu-Jones BK, Castro V, Maulhardt T, Geva A, Malovini A, South AM, Visweswaran S, Morris M, Samayamuthu MJ, Omenn GS, Ngiam KY, Mandl KD, Boeker M, Olson KL, Mowery DL, Follett RW, Hanauer DA, Bellazzi R, Moore JH, Loh NHW, Bell DS, Wagholikar KB, Chiovato L, Tibollo V, Rieg S, Li ALLJ, Jouhet V, Schriver E, Xia Z, Hutch M, Luo Y, Kohane IS, Brat GA, Murphy SN. Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data. J Am Med Inform Assoc 2021; 28:1411-1420. [PMID: 33566082 PMCID: PMC7928835 DOI: 10.1093/jamia/ocab018] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/14/2021] [Accepted: 01/29/2021] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVE The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity. MATERIALS AND METHODS Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site. RESULTS The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review. DISCUSSION We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions. CONCLUSIONS We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.
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Affiliation(s)
- Jeffrey G Klann
- Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Hossein Estiri
- Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Griffin M Weber
- Department of Biomedical Informatics, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Bertrand Moal
- IAM Unit, Public Health Department , Bordeaux University Hospital, Bordeaux, France
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Brett K Beaulieu-Jones
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Victor Castro
- Research Information Science and Computing, Mass General Brigham, Boston, Massachusetts, USA
| | - Thomas Maulhardt
- Institute of Medical Biometry and Statistics, Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Alon Geva
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts, USA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy
| | - Andrew M South
- Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, North Carolina, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Malarkodi J Samayamuthu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Kee Yuan Ngiam
- Department of Biomedical Informatics-WisDM, National University Health System, Singapore
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Martin Boeker
- Institute of Medical Biometry and Statistics, Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Karen L Olson
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Robert W Follett
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Riccardo Bellazzi
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.,Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy
| | - Jason H Moore
- Department of Biostatistics, Epidemiology, and Informatics, Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Ne-Hooi Will Loh
- Division of Critical Care, National University Health System, Singapore
| | - Douglas S Bell
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | | | - Luca Chiovato
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.,Department of Internal Medicine and Medical Therapy, University of Pavia, Pavia, Italy
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy
| | - Siegbert Rieg
- Division of Infectious Diseases, Department of Medicine II, Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Anthony L L J Li
- National Center for Infectious Diseases, Tan Tock Seng Hospital, Singapore
| | - Vianney Jouhet
- ERIAS-INSERM U1219 BPH, Bordeaux University Hospital, Bordeaux, France
| | - Emily Schriver
- Data Analytics Center, Penn Medicine, Philadelphia, Pennsylvania, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Meghan Hutch
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Research Information Science and Computing , Mass General Brigham, Boston, Massachusetts, USA
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Gordon WJ, Gottlieb D, Kreda D, Mandel JC, Mandl KD, Kohane IS. Patient-led data sharing for clinical bioinformatics research: USCDI and beyond. J Am Med Inform Assoc 2021; 28:2298-2300. [PMID: 34279631 DOI: 10.1093/jamia/ocab133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/25/2021] [Accepted: 06/15/2021] [Indexed: 11/15/2022] Open
Abstract
The 21st Century Cures Act, passed in 2016, and the Final Rules it called for create a roadmap for enabling patient access to their electronic health information. The set of data to be made available, as determined by the Office of the National Coordinator for Health IT through the US Core Data for Interoperability expansion process, will impact the value creation of this improved data liquidity. In this commentary, we look at the potential for significant value creation from USCDI in the context of clinical bioinformatics research and advocate for the research community's involvement in the USCDI process to propel this value creation forward. We also describe 1 mechanism-using existing required APIs for full data export capabilities-that could pragmatically enable this value creation at minimal additional technical lift beyond the current regulatory requirements.
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Affiliation(s)
- William J Gordon
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Mass General Brigham, Boston, Massachusetts, USA
| | - Daniel Gottlieb
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - David Kreda
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Joshua C Mandel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.,Microsoft Healthcare, Redmond, Washington, USA
| | - Kenneth D Mandl
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
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38
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Huang Y, Yuan W, Kohane IS, Beaulieu-Jones BK. Illustrating potential effects of alternate control populations on real-world evidence-based statistical analyses. JAMIA Open 2021; 4:ooab045. [PMID: 34142018 PMCID: PMC8206406 DOI: 10.1093/jamiaopen/ooab045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 05/06/2021] [Indexed: 11/14/2022] Open
Abstract
Objective Case-control study designs are commonly used in retrospective analyses of real-world evidence (RWE). Due to the increasingly wide availability of RWE, it can be difficult to determine whether findings are robust or the result of testing multiple hypotheses. Materials and Methods We investigate the potential effects of modifying cohort definitions in a case-control association study between depression and type 2 diabetes mellitus. We used a large (>75 million individuals) de-identified administrative claims database to observe the effects of minor changes to the requirements of glucose and hemoglobin A1c tests in the control group. Results We found that small permutations to the criteria used to define the control population result in significant shifts in both the demographic structure of the identified cohort as well as the odds ratio of association. These differences remain present when testing against age- and sex-matched controls. Discussion Analyses of RWE need to be carefully designed to avoid issues of multiple testing. Minor changes to control cohorts can lead to significantly different results and have the potential to alter even prospective studies through selection bias. Conclusion We believe this work offers strong support for the need for robust guidelines, best practices, and regulations around the use of observational RWE for clinical or regulatory decision-making.
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Affiliation(s)
- Yidi Huang
- Department of Biomedical Informatics, Harvard Medical School, Countway Library, Boston, Massachusetts, USA
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Countway Library, Boston, Massachusetts, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Countway Library, Boston, Massachusetts, USA
| | - Brett K Beaulieu-Jones
- Department of Biomedical Informatics, Harvard Medical School, Countway Library, Boston, Massachusetts, USA
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Kobren SN, Baldridge D, Velinder M, Krier JB, LeBlanc K, Esteves C, Pusey BN, Züchner S, Blue E, Lee H, Huang A, Bastarache L, Bican A, Cogan J, Marwaha S, Alkelai A, Murdock DR, Liu P, Wegner DJ, Paul AJ, Sunyaev SR, Kohane IS. Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases. Genet Med 2021; 23:1075-1085. [PMID: 33580225 PMCID: PMC8187147 DOI: 10.1038/s41436-020-01084-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 12/14/2020] [Accepted: 12/17/2020] [Indexed: 12/31/2022] Open
Abstract
PURPOSE Genomic sequencing has become an increasingly powerful and relevant tool to be leveraged for the discovery of genetic aberrations underlying rare, Mendelian conditions. Although the computational tools incorporated into diagnostic workflows for this task are continually evolving and improving, we nevertheless sought to investigate commonalities across sequencing processing workflows to reveal consensus and standard practice tools and highlight exploratory analyses where technical and theoretical method improvements would be most impactful. METHODS We collected details regarding the computational approaches used by a genetic testing laboratory and 11 clinical research sites in the United States participating in the Undiagnosed Diseases Network via meetings with bioinformaticians, online survey forms, and analyses of internal protocols. RESULTS We found that tools for processing genomic sequencing data can be grouped into four distinct categories. Whereas well-established practices exist for initial variant calling and quality control steps, there is substantial divergence across sites in later stages for variant prioritization and multimodal data integration, demonstrating a diversity of approaches for solving the most mysterious undiagnosed cases. CONCLUSION The largest differences across diagnostic workflows suggest that advances in structural variant detection, noncoding variant interpretation, and integration of additional biomedical data may be especially promising for solving chronically undiagnosed cases.
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Affiliation(s)
| | - Dustin Baldridge
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Matt Velinder
- Center for Genomic Discovery, University of Utah, Salt Lake City, UT, USA
| | - Joel B Krier
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kimberly LeBlanc
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Cecilia Esteves
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Barbara N Pusey
- National Human Genome Research Institute (NHGRI) at the National Institutes of Health (NIH), Bethesda, MD, USA
| | - Stephan Züchner
- Department of Human Genetics and Hussman Institute for Human Genomics, University of Miami Health System, Miami, FL, USA
| | - Elizabeth Blue
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Hane Lee
- Department of Human Genetics, David Geffen School of Medicine at the University of California, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at the University of California, Los Angeles, CA, USA
| | - Alden Huang
- Department of Human Genetics, David Geffen School of Medicine at the University of California, Los Angeles, CA, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Anna Bican
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Joy Cogan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shruti Marwaha
- Stanford Center for Undiagnosed Diseases, Stanford, CA, USA
| | - Anna Alkelai
- Institute for Genomic Medicine, Columbia University Medical Center, New York City, NY, USA
| | - David R Murdock
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Pengfei Liu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Baylor Genetics, Houston, TX, USA
| | - Daniel J Wegner
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Alexander J Paul
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Shamil R Sunyaev
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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Bourgeois FT, Gutiérrez-Sacristán A, Keller MS, Liu M, Hong C, Bonzel CL, Tan ALM, Aronow BJ, Boeker M, Booth J, Cruz Rojo J, Devkota B, García Barrio N, Gehlenborg N, Geva A, Hanauer DA, Hutch MR, Issitt RW, Klann JG, Luo Y, Mandl KD, Mao C, Moal B, Moshal KL, Murphy SN, Neuraz A, Ngiam KY, Omenn GS, Patel LP, Jiménez MP, Sebire NJ, Balazote PS, Serret-Larmande A, South AM, Spiridou A, Taylor DM, Tippmann P, Visweswaran S, Weber GM, Kohane IS, Cai T, Avillach P. International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries. JAMA Netw Open 2021; 4:e2112596. [PMID: 34115127 PMCID: PMC8196345 DOI: 10.1001/jamanetworkopen.2021.12596] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
IMPORTANCE Additional sources of pediatric epidemiological and clinical data are needed to efficiently study COVID-19 in children and youth and inform infection prevention and clinical treatment of pediatric patients. OBJECTIVE To describe international hospitalization trends and key epidemiological and clinical features of children and youth with COVID-19. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study included pediatric patients hospitalized between February 2 and October 10, 2020. Patient-level electronic health record (EHR) data were collected across 27 hospitals in France, Germany, Spain, Singapore, the UK, and the US. Patients younger than 21 years who tested positive for COVID-19 and were hospitalized at an institution participating in the Consortium for Clinical Characterization of COVID-19 by EHR were included in the study. MAIN OUTCOMES AND MEASURES Patient characteristics, clinical features, and medication use. RESULTS There were 347 males (52%; 95% CI, 48.5-55.3) and 324 females (48%; 95% CI, 44.4-51.3) in this study's cohort. There was a bimodal age distribution, with the greatest proportion of patients in the 0- to 2-year (199 patients [30%]) and 12- to 17-year (170 patients [25%]) age range. Trends in hospitalizations for 671 children and youth found discrete surges with variable timing across 6 countries. Data from this cohort mirrored national-level pediatric hospitalization trends for most countries with available data, with peaks in hospitalizations during the initial spring surge occurring within 23 days in the national-level and 4CE data. A total of 27 364 laboratory values for 16 laboratory tests were analyzed, with mean values indicating elevations in markers of inflammation (C-reactive protein, 83 mg/L; 95% CI, 53-112 mg/L; ferritin, 417 ng/mL; 95% CI, 228-607 ng/mL; and procalcitonin, 1.45 ng/mL; 95% CI, 0.13-2.77 ng/mL). Abnormalities in coagulation were also evident (D-dimer, 0.78 ug/mL; 95% CI, 0.35-1.21 ug/mL; and fibrinogen, 477 mg/dL; 95% CI, 385-569 mg/dL). Cardiac troponin, when checked (n = 59), was elevated (0.032 ng/mL; 95% CI, 0.000-0.080 ng/mL). Common complications included cardiac arrhythmias (15.0%; 95% CI, 8.1%-21.7%), viral pneumonia (13.3%; 95% CI, 6.5%-20.1%), and respiratory failure (10.5%; 95% CI, 5.8%-15.3%). Few children were treated with COVID-19-directed medications. CONCLUSIONS AND RELEVANCE This study of EHRs of children and youth hospitalized for COVID-19 in 6 countries demonstrated variability in hospitalization trends across countries and identified common complications and laboratory abnormalities in children and youth with COVID-19 infection. Large-scale informatics-based approaches to integrate and analyze data across health care systems complement methods of disease surveillance and advance understanding of epidemiological and clinical features associated with COVID-19 in children and youth.
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Affiliation(s)
- Florence T. Bourgeois
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
| | | | - Mark S. Keller
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Molei Liu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Amelia L. M. Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Bruce J. Aronow
- Departments of Biomedical Informatics, Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Ohio
| | - Martin Boeker
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Germany
| | - John Booth
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, London, United Kingdom
| | - Jaime Cruz Rojo
- Department of Health Informatics, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Batsal Devkota
- Department of Biomedical Health Informatics and the Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Noelia García Barrio
- Department of Health Informatics, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Alon Geva
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children’s Hospital, Boston, Massachusetts
| | - David A. Hanauer
- Department of Learning Health Sciences, University of Michigan, Ann Arbor
| | - Meghan R. Hutch
- Department of Preventive Medicine, Northwestern University, Evanston, Illinois
| | - Richard W. Issitt
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, London, United Kingdom
| | | | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Evanston, Illinois
| | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
| | - Chengsheng Mao
- Department of Preventive Medicine, Northwestern University, Evanston, Illinois
| | - Bertrand Moal
- IAM Unit, Bordeaux University Hospital, Bordeaux, France
| | - Karyn L. Moshal
- Department of Infectious Diseases, Great Ormond Street Hospital for Children, London, United Kingdom
| | - Shawn N. Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Antoine Neuraz
- Department of Biomedical Informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris, University of Paris, Paris, France
| | - Kee Yuan Ngiam
- Department of Biomedical informatics, WiSDM, National University Health Systems Singapore, Singapore
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, & School of Public Health, University of Michigan, Ann Arbor
| | - Lav P. Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City
| | | | - Neil J. Sebire
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, London, United Kingdom
| | | | | | - Andrew M. South
- Department of Pediatrics-Section of Nephrology, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, North Carolina
| | - Anastasia Spiridou
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, London, United Kingdom
| | - Deanne M. Taylor
- Department of Biomedical Health Informatics and the Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics, Perelman Medical School at the University of Pennsylvania, Philadelphia
| | - Patric Tippmann
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Germany
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Griffin M. Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Isaac S. Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Paul Avillach
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
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Beaulieu-Jones BK, Yuan W, Brat GA, Beam AL, Weber G, Ruffin M, Kohane IS. Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians? NPJ Digit Med 2021; 4:62. [PMID: 33785839 PMCID: PMC8010071 DOI: 10.1038/s41746-021-00426-3] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 02/08/2021] [Indexed: 01/03/2023] Open
Abstract
Machine learning can help clinicians to make individualized patient predictions only if researchers demonstrate models that contribute novel insights, rather than learning the most likely next step in a set of actions a clinician will take. We trained deep learning models using only clinician-initiated, administrative data for 42.9 million admissions using three subsets of data: demographic data only, demographic data and information available at admission, and the previous data plus charges recorded during the first day of admission. Models trained on charges during the first day of admission achieve performance close to published full EMR-based benchmarks for inpatient outcomes: inhospital mortality (0.89 AUC), prolonged length of stay (0.82 AUC), and 30-day readmission rate (0.71 AUC). Similar performance between models trained with only clinician-initiated data and those trained with full EMR data purporting to include information about patient state and physiology should raise concern in the deployment of these models. Furthermore, these models exhibited significant declines in performance when evaluated over only myocardial infarction (MI) patients relative to models trained over MI patients alone, highlighting the importance of physician diagnosis in the prognostic performance of these models. These results provide a benchmark for predictive accuracy trained only on prior clinical actions and indicate that models with similar performance may derive their signal by looking over clinician's shoulders-using clinical behavior as the expression of preexisting intuition and suspicion to generate a prediction. For models to guide clinicians in individual decisions, performance exceeding these benchmarks is necessary.
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Affiliation(s)
| | | | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andrew L Beam
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Griffin Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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42
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Marostica E, Barber R, Denize T, Kohane IS, Signoretti S, Golden JA, Yu KH. Development of a Histopathology Informatics Pipeline for Classification and Prediction of Clinical Outcomes in Subtypes of Renal Cell Carcinoma. Clin Cancer Res 2021; 27:2868-2878. [PMID: 33722896 DOI: 10.1158/1078-0432.ccr-20-4119] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 01/25/2021] [Accepted: 03/10/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE Histopathology evaluation is the gold standard for diagnosing clear cell (ccRCC), papillary, and chromophobe renal cell carcinoma (RCC). However, interrater variability has been reported, and the whole-slide histopathology images likely contain underutilized biological signals predictive of genomic profiles. EXPERIMENTAL DESIGN To address this knowledge gap, we obtained whole-slide histopathology images and demographic, genomic, and clinical data from The Cancer Genome Atlas, the Clinical Proteomic Tumor Analysis Consortium, and Brigham and Women's Hospital (Boston, MA) to develop computational methods for integrating data analyses. Leveraging these large and diverse datasets, we developed fully automated convolutional neural networks to diagnose renal cancers and connect quantitative pathology patterns with patients' genomic profiles and prognoses. RESULTS Our deep convolutional neural networks successfully detected malignancy (AUC in the independent validation cohort: 0.964-0.985), diagnosed RCC histologic subtypes (independent validation AUCs of the best models: 0.953-0.993), and predicted stage I ccRCC patients' survival outcomes (log-rank test P = 0.02). Our machine learning approaches further identified histopathology image features indicative of copy-number alterations (AUC > 0.7 in multiple genes in patients with ccRCC) and tumor mutation burden. CONCLUSIONS Our results suggest that convolutional neural networks can extract histologic signals predictive of patients' diagnoses, prognoses, and genomic variations of clinical importance. Our approaches can systematically identify previously unknown relations among diverse data modalities.
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Affiliation(s)
- Eliana Marostica
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Rebecca Barber
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts.,Department of Computer Science, Princeton University, Princeton, New Jersey
| | - Thomas Denize
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Sabina Signoretti
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Jeffrey A Golden
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts.,Cedars-Sinai Medical Center, Los Angeles, California
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts. .,Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts
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43
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Kohane IS, Aronow BJ, Avillach P, Beaulieu-Jones BK, Bellazzi R, Bradford RL, Brat GA, Cannataro M, Cimino JJ, García-Barrio N, Gehlenborg N, Ghassemi M, Gutiérrez-Sacristán A, Hanauer DA, Holmes JH, Hong C, Klann JG, Loh NHW, Luo Y, Mandl KD, Daniar M, Moore JH, Murphy SN, Neuraz A, Ngiam KY, Omenn GS, Palmer N, Patel LP, Pedrera-Jiménez M, Sliz P, South AM, Tan ALM, Taylor DM, Taylor BW, Torti C, Vallejos AK, Wagholikar KB, Weber GM, Cai T. What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask. J Med Internet Res 2021; 23:e22219. [PMID: 33600347 PMCID: PMC7927948 DOI: 10.2196/22219] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/14/2020] [Accepted: 01/10/2021] [Indexed: 12/13/2022] Open
Abstract
Coincident with the tsunami of COVID-19–related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.
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Affiliation(s)
- Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Bruce J Aronow
- Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, United States
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | | | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.,ICS Maugeri, Pavia, Italy
| | - Robert L Bradford
- North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Mario Cannataro
- Data Analytics Research Center, University Magna Graecia of Catanzaro, Catanzaro, Italy.,Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - James J Cimino
- Informatics Institute, University of Alabama at Birmingham, Birmingham, AL, United States
| | | | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Marzyeh Ghassemi
- Department of Computer Science and Medicine, University of Toronto, Toronto, ON, Canada
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Jeffrey G Klann
- Department of Medicine, Harvard Medical School, Boston, MA, United States.,Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA, United States
| | | | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
| | - Mohamad Daniar
- Clinical Research Informatics, Boston Children's Hospital, Boston, MA, United States
| | - Jason H Moore
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
| | - Shawn N Murphy
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.,Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Antoine Neuraz
- Department of Biomedical Informatics, Necker-Enfant Malades Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France.,Centre de Recherche des Cordeliers, INSERM UMRS 1138 Team 22, Université de Paris, Paris, France
| | - Kee Yuan Ngiam
- National University Health Systems, Singapore, Singapore
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Nathan Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS, United States
| | | | - Piotr Sliz
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
| | - Andrew M South
- Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Amelia Li Min Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.,Department of Biomedical Informatics, National University of Singapore, Singapore, Singapore
| | - Deanne M Taylor
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Pediatrics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, United States
| | - Bradley W Taylor
- Clinical and Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Carlo Torti
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Andrew K Vallejos
- Clinical and Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Kavishwar B Wagholikar
- Department of Medicine, Harvard Medical School, Boston, MA, United States.,Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA, United States
| | | | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
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44
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Yuan W, Beaulieu-Jones BK, Yu KH, Lipnick SL, Palmer N, Loscalzo J, Cai T, Kohane IS. Temporal bias in case-control design: preventing reliable predictions of the future. Nat Commun 2021; 12:1107. [PMID: 33597541 PMCID: PMC7889612 DOI: 10.1038/s41467-021-21390-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 01/22/2021] [Indexed: 02/07/2023] Open
Abstract
One of the primary tools that researchers use to predict risk is the case-control study. We identify a flaw, temporal bias, that is specific to and uniquely associated with these studies that occurs when the study period is not representative of the data that clinicians have during the diagnostic process. Temporal bias acts to undermine the validity of predictions by over-emphasizing features close to the outcome of interest. We examine the impact of temporal bias across the medical literature, and highlight examples of exaggerated effect sizes, false-negative predictions, and replication failure. Given the ubiquity and practical advantages of case-control studies, we discuss strategies for estimating the influence of and preventing temporal bias where it exists.
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Affiliation(s)
- William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | | | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Scott L Lipnick
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Center for Assessment Technology and Continuous Health, Massachusetts General Hospital, Boston, MA, USA
| | - Nathan Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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45
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Madhavan S, Bastarache L, Brown JS, Butte AJ, Dorr DA, Embi PJ, Friedman CP, Johnson KB, Moore JH, Kohane IS, Payne PRO, Tenenbaum JD, Weiner MG, Wilcox AB, Ohno-Machado L. Use of electronic health records to support a public health response to the COVID-19 pandemic in the United States: a perspective from 15 academic medical centers. J Am Med Inform Assoc 2021; 28:393-401. [PMID: 33260207 PMCID: PMC7665546 DOI: 10.1093/jamia/ocaa287] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 10/27/2020] [Accepted: 10/30/2020] [Indexed: 11/12/2022] Open
Abstract
Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies.
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Affiliation(s)
- Subha Madhavan
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jeffrey S Brown
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Atul J Butte
- University of California Health System (UC Health), University of California, San Francisco, California, USA
| | - David A Dorr
- Departments of Medical Informatics and Clinical Epidemiology and Medicine, Oregon Health and Science University, Portland, Oregon, USA
| | - Peter J Embi
- Indiana University School of Medicine, Regenstrief Institute, Inc, Indianapolis, Indiana, USA
| | - Charles P Friedman
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA
| | - Kevin B Johnson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jason H Moore
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Philip R O Payne
- Institute for Informatics, Washington University in St. Louis, School of Medicine, St. Louis, Missouri, USA
| | - Jessica D Tenenbaum
- North Carolina Department of Health and Human Services, Raleigh, North Carolina, USA
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA
| | - Mark G Weiner
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Adam B Wilcox
- Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Lucila Ohno-Machado
- Department of Biomedical Informatics, University of California San Diego Health, La Jolla, California, USA
- Division of Health Services Research & Development, VA San Diego Healthcare System, San Diego, California, USA
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Weber GM, Hong C, Palmer NP, Avillach P, Murphy SN, Gutiérrez-Sacristán A, Xia Z, Serret-Larmande A, Neuraz A, Omenn GS, Visweswaran S, Klann JG, South AM, Loh NHW, Cannataro M, Beaulieu-Jones BK, Bellazzi R, Agapito G, Alessiani M, Aronow BJ, Bell DS, Bellasi A, Benoit V, Beraghi M, Boeker M, Booth J, Bosari S, Bourgeois FT, Brown NW, Bucalo M, Chiovato L, Chiudinelli L, Dagliati A, Devkota B, DuVall SL, Follett RW, Ganslandt T, García Barrio N, Gradinger T, Griffier R, Hanauer DA, Holmes JH, Horki P, Huling KM, Issitt RW, Jouhet V, Keller MS, Kraska D, Liu M, Luo Y, Lynch KE, Malovini A, Mandl KD, Mao C, Maram A, Matheny ME, Maulhardt T, Mazzitelli M, Milano M, Moore JH, Morris JS, Morris M, Mowery DL, Naughton TP, Ngiam KY, Norman JB, Patel LP, Pedrera Jimenez M, Ramoni RB, Schriver ER, Scudeller L, Sebire NJ, Serrano Balazote P, Spiridou A, Tan AL, Tan BW, Tibollo V, Torti C, Trecarichi EM, Vitacca M, Zambelli A, Zucco C, Kohane IS, Cai T, Brat GA. International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study. medRxiv 2021:2020.12.16.20247684. [PMID: 33564777 PMCID: PMC7872369 DOI: 10.1101/2020.12.16.20247684] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Objectives To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions. Design Retrospective cohort study. Setting The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe. Participants Patients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2. Primary and secondary outcome measures Patients were categorized as "ever-severe" or "never-severe" using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction. Results Of 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites. Conclusions Laboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models.
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Affiliation(s)
- Griffin M Weber
- Harvard Medical School, Department of Biomedical Informatics
| | - Chuan Hong
- Harvard Medical School, Department of Biomedical Informatics
| | - Nathan P Palmer
- Harvard Medical School, Department of Biomedical Informatics
| | - Paul Avillach
- Harvard Medical School, Department of Biomedical Informatics
| | | | | | | | - Arnaud Serret-Larmande
- Ho pital Européen Georges Pompidou, Assistance Publique - Ho pitaux de Paris, Department of biomedical informatics
| | | | - Gilbert S Omenn
- University of Michigan, Dept of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - John Booth
- Great Ormond Street Hospital for Children
| | - Silvano Bosari
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico
| | | | | | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions)
| | | | | | | | | | | | | | - Thomas Ganslandt
- Ruprecht Karls University Heidelberg Faculty of Medicine Mannheim
| | | | - Tobias Gradinger
- Ruprecht Karls University Heidelberg Faculty of Medicine Mannheim
| | | | - David A Hanauer
- University of Michigan Institute for Healthcare Policy & Innovation
| | - John H Holmes
- University of Pennsylvania Perelman School of Medicine
| | | | | | | | | | - Mark S Keller
- Harvard Medical School, Department of Biomedical Informatics
| | | | - Molei Liu
- Harvard University T H Chan School of Public Health
| | | | | | | | - Kenneth D Mandl
- Boston Children's Hospital, Computational Health Informatics Program
| | | | | | | | | | | | | | - Jason H Moore
- University of Pennsylvania Perelman School of Medicine
| | | | | | | | | | | | - James B Norman
- Harvard Medical School, Department of Biomedical Informatics
| | | | | | | | | | | | | | | | | | - Amelia Lm Tan
- Harvard Medical School, Department of Biomedical Informatics
| | | | | | | | | | | | | | | | - Isaac S Kohane
- Harvard Medical School, Department of Biomedical Informatics
| | - Tianxi Cai
- Harvard Medical School, Department of Biomedical Informatics
| | - Gabriel A Brat
- Beth Israel Deaconess Medical Center, Surgery
- Harvard Medical School, Department of Biomedical Informatics
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Abstract
IMPORTANCE The Undiagnosed Diseases Network (UDN) is a national network that evaluates individual patients whose signs and symptoms have been refractory to diagnosis. Providing reliable estimates of admission outcomes may assist clinical evaluators to distinguish, prioritize, and accelerate admission to the UDN for patients with undiagnosed diseases. OBJECTIVE To develop computational models that effectively predict admission outcomes for applicants seeking UDN evaluation and to rank the applications based on the likelihood of patient admission to the UDN. DESIGN, SETTING, AND PARTICIPANTS This prognostic study included all applications submitted to the UDN from July 2014 to June 2019, with 1209 applications accepted and 1212 applications not accepted. The main inclusion criterion was an undiagnosed condition despite thorough evaluation by a health care professional; the main exclusion criteria were a diagnosis that explained the objective findings or a review of the records that suggested a diagnosis. A classifier was trained using information extracted from application forms, referral letters from health care professionals, and semantic similarity between referral letters and textual description of known mendelian disorders. The admission labels were provided by the case review committee of the UDN. In addition to retrospective analysis, the classifier was prospectively tested on another 288 applications that were not evaluated at the time of classifier development. MAIN OUTCOMES AND MEASURES The primary outcomes were whether a patient was accepted or not accepted to the UDN and application order ranked based on likelihood of admission. The performance of the classifier was assessed by comparing its predictions against the UDN admission outcomes and by measuring improvement in the mean processing time for accepted applications. RESULTS The best classifier obtained sensitivity of 0.843, specificity of 0.738, and area under the receiver operating characteristic curve of 0.844 for predicting admission outcomes among 1212 accepted and 1210 not accepted applications. In addition, the classifier can decrease the current mean (SD) UDN processing time for accepted applications from 3.29 (3.17) months to 1.05 (3.82) months (68% improvement) by ordering applications based on their likelihood of acceptance. CONCLUSIONS AND RELEVANCE A classification system was developed that may assist clinical evaluators to distinguish, prioritize, and accelerate admission to the UDN for patients with undiagnosed diseases. Accelerating the admission process may improve the diagnostic journeys for these patients and serve as a model for partial automation of triaging or referral for other resource-constrained applications. Such classification models make explicit some of the considerations that currently inform the use of whole-genome sequencing for undiagnosed disease and thereby invite a broader discussion in the clinical genetics community.
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Affiliation(s)
- Hadi Amiri
- Department of Biomedical Informatics, Harvard University, Boston, Massachusetts
- Department of Computer Science, University of Massachusetts, Lowell
| | - Isaac S. Kohane
- Department of Biomedical Informatics, Harvard University, Boston, Massachusetts
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48
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Liao KP, Sun J, Cai TA, Link N, Hong C, Huang J, Huffman JE, Gronsbell J, Zhang Y, Ho YL, Castro V, Gainer V, Murphy SN, O'Donnell CJ, Gaziano JM, Cho K, Szolovits P, Kohane IS, Yu S, Cai T. High-throughput multimodal automated phenotyping (MAP) with application to PheWAS. J Am Med Inform Assoc 2021; 26:1255-1262. [PMID: 31613361 DOI: 10.1093/jamia/ocz066] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 04/08/2019] [Accepted: 04/26/2019] [Indexed: 01/01/2023] Open
Abstract
OBJECTIVE Electronic health records linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. The objective of this study was to develop an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP). MATERIALS AND METHODS We developed a mapping method for automatically identifying relevant ICD and NLP concepts for a specific phenotype leveraging the Unified Medical Language System. Along with health care utilization, aggregated ICD and NLP counts were jointly analyzed by fitting an ensemble of latent mixture models. The multimodal automated phenotyping (MAP) algorithm yields a predicted probability of phenotype for each patient and a threshold for classifying participants with phenotype yes/no. The algorithm was validated using labeled data for 16 phenotypes from a biorepository and further tested in an independent cohort phenome-wide association studies (PheWAS) for 2 single nucleotide polymorphisms with known associations. RESULTS The MAP algorithm achieved higher or similar AUC and F-scores compared to the ICD code across all 16 phenotypes. The features assembled via the automated approach had comparable accuracy to those assembled via manual curation (AUCMAP 0.943, AUCmanual 0.941). The PheWAS results suggest that the MAP approach detected previously validated associations with higher power when compared to the standard PheWAS method based on ICD codes. CONCLUSION The MAP approach increased the accuracy of phenotype definition while maintaining scalability, thereby facilitating use in studies requiring large-scale phenotyping, such as PheWAS.
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Affiliation(s)
- Katherine P Liao
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Jiehuan Sun
- Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tianrun A Cai
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Nicholas Link
- Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jie Huang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | | | - Yichi Zhang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,University of Rhode Island, Kingston, RI, USA
| | - Yuk-Lam Ho
- Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA
| | | | | | - Shawn N Murphy
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Partners Healthcare Systems, Summerville, MA, USA.,Massachusetts General Hospital, Boston, MA, USA
| | - Christopher J O'Donnell
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, MA, USA.,Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - J Michael Gaziano
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Kelly Cho
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Peter Szolovits
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sheng Yu
- Center for Statistical Science, Tsinghua University, Beijing, China.,Department of Industrial Engineering, Tsinghua University, Beijing, China.,Institute for Data Science, Tsinghua University, Beijing, China
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Division of Data Sciences, VA Boston Healthcare System, Boston, MA, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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49
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Le TT, Gutiérrez-Sacristán A, Son J, Hong C, South AM, Beaulieu-Jones BK, Loh NHW, Luo Y, Morris M, Ngiam KY, Patel LP, Samayamuthu MJ, Schriver E, Tan AL, Moore J, Cai T, Omenn GS, Avillach P, Kohane IS, Visweswaran S, Mowery DL, Xia Z. Multinational Prevalence of Neurological Phenotypes in Patients Hospitalized with COVID-19. medRxiv 2021. [PMID: 33655281 PMCID: PMC7924306 DOI: 10.1101/2021.01.27.21249817] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
OBJECTIVE: Neurological complications can worsen outcomes in COVID-19. We defined the prevalence of a wide range of neurological conditions among patients hospitalized with COVID-19 in geographically diverse multinational populations. METHODS: Using electronic health record (EHR) data from 348 participating hospitals across 6 countries and 3 continents between January and September 2020, we performed a cross-sectional study of hospitalized adult and pediatric patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test, both with and without severe COVID-19. We assessed the frequency of each disease category and 3-character International Classification of Disease (ICD) code of neurological diseases by countries, sites, time before and after admission for COVID-19, and COVID-19 severity. RESULTS: Among the 35,177 hospitalized patients with SARS-CoV-2 infection, there was increased prevalence of disorders of consciousness (5.8%, 95% confidence interval [CI]: 3.7%−7.8%, pFDR<.001) and unspecified disorders of the brain (8.1%, 95%CI: 5.7%−10.5%, pFDR<.001), compared to pre-admission prevalence. During hospitalization, patients who experienced severe COVID-19 status had 22% (95%CI: 19%−25%) increase in the relative risk (RR) of disorders of consciousness, 24% (95%CI: 13%−35%) increase in other cerebrovascular diseases, 34% (95%CI: 20%−50%) increase in nontraumatic intracranial hemorrhage, 37% (95%CI: 17%−60%) increase in encephalitis and/or myelitis, and 72% (95%CI: 67%−77%) increase in myopathy compared to those who never experienced severe disease. INTERPRETATION: Using an international network and common EHR data elements, we highlight an increase in the prevalence of central and peripheral neurological phenotypes in patients hospitalized with SARS-CoV-2 infection, particularly among those with severe disease.
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Affiliation(s)
- Trang T Le
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Jiyeon Son
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chuan Hong
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andrew M South
- Department of Pediatrics, Wake Forest School of Medicine, Winston Salem, NC, USA
| | | | - Ne Hooi Will Loh
- Department of Critical Care, National University Health Systems, Singapore
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kee Yuan Ngiam
- Department of Surgery, National University Health Systems, Singapore
| | - Lav P Patel
- Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA
| | | | - Emily Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, PA, USA
| | - Amelia Lm Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Jason Moore
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, PA, USA
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50
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Diao JA, Wu GJ, Taylor HA, Tucker JK, Powe NR, Kohane IS, Manrai AK. Clinical Implications of Removing Race From Estimates of Kidney Function. JAMA 2021; 325:184-186. [PMID: 33263721 PMCID: PMC7711563 DOI: 10.1001/jama.2020.22124] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 10/22/2020] [Indexed: 12/22/2022]
Affiliation(s)
- James A. Diao
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
| | - Gloria J. Wu
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
| | - Herman A. Taylor
- Cardiovascular Research Institute, Morehouse Medical School, Atlanta, Georgia
| | - John K. Tucker
- Division of Renal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Neil R. Powe
- Department of Medicine, Zuckerberg San Francisco General Hospital, San Francisco, California
| | - Isaac S. Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Arjun K. Manrai
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
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