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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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2
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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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3
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Hong C, Zhang HG, L'Yi S, Weber G, Avillach P, Tan BWQ, Gutiérrez-Sacristán A, Bonzel CL, Palmer NP, Malovini A, Tibollo V, Luo Y, Hutch MR, Liu M, Bourgeois F, Bellazzi R, Chiovato L, Sanz Vidorreta FJ, Le TT, Wang X, Yuan W, Neuraz A, Benoit V, Moal B, Morris M, Hanauer DA, Maidlow S, Wagholikar K, Murphy S, Estiri H, Makoudjou A, Tippmann P, Klann J, Follett RW, Gehlenborg N, Omenn GS, Xia Z, Dagliati A, Visweswaran S, Patel LP, Mowery DL, Schriver ER, Samayamuthu MJ, Kavuluru R, Lozano-Zahonero S, Zöller D, Tan ALM, Tan BWL, Ngiam KY, Holmes JH, Schubert P, Cho K, Ho YL, Beaulieu-Jones BK, Pedrera-Jiménez M, García-Barrio N, Serrano-Balazote P, Kohane I, South A, Brat GA, Cai T. Changes in laboratory value improvement and mortality rates over the course of the pandemic: an international retrospective cohort study of hospitalised patients infected with SARS-CoV-2. BMJ Open 2022; 12:e057725. [PMID: 35738646 PMCID: PMC9226470 DOI: 10.1136/bmjopen-2021-057725] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 06/12/2022] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVE To assess changes in international mortality rates and laboratory recovery rates during hospitalisation for patients hospitalised with SARS-CoV-2 between the first wave (1 March to 30 June 2020) and the second wave (1 July 2020 to 31 January 2021) of the COVID-19 pandemic. DESIGN, SETTING AND PARTICIPANTS This is a retrospective cohort study of 83 178 hospitalised patients admitted between 7 days before or 14 days after PCR-confirmed SARS-CoV-2 infection within the Consortium for Clinical Characterization of COVID-19 by Electronic Health Record, an international multihealthcare system collaborative of 288 hospitals in the USA and Europe. The laboratory recovery rates and mortality rates over time were compared between the two waves of the pandemic. PRIMARY AND SECONDARY OUTCOME MEASURES The primary outcome was all-cause mortality rate within 28 days after hospitalisation stratified by predicted low, medium and high mortality risk at baseline. The secondary outcome was the average rate of change in laboratory values during the first week of hospitalisation. RESULTS Baseline Charlson Comorbidity Index and laboratory values at admission were not significantly different between the first and second waves. The improvement in laboratory values over time was faster in the second wave compared with the first. The average C reactive protein rate of change was -4.72 mg/dL vs -4.14 mg/dL per day (p=0.05). The mortality rates within each risk category significantly decreased over time, with the most substantial decrease in the high-risk group (42.3% in March-April 2020 vs 30.8% in November 2020 to January 2021, p<0.001) and a moderate decrease in the intermediate-risk group (21.5% in March-April 2020 vs 14.3% in November 2020 to January 2021, p<0.001). CONCLUSIONS Admission profiles of patients hospitalised with SARS-CoV-2 infection did not differ greatly between the first and second waves of the pandemic, but there were notable differences in laboratory improvement rates during hospitalisation. Mortality risks among patients with similar risk profiles decreased over the course of the pandemic. The improvement in laboratory values and mortality risk was consistent across multiple countries.
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Affiliation(s)
- Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Griffin Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Bryce W Q Tan
- Department of Medicine, National University Hospital, Singapore
| | | | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Lombardia, Italy
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Lombardia, Italy
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Evanston, Illinois, USA
| | - Meghan R Hutch
- Department of Preventive Medicine, Northwestern University, Evanston, Illinois, USA
| | - Molei Liu
- Department of Biostatistics, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
| | - Florence Bourgeois
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, 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, Lombardia, Italy
| | | | - Trang T Le
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Antoine Neuraz
- Department of Biomedical Informatics, Hopital Universitaire Necker-Enfants Malades, Paris, Île-de-France, France
| | - Vincent Benoit
- IT department, Innovation & Data, APHP Greater Paris University Hospital, Paris, France
| | - Bertrand Moal
- IAM unit, Bordeaux University Hospital, Bordeaux, France
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Sarah Maidlow
- MICHR Informatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Kavishwar Wagholikar
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Shawn Murphy
- Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, University of Freiburg Faculty of Medicine, Freiburg, Baden-Württemberg, Germany
| | - Patric Tippmann
- Institute of Medical Biometry and Statistics, Medical Center-University of Freiburg, Freiburg, Baden-Württemberg, Germany
| | - Jeffery Klann
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Robert W Follett
- Department of Medicine, David Geffen School of Medicine, Los Angeles, California, USA
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Gilbert S Omenn
- Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Arianna Dagliati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Kansas, USA
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Emily R Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | | | - Ramakanth Kavuluru
- Institute for Biomedical Informatics, University of Kentucky, Lexington, Kentucky, USA
| | - Sara Lozano-Zahonero
- Institute of Medical Biometry and Statistics, University of Freiburg Faculty of Medicine, Freiburg, Baden-Württemberg, Germany
| | - Daniela Zöller
- Institute of Medical Biometry and Statistics, University of Freiburg Faculty of Medicine, Freiburg, Baden-Württemberg, Germany
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Byorn W L Tan
- Department of Medicine, National University Hospital, Singapore
| | - Kee Yuan Ngiam
- Department of Surgery, National University Hospital, Singapore
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | | | - Miguel Pedrera-Jiménez
- Health Informatics, Hospital Universitario 12 de Octubre, Madrid, Comunidad de Madrid, Spain
| | - Noelia García-Barrio
- Health Informatics, Hospital Universitario 12 de Octubre, Madrid, Comunidad de Madrid, Spain
| | - Pablo Serrano-Balazote
- Health Informatics, Hospital Universitario 12 de Octubre, Madrid, Comunidad de Madrid, Spain
| | - Isaac Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Andrew South
- Department of Pediatrics, Section of Nephrology, Wake Forest University, Winston Salem, North Carolina, USA
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - T Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
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4
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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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5
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Shapiro DJ, Palmer NP, Bourgeois FT. Factors Associated With Corticosteroid Treatment for Pediatric Acute Respiratory Tract Infections. J Pediatric Infect Dis Soc 2021; 10:1101-1104. [PMID: 34468742 DOI: 10.1093/jpids/piab082] [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: 03/04/2021] [Accepted: 08/24/2021] [Indexed: 11/14/2022]
Abstract
Systemic corticosteroids are not recommended to treat children with acute respiratory tract infections (ARTIs). Using data from a national commercial health care company, we found that corticosteroid treatment occurred in 3.2% of ARTI encounters. The adjusted odds of corticosteroid treatment were highest for bronchitis/bronchiolitis, in emergency departments, and in the South.
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Affiliation(s)
- Daniel J Shapiro
- Division of Emergency Medicine, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Florence T Bourgeois
- Division of Emergency Medicine, Boston Children's Hospital, Boston, Massachusetts, USA.,Departments of Pediatrics and Emergency Medicine, Harvard Medical School, Boston, Massachusetts, USA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
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6
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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
| | -
- 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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7
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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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8
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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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9
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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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10
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Brat GA, Weber GM, Gehlenborg N, Avillach P, Palmer NP, Chiovato L, Cimino J, Waitman LR, Omenn GS, Malovini A, Moore JH, Beaulieu-Jones BK, Tibollo V, Murphy SN, Yi SL, Keller MS, Bellazzi R, Hanauer DA, Serret-Larmande A, Gutierrez-Sacristan A, Holmes JJ, Bell DS, Mandl KD, Follett RW, Klann JG, Murad DA, Scudeller L, Bucalo M, Kirchoff K, Craig J, Obeid J, Jouhet V, Griffier R, Cossin S, Moal B, Patel LP, Bellasi A, Prokosch HU, Kraska D, Sliz P, Tan ALM, Ngiam KY, Zambelli A, Mowery DL, Schiver E, Devkota B, Bradford RL, Daniar M, Daniel C, Benoit V, Bey R, Paris N, Serre P, Orlova N, Dubiel J, Hilka M, Jannot AS, Breant S, Leblanc J, Griffon N, Burgun A, Bernaux M, Sandrin A, Salamanca E, Cormont S, Ganslandt T, Gradinger T, Champ J, Boeker M, Martel P, Esteve L, Gramfort A, Grisel O, Leprovost D, Moreau T, Varoquaux G, Vie JJ, Wassermann D, Mensch A, Caucheteux C, Haverkamp C, Lemaitre G, Bosari S, Krantz ID, South A, Cai T, Kohane IS. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium. NPJ Digit Med 2020. [PMID: 32864472 DOI: 10.1101/2020.04.13.20059691v5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.
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Affiliation(s)
- Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Luca Chiovato
- IRCCS ICS Maugeri, Pavia, Italy.,Department of Internal Medicine and Medical Therapy, University of Pavia, Pavia, Italy
| | | | - Lemuel R Waitman
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS USA
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
| | | | - Jason H Moore
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA.,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
| | - Sehi L' Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Mark S Keller
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Riccardo Bellazzi
- IRCCS ICS Maugeri, Pavia, Italy.,Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - David A Hanauer
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI USA
| | | | | | - John J Holmes
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA.,Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI USA
| | - Douglas S Bell
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA USA
| | - Robert W Follett
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Jeffrey G Klann
- Department of Medicine, Massachusetts General Hospital, Boston, MA USA
| | - Douglas A Murad
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Luigia Scudeller
- Scientific Direction, IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, Milano, Italy
| | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | - Katie Kirchoff
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC USA
| | - Jean Craig
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC USA
| | - Jihad Obeid
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC USA
| | | | | | | | | | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS USA
| | - Antonio Bellasi
- UOC Ricerca, Innovazione e Brand Reputation, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Hans U Prokosch
- Department of Medical Informatics, University of Erlangen-Nürnberg, Erlangen, Germany
| | - Detlef Kraska
- Center for Medical Information and Communication Technology, University Hospital Erlangen, Erlangen, Germany
| | - Piotr Sliz
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA USA
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Kee Yuan Ngiam
- National University Health Systems, Singapore, Singapore
| | - Alberto Zambelli
- Department of Oncology, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Danielle L Mowery
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA.,Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI USA
| | - Emily Schiver
- Penn Medicine, Data Analytics Center, Philadelphia, PA USA
| | - Batsal Devkota
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA USA
| | - Robert L Bradford
- North Carolina Translational and Clinical Sciences (NC TraCS) Institute, UNC Chapel Hill, Chapel Hill, NC USA
| | - Mohamad Daniar
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA USA
| | - Christel Daniel
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Vincent Benoit
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Romain Bey
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Nicolas Paris
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Patricia Serre
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Nina Orlova
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Julien Dubiel
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Martin Hilka
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Anne Sophie Jannot
- Department of Biomedical Informatics, HEGP, APHP Greater Paris University Hospital, Paris, France
| | - Stephane Breant
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Judith Leblanc
- Clinical Research Unit, Saint Antoine Hospital, APHP Greater Paris University Hospital, Paris, France
| | - Nicolas Griffon
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Anita Burgun
- Department of Biomedical Informatics, HEGP, APHP Greater Paris University Hospital, Paris, France
| | - Melodie Bernaux
- Strategy and Transformation Department, APHP Greater Paris University Hospital, Paris, France
| | - Arnaud Sandrin
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Elisa Salamanca
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Sylvie Cormont
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Thomas Ganslandt
- Heinrich-Lanz-Center for Digital Health, University Medicine Mannheim, Heidelberg University, Mannheim, Germany
| | - Tobias Gradinger
- Heinrich-Lanz-Center for Digital Health, University Medicine Mannheim, Heidelberg University, Mannheim, Germany
| | - Julien Champ
- INRIA Sophia-Antipolis-ZENITH Team, LIRMM, Montpellier, France
| | - Martin Boeker
- Institute of Medical Biometry and Statistics, Medical Center, University of Freiburg, Freiburg im Breisgau, Germany
| | - Patricia Martel
- Clinical Research Unit, Paris Saclay, APHP Greater Paris University Hospital, Paris, France
| | - Loic Esteve
- SED/SIERRA, Inria Centre de Paris, Paris, France
| | | | | | | | | | | | | | | | | | | | - Christian Haverkamp
- Institute of Digitalization in Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg im Breisgau, Germany
| | | | - Silvano Bosari
- IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, Milano, Italy
| | - Ian D Krantz
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA USA
| | - Andrew South
- Brenner Children's Hospital, Wake Forest School of Medicine, Winston-Salem, NC USA
| | - Tianxi Cai
- 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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11
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Brat GA, Weber GM, Gehlenborg N, Avillach P, Palmer NP, Chiovato L, Cimino J, Waitman LR, Omenn GS, Malovini A, Moore JH, Beaulieu-Jones BK, Tibollo V, Murphy SN, Yi SL, Keller MS, Bellazzi R, Hanauer DA, Serret-Larmande A, Gutierrez-Sacristan A, Holmes JJ, Bell DS, Mandl KD, Follett RW, Klann JG, Murad DA, Scudeller L, Bucalo M, Kirchoff K, Craig J, Obeid J, Jouhet V, Griffier R, Cossin S, Moal B, Patel LP, Bellasi A, Prokosch HU, Kraska D, Sliz P, Tan ALM, Ngiam KY, Zambelli A, Mowery DL, Schiver E, Devkota B, Bradford RL, Daniar M, Daniel C, Benoit V, Bey R, Paris N, Serre P, Orlova N, Dubiel J, Hilka M, Jannot AS, Breant S, Leblanc J, Griffon N, Burgun A, Bernaux M, Sandrin A, Salamanca E, Cormont S, Ganslandt T, Gradinger T, Champ J, Boeker M, Martel P, Esteve L, Gramfort A, Grisel O, Leprovost D, Moreau T, Varoquaux G, Vie JJ, Wassermann D, Mensch A, Caucheteux C, Haverkamp C, Lemaitre G, Bosari S, Krantz ID, South A, Cai T, Kohane IS. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium. NPJ Digit Med 2020; 3:109. [PMID: 32864472 PMCID: PMC7438496 DOI: 10.1038/s41746-020-00308-0] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [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: 04/15/2020] [Accepted: 06/16/2020] [Indexed: 12/18/2022] Open
Abstract
We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.
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Affiliation(s)
- Gabriel A. Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Griffin M. Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Nathan P. Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Luca Chiovato
- IRCCS ICS Maugeri, Pavia, Italy
- Department of Internal Medicine and Medical Therapy, University of Pavia, Pavia, Italy
| | | | - Lemuel R. Waitman
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS USA
| | - Gilbert S. Omenn
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
| | | | - Jason H. Moore
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA
- 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
| | - Sehi L’ Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Mark S. Keller
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Riccardo Bellazzi
- IRCCS ICS Maugeri, Pavia, Italy
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - David A. Hanauer
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI USA
| | | | | | - John J. Holmes
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI USA
| | - Douglas S. Bell
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA USA
| | - Robert W. Follett
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Jeffrey G. Klann
- Department of Medicine, Massachusetts General Hospital, Boston, MA USA
| | - Douglas A. Murad
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA USA
| | - Luigia Scudeller
- Scientific Direction, IRCCS Ca’ Granda Ospedale Maggiore Policlinico di Milano, Milano, Italy
| | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | - Katie Kirchoff
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC USA
| | - Jean Craig
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC USA
| | - Jihad Obeid
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC USA
| | | | | | | | | | - Lav P. Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS USA
| | - Antonio Bellasi
- UOC Ricerca, Innovazione e Brand Reputation, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Hans U. Prokosch
- Department of Medical Informatics, University of Erlangen-Nürnberg, Erlangen, Germany
| | - Detlef Kraska
- Center for Medical Information and Communication Technology, University Hospital Erlangen, Erlangen, Germany
| | - Piotr Sliz
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA USA
| | - Amelia L. M. Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Kee Yuan Ngiam
- National University Health Systems, Singapore, Singapore
| | - Alberto Zambelli
- Department of Oncology, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Danielle L. Mowery
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI USA
| | - Emily Schiver
- Penn Medicine, Data Analytics Center, Philadelphia, PA USA
| | - Batsal Devkota
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA USA
| | - Robert L. Bradford
- North Carolina Translational and Clinical Sciences (NC TraCS) Institute, UNC Chapel Hill, Chapel Hill, NC USA
| | - Mohamad Daniar
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA USA
| | - Christel Daniel
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Vincent Benoit
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Romain Bey
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Nicolas Paris
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Patricia Serre
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Nina Orlova
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Julien Dubiel
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Martin Hilka
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Anne Sophie Jannot
- Department of Biomedical Informatics, HEGP, APHP Greater Paris University Hospital, Paris, France
| | - Stephane Breant
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Judith Leblanc
- Clinical Research Unit, Saint Antoine Hospital, APHP Greater Paris University Hospital, Paris, France
| | - Nicolas Griffon
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Anita Burgun
- Department of Biomedical Informatics, HEGP, APHP Greater Paris University Hospital, Paris, France
| | - Melodie Bernaux
- Strategy and Transformation Department, APHP Greater Paris University Hospital, Paris, France
| | - Arnaud Sandrin
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Elisa Salamanca
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Sylvie Cormont
- WIND Department APHP Greater Paris University Hospital, Paris, France
| | - Thomas Ganslandt
- Heinrich-Lanz-Center for Digital Health, University Medicine Mannheim, Heidelberg University, Mannheim, Germany
| | - Tobias Gradinger
- Heinrich-Lanz-Center for Digital Health, University Medicine Mannheim, Heidelberg University, Mannheim, Germany
| | - Julien Champ
- INRIA Sophia-Antipolis—ZENITH Team, LIRMM, Montpellier, France
| | - Martin Boeker
- Institute of Medical Biometry and Statistics, Medical Center, University of Freiburg, Freiburg im Breisgau, Germany
| | - Patricia Martel
- Clinical Research Unit, Paris Saclay, APHP Greater Paris University Hospital, Paris, France
| | - Loic Esteve
- SED/SIERRA, Inria Centre de Paris, Paris, France
| | | | | | | | | | | | | | | | | | | | - Christian Haverkamp
- Institute of Digitalization in Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg im Breisgau, Germany
| | | | - Silvano Bosari
- IRCCS Ca’ Granda Ospedale Maggiore Policlinico di Milano, Milano, Italy
| | - Ian D. Krantz
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA USA
| | - Andrew South
- Brenner Children’s Hospital, Wake Forest School of Medicine, Winston-Salem, NC USA
| | - Tianxi Cai
- 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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12
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Palmer NP, Silvester JA, Lee JJ, Beam AL, Fried I, Valtchinov VI, Rahimov F, Kong SW, Ghodoussipour S, Hood HC, Bousvaros A, Grand RJ, Kunkel LM, Kohane IS. Concordance between gene expression in peripheral whole blood and colonic tissue in children with inflammatory bowel disease. PLoS One 2019; 14:e0222952. [PMID: 31618209 PMCID: PMC6795427 DOI: 10.1371/journal.pone.0222952] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 09/10/2019] [Indexed: 12/19/2022] Open
Abstract
Background Presenting features of inflammatory bowel disease (IBD) are non-specific. We hypothesized that mRNA profiles could (1) identify genes and pathways involved in disease pathogenesis; (2) identify a molecular signature that differentiates IBD from other conditions; (3) provide insight into systemic and colon-specific dysregulation through study of the concordance of the gene expression. Methods Children (8–18 years) were prospectively recruited at the time of diagnostic colonoscopy for possible IBD. We used transcriptome-wide mRNA profiling to study gene expression in colon biopsies and paired whole blood samples. Using blood mRNA measurements, we fit a regression model for disease state prediction that was validated in an independent test set of adult subjects (GSE3365). Results Ninety-eight children were recruited [39 Crohn’s disease, 18 ulcerative colitis, 2 IBDU, 39 non-IBD]. There were 1,118 significantly differentially (IBD vs non-IBD) expressed genes in colon tissue, and 880 in blood. The direction of relative change in expression was concordant for 106/112 genes differentially expressed in both tissue types. The regression model from the blood mRNA measurements distinguished IBD vs non-IBD disease status in the independent test set with 80% accuracy using only 6 genes. The overlap of 5 immune and metabolic pathways in the two tissue types was significant (p<0.001). Conclusions Blood and colon tissue from patients with IBD share a common transcriptional profile dominated by immune and metabolic pathways. Our results suggest that peripheral blood expression levels of as few as 6 genes (IL7R, UBB, TXNIP, S100A8, ALAS2, and SLC2A3) may distinguish patients with IBD from non-IBD.
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Affiliation(s)
- Nathan P. Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jocelyn A. Silvester
- Division of Gastroenterology and Nutrition, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail:
| | - Jessica J. Lee
- Division of Gastroenterology and Nutrition, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Andrew L. Beam
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Inbar Fried
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Vladimir I. Valtchinov
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
- Center for Evidence Based Imaging, Brigham and Women’s Hospital, Harvard Medical School, Massachusetts, United States of America
| | - Fedik Rahimov
- Division of Genetics and Genomics, Boston Children’s Hospital, Departments of Genetics and Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Sek Won Kong
- Computational Health Informatics Program, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Saum Ghodoussipour
- Division of Gastroenterology and Nutrition, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Helen C. Hood
- Division of Gastroenterology and Nutrition, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Athos Bousvaros
- Division of Gastroenterology and Nutrition, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Richard J. Grand
- Division of Gastroenterology and Nutrition, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Louis M. Kunkel
- Division of Genetics and Genomics, Boston Children’s Hospital, Departments of Genetics and Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Isaac S. Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
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13
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Fried I, Beam AL, Kohane IS, Palmer NP. Utilization, Cost, and Outcome of Branded vs Compounded 17-Alpha Hydroxyprogesterone Caproate in Prevention of Preterm Birth. JAMA Intern Med 2017; 177:1689-1690. [PMID: 28973537 PMCID: PMC5820707 DOI: 10.1001/jamainternmed.2017.5017] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [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: 11/14/2022]
Abstract
This study compares the utilization, cost, and outcome data from an insurance claims database to determine the difference in treatment efficacy between the branded and compounded versions of 17-alpha hydroxyprogesterone caproate.
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Affiliation(s)
- Inbar Fried
- Department of Biomedical Informatics, Harvard Medical School, Boston Massachusetts
| | - Andrew L Beam
- Department of Biomedical Informatics, Harvard Medical School, Boston Massachusetts
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston Massachusetts
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston Massachusetts
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14
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Lyons TW, Olson KL, Palmer NP, Horwitz R, Mandl KD, Fine AM. Patients Visiting Multiple Emergency Departments: Patterns, Costs, and Risk Factors. Acad Emerg Med 2017; 24:1349-1357. [PMID: 28861915 DOI: 10.1111/acem.13304] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Revised: 08/23/2017] [Accepted: 08/27/2017] [Indexed: 11/29/2022]
Abstract
OBJECTIVES We sought to characterize the population of patients seeking care at multiple emergency departments (EDs) and to quantify the proportion of all ED visits and costs accounted for by these patients. METHODS We performed a retrospective, cohort study of deidentified insurance claims for privately insured patients with one of more ED visits between 2010 and 2016. We measured the number of EDs visited by each patient and determined the overall proportion of all ED visits and ED costs accounted for by patients who visit multiple EDs. We identified factors associated with visiting multiple EDs. RESULTS A total of 8,651,716 patients made 16,390,676 ED visits over the study period, accounting for $26,102,831,740 in ED costs. A significant minority (20.5%) of patients visited more than one ED over the study period. However, these patients accounted for a disproportionate amount of all ED visits (41.4%) and all ED costs (39.2%). A small proportion (0.4%) of patients visited five or more EDs but accounted for 2.8% of ED visits and costs. Among patients with two ED visits within 30 days, 32% were to different EDs. Having at least one ED visit for mental health or substance abuse-related diagnosis was associated with increased odds of visiting multiple EDs. CONCLUSIONS A substantial minority of patients visit multiple EDs, but account for a disproportionate burden of overall ED utilization and costs. Future work should evaluate the impact of visiting multiple EDs on care utilization and outcomes and explore systems for improving access to patient records across care centers.
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Affiliation(s)
- Todd W. Lyons
- Computational Health Informatics Program; Boston Children's Hospital; Boston MA
- Division of Emergency Medicine; Boston Children's Hospital; Boston MA
| | - Karen L. Olson
- Computational Health Informatics Program; Boston Children's Hospital; Boston MA
- Division of Emergency Medicine; Boston Children's Hospital; Boston MA
| | - Nathan P. Palmer
- Department of Biomedical Informatics; Harvard Medical School; Boston MA
| | - Reed Horwitz
- Computational Health Informatics Program; Boston Children's Hospital; Boston MA
| | - Kenneth D. Mandl
- Computational Health Informatics Program; Boston Children's Hospital; Boston MA
- Division of Emergency Medicine; Boston Children's Hospital; Boston MA
- Department of Biomedical Informatics; Harvard Medical School; Boston MA
| | - Andrew M. Fine
- Division of Emergency Medicine; Boston Children's Hospital; Boston MA
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15
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Homer ML, Palmer NP, Fox KP, Armstrong J, Mandl KD. Predicting Falls in People Aged 65 Years and Older from Insurance Claims. Am J Med 2017; 130:744.e17-744.e23. [PMID: 28111165 PMCID: PMC5441951 DOI: 10.1016/j.amjmed.2017.01.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [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: 09/04/2016] [Revised: 12/30/2016] [Accepted: 01/02/2017] [Indexed: 11/25/2022]
Abstract
BACKGROUND Accidental falls among people aged 65 years and older caused approximately 2,700,000 injuries, 27,000 deaths, and cost more than 34 billion dollars in the US annually in recent years. Here, we derive and validate a predictive model for falls based on a retrospective cohort of those 65 years and older. METHODS Insurance claims from a 1-year observational period were used to predict a fall-related claim in the following 2 years. The predictive model takes into account a person's age, sex, prescriptions, and diagnoses. Through random assignment, half of the people had their claims used to derive the model, while the remaining people had their claims used to validate the model. RESULTS Of 120,881 individuals with Aetna health insurance coverage, 12,431 (10.3%) members fell. During validation, people were risk stratified across 20 levels, where those in the highest risk stratum had 10.5 times the risk as those in the lowest stratum (33.1% vs 3.1%). CONCLUSIONS Using only insurance claims, individuals in this large cohort at high risk of falls could be readily identified up to 2 years in advance. Although external validation is needed, the findings support the use of the model to better target interventions.
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Affiliation(s)
- Mark L Homer
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Mass; Department of Biomedical Informatics, Harvard Medical School, Boston, Mass
| | - Nathan P Palmer
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Mass; Department of Biomedical Informatics, Harvard Medical School, Boston, Mass
| | | | | | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Mass; Department of Biomedical Informatics, Harvard Medical School, Boston, Mass.
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16
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Nazeen S, Palmer NP, Berger B, Kohane IS. Integrative analysis of genetic data sets reveals a shared innate immune component in autism spectrum disorder and its co-morbidities. Genome Biol 2016; 17:228. [PMID: 27842596 PMCID: PMC5108086 DOI: 10.1186/s13059-016-1084-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2016] [Accepted: 10/12/2016] [Indexed: 12/22/2022] Open
Abstract
Background Autism spectrum disorder (ASD) is a common neurodevelopmental disorder that tends to co-occur with other diseases, including asthma, inflammatory bowel disease, infections, cerebral palsy, dilated cardiomyopathy, muscular dystrophy, and schizophrenia. However, the molecular basis of this co-occurrence, and whether it is due to a shared component that influences both pathophysiology and environmental triggering of illness, has not been elucidated. To address this, we deploy a three-tiered transcriptomic meta-analysis that functions at the gene, pathway, and disease levels across ASD and its co-morbidities. Results Our analysis reveals a novel shared innate immune component between ASD and all but three of its co-morbidities that were examined. In particular, we find that the Toll-like receptor signaling and the chemokine signaling pathways, which are key pathways in the innate immune response, have the highest shared statistical significance. Moreover, the disease genes that overlap these two innate immunity pathways can be used to classify the cases of ASD and its co-morbidities vs. controls with at least 70 % accuracy. Conclusions This finding suggests that a neuropsychiatric condition and the majority of its non-brain-related co-morbidities share a dysregulated signal that serves as not only a common genetic basis for the diseases but also as a link to environmental triggers. It also raises the possibility that treatment and/or prophylaxis used for disorders of innate immunity may be successfully used for ASD patients with immune-related phenotypes. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1084-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sumaiya Nazeen
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, 25 Shattuck Street, Boston, 02115, MA, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA. .,Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA.
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, 25 Shattuck Street, Boston, 02115, MA, USA.
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17
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Homer ML, Palmer NP, Bodenreider O, Cami A, Chadwick L, Mandl KD. The Drug Data to Knowledge Pipeline: Large-Scale Claims Data Classification for Pharmacologic Insight. AMIA Jt Summits Transl Sci Proc 2016; 2016:105-11. [PMID: 27570659 PMCID: PMC5001754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
In biomedical informatics, assigning drug codes to categories is a common step in the analysis pipeline. Unfortunately, incomplete mappings are the norm rather than the exception with coverage values less than 85% not uncommon. Here, we perform this linking task on a nationwide insurance claims database with over 13 million members who were dispensed, according to National Drug Codes (NDCs), over 50,000 unique product forms of medication. The chosen approach employs Cerner Multum's VantageRx and the U.S. National Library of Medicine's RxMix. As a result, 94.0% of the NDCs were successfully mapped to categories used by common drug terminologies, e.g., Anatomical Therapeutic Chemical (ATC). Implemented as an SQL database and scripts, the approach is generic and can be setup for a new data set in a few hours. Thus, the method is a viable option for large-scale drug classification.
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Affiliation(s)
- Mark L. Homer
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA;,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Nathan P. Palmer
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA;,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Olivier Bodenreider
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Aurel Cami
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA;,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Laura Chadwick
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA;,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA;,MCPHS University, Boston, MA, USA
| | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA;,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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18
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Prilutsky D, Palmer NP, Smedemark-Margulies N, Schlaeger TM, Margulies DM, Kohane IS. iPSC-derived neurons as a higher-throughput readout for autism: promises and pitfalls. Trends Mol Med 2013; 20:91-104. [PMID: 24374161 DOI: 10.1016/j.molmed.2013.11.004] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [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: 06/13/2013] [Revised: 11/20/2013] [Accepted: 11/21/2013] [Indexed: 12/13/2022]
Abstract
The elucidation of disease etiologies and establishment of robust, scalable, high-throughput screening assays for autism spectrum disorders (ASDs) have been impeded by both inaccessibility of disease-relevant neuronal tissue and the genetic heterogeneity of the disorder. Neuronal cells derived from induced pluripotent stem cells (iPSCs) from autism patients may circumvent these obstacles and serve as relevant cell models. To date, derived cells are characterized and screened by assessing their neuronal phenotypes. These characterizations are often etiology-specific or lack reproducibility and stability. In this review, we present an overview of efforts to study iPSC-derived neurons as a model for autism, and we explore the plausibility of gene expression profiling as a reproducible and stable disease marker.
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Affiliation(s)
- Daria Prilutsky
- Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Nathan P Palmer
- Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | | | | | - David M Margulies
- Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Divisions of Genetics and Developmental Medicine, Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Isaac S Kohane
- Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.
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19
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Zimolzak AJ, Spettell CM, Fernandes J, Fusaro VA, Palmer NP, Saria S, Kohane IS, Jonikas MA, Mandl KD. Early detection of poor adherers to statins: applying individualized surveillance to pay for performance. PLoS One 2013; 8:e79611. [PMID: 24223977 PMCID: PMC3817130 DOI: 10.1371/journal.pone.0079611] [Citation(s) in RCA: 11] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2013] [Accepted: 09/24/2013] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Medication nonadherence costs $300 billion annually in the US. Medicare Advantage plans have a financial incentive to increase medication adherence among members because the Centers for Medicare and Medicaid Services (CMS) now awards substantive bonus payments to such plans, based in part on population adherence to chronic medications. We sought to build an individualized surveillance model that detects early which beneficiaries will fall below the CMS adherence threshold. METHODS This was a retrospective study of over 210,000 beneficiaries initiating statins, in a database of private insurance claims, from 2008-2011. A logistic regression model was constructed to use statin adherence from initiation to day 90 to predict beneficiaries who would not meet the CMS measure of proportion of days covered 0.8 or above, from day 91 to 365. The model controlled for 15 additional characteristics. In a sensitivity analysis, we varied the number of days of adherence data used for prediction. RESULTS Lower adherence in the first 90 days was the strongest predictor of one-year nonadherence, with an odds ratio of 25.0 (95% confidence interval 23.7-26.5) for poor adherence at one year. The model had an area under the receiver operating characteristic curve of 0.80. Sensitivity analysis revealed that predictions of comparable accuracy could be made only 40 days after statin initiation. When members with 30-day supplies for their first statin fill had predictions made at 40 days, and members with 90-day supplies for their first fill had predictions made at 100 days, poor adherence could be predicted with 86% positive predictive value. CONCLUSIONS To preserve their Medicare Star ratings, plan managers should identify or develop effective programs to improve adherence. An individualized surveillance approach can be used to target members who would most benefit, recognizing the tradeoff between improved model performance over time and the advantage of earlier detection.
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Affiliation(s)
- Andrew J. Zimolzak
- Children’s Hospital Informatics Program at Harvard-Massachusetts Institute of Technology Health Sciences and Technology, Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
| | | | | | - Vincent A. Fusaro
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Nathan P. Palmer
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Suchi Saria
- Division of Health Sciences & Informatics, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Isaac S. Kohane
- Children’s Hospital Informatics Program at Harvard-Massachusetts Institute of Technology Health Sciences and Technology, Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Magdalena A. Jonikas
- Children’s Hospital Informatics Program at Harvard-Massachusetts Institute of Technology Health Sciences and Technology, Boston Children’s Hospital, Boston, Massachusetts, United States of America
| | - Kenneth D. Mandl
- Children’s Hospital Informatics Program at Harvard-Massachusetts Institute of Technology Health Sciences and Technology, Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail:
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20
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Palmer NP, Schmid PR, Berger B, Kohane IS. A gene expression profile of stem cell pluripotentiality and differentiation is conserved across diverse solid and hematopoietic cancers. Genome Biol 2012; 13:R71. [PMID: 22909066 PMCID: PMC3491371 DOI: 10.1186/gb-2012-13-8-r71] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2012] [Accepted: 08/21/2012] [Indexed: 01/06/2023] Open
Abstract
Background Understanding the fundamental mechanisms of tumorigenesis remains one of the most pressing problems in modern biology. To this end, stem-like cells with tumor-initiating potential have become a central focus in cancer research. While the cancer stem cell hypothesis presents a compelling model of self-renewal and partial differentiation, the relationship between tumor cells and normal stem cells remains unclear. Results We identify, in an unbiased fashion, mRNA transcription patterns associated with pluripotent stem cells. Using this profile, we derive a quantitative measure of stem cell-like gene expression activity. We show how this 189 gene signature stratifies a variety of stem cell, malignant and normal tissue samples by their relative plasticity and state of differentiation within Concordia, a diverse gene expression database consisting of 3,209 Affymetrix HGU133+ 2.0 microarray assays. Further, the orthologous murine signature correctly orders a time course of differentiating embryonic mouse stem cells. Finally, we demonstrate how this stem-like signature serves as a proxy for tumor grade in a variety of solid tumors, including brain, breast, lung and colon. Conclusions This core stemness gene expression signature represents a quantitative measure of stem cell-associated transcriptional activity. Broadly, the intensity of this signature correlates to the relative level of plasticity and differentiation across all of the human tissues analyzed. The fact that the intensity of this signature is also capable of differentiating histological grade for a variety of human malignancies suggests potential therapeutic and diagnostic implications.
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