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Schenck EJ, Siempos II. Innovation in Enrichment: Is Persistence Enough? Crit Care Med 2024; 52:853-856. [PMID: 38619345 PMCID: PMC11027940 DOI: 10.1097/ccm.0000000000006239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Affiliation(s)
- Edward J Schenck
- NewYork-Presbyterian Hospital, Weill Cornell Medicine, New York, NY
- Division of Pulmonary & Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY
| | - Ilias I Siempos
- Division of Pulmonary & Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY
- First Department of Critical Care Medicine and Pulmonary Services, Evangelismos Hospital, National and Kapodistrian University of Athens Medical School, Athens, Greece
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Giannakoulis VG, Schenck EJ, Papoutsi E, Price DR, Villar J, Sarwath H, Schmidt F, Thompson BT, Choi AMK, Siempos II. Early Mortality in Clinical Trials of Acute Respiratory Distress Syndrome. Am J Respir Crit Care Med 2024. [PMID: 38691826 DOI: 10.1164/rccm.202402-0318le] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Accepted: 04/30/2024] [Indexed: 05/03/2024] Open
Affiliation(s)
| | - Edward J Schenck
- Weill Cornell Medical College, Medicine, New York, New York, United States
| | - Eleni Papoutsi
- First Department of Critical Care Medicine and Pulmonary Services, Evangelismos Hospital, National and Kapodistrian University of Athens Medical School, Athens, Greece
| | - David R Price
- Weill Cornell Medical College, 12295, Medicine - Division of Pulmonary Critical Care, New York, New York, United States
| | - Jesús Villar
- Hospital Universitario Dr. Negrin, Research Unit, Las Palmas de Gran Canaria, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | - Hina Sarwath
- Weill Cornell Medicine - Qatar, 36579, Doha, Qatar
| | | | - B Taylor Thompson
- Massachusetts General Hospital, Harvard School of Medicine,, 5Division of Pulmonary and Critical Care Medicine, Department of Medicine, Boston, Massachusetts, United States
| | - Augustine M K Choi
- Weill Cornell Medical College, Division of Pulmonary and Critical Care Medicine, Weill Department of Medicine, New York, New York, United States
| | - Ilias I Siempos
- National and Kapodistrian University of Athens, 68993, Athens, Greece;
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Gordon AC, Alipanah-Lechner N, Bos LD, Dianti J, Diaz JV, Finfer S, Fujii T, Giamarellos-Bourboulis EJ, Goligher EC, Gong MN, Karakike E, Liu V, Lumlertgul N, Marshall JC, Menon DK, Meyer NJ, Munroe ES, Myatra SN, Ostermann M, Prescott HC, Randolph AG, Schenck EJ, Seymour CW, Shankar-Hari M, Singer M, Smit MR, Tanaka A, Taccone FS, Thompson BT, Torres LK, Van der Poll T, Vincent JL, Calfee CS. From ICU Syndromes to ICU Subphenotypes: Consensus Report and Recommendations For Developing Precision Medicine in ICU. Am J Respir Crit Care Med 2024. [PMID: 38687499 DOI: 10.1164/rccm.202311-2086so] [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: 11/14/2023] [Accepted: 04/29/2024] [Indexed: 05/02/2024] Open
Abstract
Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a "precision medicine" approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. In order to impact clinical care, identified subpopulations must do more than differentiate prognosis. They must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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Affiliation(s)
- Anthony C Gordon
- Imperial College London Faculty of Medicine, 4957, Intensive Care Medicine, London, United Kingdom of Great Britain and Northern Ireland;
| | - Narges Alipanah-Lechner
- UCSF, 8785, Medicine, San Francisco, California, United States
- UCSF Center for Tuberculosis , Medicine, San Francisco, California, United States
| | - Lieuwe D Bos
- Amsterdam UMC Locatie AMC, 26066, Intensive Care, Amsterdam, Netherlands
| | - Jose Dianti
- University of Toronto, 7938, Interdepartmental Division of Critical Care Medicine, Toronto, Canada
- St Michael's Hospital Li Ka Shing Knowledge Institute, 518773, Keenan Research Centre, Toronto, Canada
- Toronto General Hospital, 33540, Research Institute, Toronto, Canada
| | | | - Simon Finfer
- University of Sydney, Intensive Care, St. Leonards, New South Wales, Australia
| | - Tomoko Fujii
- Jikei University Hospital, 157437, Minato-ku, Japan
| | | | - Ewan C Goligher
- University Health Network, 7989, Department of Medicine, Division of Respirology, Critical Care Program, Toronto, Ontario, Canada
- University of Toronto, 7938, Interdepartmental Division of Critical Care Medicine, Toronto, Ontario, Canada
| | - Michelle Ng Gong
- Montefiore Medical Center, Division of Critical Care Med, Bronx, New York, United States
| | - Eleni Karakike
- National and Kapodistrian University of Athens - Faculty of Medicine, 68989, Athens, Greece
| | - Vincent Liu
- Kaiser Permanente, Division of Research, 94612, California, United States
| | - Nuttha Lumlertgul
- Chulalongkorn University Faculty of Medicine, 65103, Medicine, Bangkok, Thailand
| | | | - David K Menon
- Cambridge University, 2152, Cambridge, Cambridgeshire, United Kingdom of Great Britain and Northern Ireland
| | - Nuala J Meyer
- University of Pennsylvania, 6572, Medicine, Philadelphia, Pennsylvania, United States
| | - Elizabeth S Munroe
- University of Michigan, 1259, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Ann Arbor, Michigan, United States
| | - Sheila N Myatra
- Tata Memorial Hospital, Department of Anaesthesiology,Critical Care and Pain, Mumbai, Maharashtra, India
| | - Marlies Ostermann
- King's College London, Guy's & St Thomas Hospital, Critical Care, London, United Kingdom of Great Britain and Northern Ireland
| | - Hallie C Prescott
- University of Michigan, Internal Medicine, Ann Arbor, Michigan, United States
| | | | - Edward J Schenck
- Weill Cornell Medical College, Medicine, New York, New York, United States
| | | | - Manu Shankar-Hari
- University of Edinburgh MRC Centre for Inflammation Research, 47954, The Queen's Medical Research Institute, Edinburgh, United Kingdom of Great Britain and Northern Ireland
| | - Mervyn Singer
- University College London, 4919, Bloomsbury Inst of Intensive Care Medicine, London, United Kingdom of Great Britain and Northern Ireland
| | - Marry R Smit
- Amsterdam UMC, location University of Amsterdam, Department of Intensive Care, Amsterdam, Netherlands
| | - Aiko Tanaka
- University of Fukui Hospital, Department of Intensive Care, Fukui, Japan
| | | | - B Taylor Thompson
- Massachusetts General Hospital, Harvard School of Medicine,, 5Division of Pulmonary and Critical Care Medicine, Department of Medicine, Boston, Massachusetts, United States
| | - Lisa K Torres
- Weill Cornell Medicine, 12295, Department of Medicine, Division of Pulmonary and Critical Care Medicine, New York, New York, United States
| | - Tom Van der Poll
- Amsterdam UMC -AMC Campus, 26066, Center for Experimental Molecular Medicine, Amsterdam, Noord-Holland, Netherlands
- Amsterdam UMC -AMC Campus, 26066, Department of Medicine, Division of Infectious Diseases, Amsterdam, Noord-Holland, Netherlands
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DO DP, Diaz JL, Weidman K, Graham J, Goyal P, Rajan M, Lau J, Pinheiro L, Rachid L, Simmons W, Schenck EJ, Safford MM, Lief L. Social Networks as a Key Health Determinant in Acute Illness Recovery: A Lesson from the COVID-19 Pandemic. Am J Med 2024:S0002-9343(24)00236-5. [PMID: 38677397 DOI: 10.1016/j.amjmed.2024.04.024] [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: 01/10/2024] [Revised: 04/09/2024] [Accepted: 04/15/2024] [Indexed: 04/29/2024]
Abstract
BACKGROUND The COVID-19 pandemic highlighted the importance of considering social determinants of health in health outcomes. Within this spectrum of determinants, social networks garnered attention as the pandemic highlighted the negative effects of social isolation in the context of social distancing measures. Post-pandemic, examining the role social networks play in COVID-19 recovery can help guide patient care and shape future health policies. This study aimed to investigate the relationship between social networks and self-rated health change, as well as physical function, in patients recovering from COVID-19 pneumonia. METHODS This was a retrospective cohort study utilizing clinical data from two New York City hospitals and a 9-month follow-up survey of COVID-19 pneumonia survivors. We evaluated a composite Social Network Score from the 6-item Lubben Social Network Scale and its association with two outcomes: 1) self-rated health change and 2) physical function. RESULTS A total of 208 patients were included in this study. A one-point increase in the Social Network Score was associated with greater odds of both improved or similar self-rated health change (odds ratio [OR] 1.07, 95% CI 1.02 - 1.12, p = 0.01), as well as unimpaired physical function (OR 1.08, 95% CI 1.03 - 1.14, p < 0.01). CONCLUSION This study emphasized the importance of social networks as a social determinant of health among patients recovering from COVID-19 hospitalization. Targeted interventions to enhance social networks may benefit not only COVID-19 patients but also individuals recovering from other acute illnesses.
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Affiliation(s)
- Di Pan DO
- Division of Pulmonary and Critical Care, Department of Medicine, Weill Cornell Medical College, New York, NY.
| | - Jihui L Diaz
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA
| | - Karissa Weidman
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY
| | - Julia Graham
- Division of Pulmonary and Critical Care, Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Parag Goyal
- Division of Cardiology, Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Mangala Rajan
- Division of General Internal Medicine, Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Jennifer Lau
- Division of General Internal Medicine, Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Laura Pinheiro
- Division of General Internal Medicine, Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Leena Rachid
- Stritch School of Medicine at Loyola University Chicago, Maywood, IL
| | - Will Simmons
- Department of Population Health Sciences, Weill Cornell Medical College, New York, NY
| | - Edward J Schenck
- Division of Pulmonary and Critical Care, Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Monika M Safford
- Division of General Internal Medicine, Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Lindsay Lief
- Division of Pulmonary and Critical Care, Department of Medicine, Weill Cornell Medical College, New York, NY
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Schenck EJ, Plataki M, Wheelock CE. A Lipid Map for Community-acquired Pneumonia with Sepsis: Observation Is the First Step in Scientific Progress. Am J Respir Crit Care Med 2024; 209:903-904. [PMID: 38412325 DOI: 10.1164/rccm.202401-0213ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 02/27/2024] [Indexed: 02/29/2024] Open
Affiliation(s)
- Edward J Schenck
- NewYork-Presbyterian Hospital and Joan and Sanford I. Weill Department of Medicine Weill Cornell Medicine New York, New York
| | - Maria Plataki
- NewYork-Presbyterian Hospital and Joan and Sanford I. Weill Department of Medicine Weill Cornell Medicine New York, New York
| | - Craig E Wheelock
- Unit of Integrative Metabolomics Institute of Environmental Medicine Karolinska Institute Stockholm, Sweden
- Department of Respiratory Medicine and Allergy Karolinska University Hospital Stockholm, Sweden
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Calfee CS, Harhay MO, Schenck EJ, Ferguson ND, Heunks L, White D, Brochard LJ. Critical Care: A Second Special Issue of the Blue Journal. Am J Respir Crit Care Med 2024; 209:769-771. [PMID: 38501798 PMCID: PMC10995575 DOI: 10.1164/rccm.202402-0460ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 03/14/2024] [Indexed: 03/20/2024] Open
Affiliation(s)
- Carolyn S Calfee
- Department of Medicine
- Department of Anesthesia University of California, San Francisco San Francisco, California
| | - Michael O Harhay
- Perelman School of Medicine University of Pennsylvania Philadelphia, Pennsylvania
| | - Edward J Schenck
- Joan and Sanford I. Weill Department of Medicine Weill Cornell Medicine New York, New York
- NewYork-Presbyterian Hospital Weill Cornell Medical Center New York, New York
| | - Niall D Ferguson
- Interdepartmental Division of Critical Care Medicine University of Toronto Toronto, Ontario, Canada
- Department of Medicine Toronto General Hospital Toronto, Ontario, Canada
| | - Leo Heunks
- Department of Intensive Care Radboud University Medical Center Nijmegen, the Netherlands
| | - Douglas White
- Department of Critical Care Medicine University of Pittsburgh School of Medicine Pittsburgh, Pennsylvania
| | - Laurent J Brochard
- Interdepartmental Division of Critical Care Medicine University of Toronto Toronto, Ontario, Canada
- Keenan Research Center Unity Health Toronto Toronto, Ontario, Canada
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Rajendran S, Xu Z, Pan W, Zang C, Siempos I, Torres L, Xu J, Bian J, Schenck EJ, Wang F. Corticosteroids for infectious critical illness: A multicenter target trial emulation stratified by predicted organ dysfunction trajectory. medRxiv 2024:2024.03.07.24303926. [PMID: 38496630 PMCID: PMC10942524 DOI: 10.1101/2024.03.07.24303926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Corticosteroids decrease the duration of organ dysfunction in a range of infectious critical illnesses, but their risk and benefit are not fully defined using this construct. This retrospective multicenter study aimed to evaluate the association between usage of corticosteroids and mortality of patients with infectious critical illness by emulating a target trial framework. The study employed a novel stratification method with predictive machine learning (ML) subphenotyping based on organ dysfunction trajectory. Our analysis revealed that corticosteroids' effectiveness varied depending on the stratification method. The ML-based approach identified four distinct subphenotypes, two of which had a large enough sample size in our patient cohorts for further evaluation: "Rapidly Improving" (RI) and "Rapidly Worsening," (RW) which showed divergent responses to corticosteroid treatment. Specifically, the RW group either benefited or were not harmed from corticosteroids, whereas the RI group appeared to derive harm. In the development cohort, which comprised of a combination of patients from the eICU and MIMIC-IV datasets, hazard ratio estimates for the primary outcome, 28-day mortality, in the RW group was 1.05 (95% CI: 0.96 - 1.04) whereas for the RW group, it was 1.40 (95% CI: 1.28 - 1.54). For the validation cohort, which comprised of patients from the Critical carE Database for Advanced Research, estimates for 28-day mortality for the RW and RI groups were 1.24 (95% CI: 1.05 - 1.46) and 1.34 (95% CI: 1.14 - 1.59), respectively. For secondary outcomes, the RW group had a shorter time to ICU discharge and time to cessation of mechanical ventilation with corticosteroid treatment, where the RI group again demonstrated harm. The findings support matching treatment strategies to empirically observed pathobiology and offer a more nuanced understanding of corticosteroid utility. Our results have implications for the design and interpretation of both observational studies and randomized controlled trials (RCTs), suggesting the need for stratification methods that account for the differential response to standard of care.
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Affiliation(s)
- Suraj Rajendran
- Tri-Institutional Computational Biology & Medicine Program, Cornell University, NY, USA
| | - Zhenxing Xu
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Weishen Pan
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Chengxi Zang
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Ilias Siempos
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, USA
- First Department of Critical Care Medicine and Pulmonary Services, Evangelismos Hospital, National and Kapodistrian University of Athens Medical School, Athens, Greece
| | - Lisa Torres
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes and Biomedical Informatics. College of Medicine. University of Florida. Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics. College of Medicine. University of Florida. Gainesville, FL, USA
| | - Edward J. Schenck
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, USA
| | - Fei Wang
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
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Calfee CS, Harhay MO, Schenck EJ, Ferguson ND, Heunks L, White DB, Brochard LJ. Critical Care: A Special Issue of the Blue Journal. Am J Respir Crit Care Med 2024; 209:465-467. [PMID: 38300147 PMCID: PMC10919106 DOI: 10.1164/rccm.202401-0233ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 02/02/2024] Open
Affiliation(s)
- Carolyn S Calfee
- Departments of Medicine and Anesthesia University of California, San Francisco San Francisco, California
| | - Michael O Harhay
- University of Pennsylvania Perelman School of Medicine Philadelphia, Pennsylvania
| | - Edward J Schenck
- Division of Pulmonary and Critical Care Medicine Joan and Sanford I. Weill Department of Medicine Weill Cornell Medicine New York, New York
- NewYork Presbyterian Hospital Weill Cornell Medical Center New York, New York
| | - Niall D Ferguson
- Interdepartmental Division of Critical Care Medicine Department of Medicine University of Toronto Toronto, Ontario, Canada
- Department of Medicine Toronto General Hospital University of Toronto Toronto, Ontario, Canada
| | - Leo Heunks
- Department of Intensive Care Radboud University Medical Center Nijmegen, The Netherland
| | - Douglas B White
- Division of Pulmonary and Critical Care Medicine San Francisco School of Medicine University of California San Francisco, California
| | - Laurent J Brochard
- Keenan Research Centre for Biomedical Science Li Ka Shing Knowledge Institute Unity Health Toronto Toronto, Ontario, Canada
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Díaz I, Lee H, Kıcıman E, Schenck EJ, Akacha M, Follman D, Ghosh D. Sensitivity analysis for causality in observational studies for regulatory science. J Clin Transl Sci 2023; 7:e267. [PMID: 38380390 PMCID: PMC10877517 DOI: 10.1017/cts.2023.688] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 10/30/2023] [Accepted: 11/16/2023] [Indexed: 02/22/2024] Open
Abstract
Objective The United States Congress passed the 21st Century Cures Act mandating the development of Food and Drug Administration guidance on regulatory use of real-world evidence. The Forum on the Integration of Observational and Randomized Data conducted a meeting with various stakeholder groups to build consensus around best practices for the use of real-world data (RWD) to support regulatory science. Our companion paper describes in detail the context and discussion of the meeting, which includes a recommendation to use a causal roadmap for study designs using RWD. This article discusses one step of the roadmap: the specification of a sensitivity analysis for testing robustness to violations of causal model assumptions. Methods We present an example of a sensitivity analysis from a RWD study on the effectiveness of Nifurtimox in treating Chagas disease, and an overview of various methods, emphasizing practical considerations on their use for regulatory purposes. Results Sensitivity analyses must be accompanied by careful design of other aspects of the causal roadmap. Their prespecification is crucial to avoid wrong conclusions due to researcher degrees of freedom. Sensitivity analysis methods require auxiliary information to produce meaningful conclusions; it is important that they have at least two properties: the validity of the conclusions does not rely on unverifiable assumptions, and the auxiliary information required by the method is learnable from the corpus of current scientific knowledge. Conclusions Prespecified and assumption-lean sensitivity analyses are a crucial tool that can strengthen the validity and trustworthiness of effectiveness conclusions for regulatory science.
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Affiliation(s)
- Iván Díaz
- Division of Biostatistics, Department of Population Health,
New York University Grossman School of Medicine, New
York, NY, USA
| | - Hana Lee
- Office of Biostatistics, Office of Translational Sciences, Center for Drug
Evaluation and Research, U.S. Food and Drug Administration, Silver
Spring, MD, USA
| | | | | | | | - Dean Follman
- Biostatistics Research Branch, National Institute of Allergy and Infectious
Disease, Silver Spring, MD,
USA
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School
of Public Health, University of Colorado Anschutz Medical Campus,
Colorado, USA
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Xie Q, Schenck EJ, Yang HS, Chen Y, Peng Y, Wang F. Faithful AI in Medicine: A Systematic Review with Large Language Models and Beyond. Res Sq 2023:rs.3.rs-3661764. [PMID: 38106170 PMCID: PMC10723541 DOI: 10.21203/rs.3.rs-3661764/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Objective While artificial intelligence (AI), particularly large language models (LLMs), offers significant potential for medicine, it raises critical concerns due to the possibility of generating factually incorrect information, leading to potential long-term risks and ethical issues. This review aims to provide a comprehensive overview of the faithfulness problem in existing research on AI in healthcare and medicine, with a focus on the analysis of the causes of unfaithful results, evaluation metrics, and mitigation methods. Materials and Methods Using PRISMA methodology, we sourced 5,061 records from five databases (PubMed, Scopus, IEEE Xplore, ACM Digital Library, Google Scholar) published between January 2018 to March 2023. We removed duplicates and screened records based on exclusion criteria. Results With 40 leaving articles, we conducted a systematic review of recent developments aimed at optimizing and evaluating factuality across a variety of generative medical AI approaches. These include knowledge-grounded LLMs, text-to-text generation, multimodality-to-text generation, and automatic medical fact-checking tasks. Discussion Current research investigating the factuality problem in medical AI is in its early stages. There are significant challenges related to data resources, backbone models, mitigation methods, and evaluation metrics. Promising opportunities exist for novel faithful medical AI research involving the adaptation of LLMs and prompt engineering. Conclusion This comprehensive review highlights the need for further research to address the issues of reliability and factuality in medical AI, serving as both a reference and inspiration for future research into the safe, ethical use of AI in medicine and healthcare.
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Schenck EJ, Turetz ML, Niederman MS. Letter from the United States: An update on the New York experience with COVID-19. Respirology 2023; 28:892-894. [PMID: 37495224 DOI: 10.1111/resp.14561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 07/28/2023]
Affiliation(s)
- Edward J Schenck
- Division of Pulmonary and Critical Care Medicine, Weill Cornell Medical College, New York, New York, USA
| | - Meredith L Turetz
- Division of Pulmonary and Critical Care Medicine, Weill Cornell Medical College, New York, New York, USA
| | - Michael S Niederman
- Division of Pulmonary and Critical Care Medicine, Weill Cornell Medical College, New York, New York, USA
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12
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Cheong JG, Ravishankar A, Sharma S, Parkhurst CN, Grassmann SA, Wingert CK, Laurent P, Ma S, Paddock L, Miranda IC, Karakaslar EO, Nehar-Belaid D, Thibodeau A, Bale MJ, Kartha VK, Yee JK, Mays MY, Jiang C, Daman AW, Martinez de Paz A, Ahimovic D, Ramos V, Lercher A, Nielsen E, Alvarez-Mulett S, Zheng L, Earl A, Yallowitz A, Robbins L, LaFond E, Weidman KL, Racine-Brzostek S, Yang HS, Price DR, Leyre L, Rendeiro AF, Ravichandran H, Kim J, Borczuk AC, Rice CM, Jones RB, Schenck EJ, Kaner RJ, Chadburn A, Zhao Z, Pascual V, Elemento O, Schwartz RE, Buenrostro JD, Niec RE, Barrat FJ, Lief L, Sun JC, Ucar D, Josefowicz SZ. Epigenetic memory of coronavirus infection in innate immune cells and their progenitors. Cell 2023; 186:3882-3902.e24. [PMID: 37597510 PMCID: PMC10638861 DOI: 10.1016/j.cell.2023.07.019] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 04/20/2023] [Accepted: 07/12/2023] [Indexed: 08/21/2023]
Abstract
Inflammation can trigger lasting phenotypes in immune and non-immune cells. Whether and how human infections and associated inflammation can form innate immune memory in hematopoietic stem and progenitor cells (HSPC) has remained unclear. We found that circulating HSPC, enriched from peripheral blood, captured the diversity of bone marrow HSPC, enabling investigation of their epigenomic reprogramming following coronavirus disease 2019 (COVID-19). Alterations in innate immune phenotypes and epigenetic programs of HSPC persisted for months to 1 year following severe COVID-19 and were associated with distinct transcription factor (TF) activities, altered regulation of inflammatory programs, and durable increases in myelopoiesis. HSPC epigenomic alterations were conveyed, through differentiation, to progeny innate immune cells. Early activity of IL-6 contributed to these persistent phenotypes in human COVID-19 and a mouse coronavirus infection model. Epigenetic reprogramming of HSPC may underlie altered immune function following infection and be broadly relevant, especially for millions of COVID-19 survivors.
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Affiliation(s)
- Jin-Gyu Cheong
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA; Immunology and Microbial Pathogenesis Program, Weill Cornell Medicine, New York, NY 10065, USA
| | - Arjun Ravishankar
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Siddhartha Sharma
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | | | - Simon A Grassmann
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Claire K Wingert
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Paoline Laurent
- HSS Research Institute, Hospital for Special Surgery, New York, NY 10021, USA
| | - Sai Ma
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02142, USA
| | - Lucinda Paddock
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | | | - Emin Onur Karakaslar
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Leiden University Medical Center (LUMC), Leiden, The Netherlands
| | | | - Asa Thibodeau
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Michael J Bale
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA; Immunology and Microbial Pathogenesis Program, Weill Cornell Medicine, New York, NY 10065, USA
| | - Vinay K Kartha
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02142, USA
| | - Jim K Yee
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Minh Y Mays
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Chenyang Jiang
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Andrew W Daman
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA; Immunology and Microbial Pathogenesis Program, Weill Cornell Medicine, New York, NY 10065, USA
| | - Alexia Martinez de Paz
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Dughan Ahimovic
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA; Immunology and Microbial Pathogenesis Program, Weill Cornell Medicine, New York, NY 10065, USA
| | - Victor Ramos
- The Rockefeller University, New York, NY 10065, USA
| | | | - Erik Nielsen
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | | | - Ling Zheng
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Andrew Earl
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02142, USA
| | - Alisha Yallowitz
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Lexi Robbins
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | | | - Karissa L Weidman
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Sabrina Racine-Brzostek
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - He S Yang
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - David R Price
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Louise Leyre
- Immunology and Microbial Pathogenesis Program, Weill Cornell Medicine, New York, NY 10065, USA
| | - André F Rendeiro
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10065, USA; CeMM Research Center for Molecular Medicine, Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Hiranmayi Ravichandran
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10065, USA; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Junbum Kim
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Alain C Borczuk
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA; Department of Pathology and Laboratory Medicine, Northwell Health, Greenvale, NY 11548, USA
| | | | - R Brad Jones
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY 10065, USA; Department of Microbiology and Immunology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Edward J Schenck
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Robert J Kaner
- Department of Genetic Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Amy Chadburn
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Zhen Zhao
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Virginia Pascual
- Department of Pediatrics, Gale and Ira Drukier Institute for Children's Health, Weill Cornell Medicine, New York, NY 10065, USA
| | - Olivier Elemento
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Robert E Schwartz
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Jason D Buenrostro
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02142, USA
| | - Rachel E Niec
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA; The Rockefeller University, New York, NY 10065, USA
| | - Franck J Barrat
- Immunology and Microbial Pathogenesis Program, Weill Cornell Medicine, New York, NY 10065, USA; HSS Research Institute, Hospital for Special Surgery, New York, NY 10021, USA; Department of Microbiology and Immunology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Lindsay Lief
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Joseph C Sun
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Duygu Ucar
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Institute for Systems Genomics, University of Connecticut Health Center, Farmington, CT, USA.
| | - Steven Z Josefowicz
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA; Immunology and Microbial Pathogenesis Program, Weill Cornell Medicine, New York, NY 10065, USA.
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13
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Schmidt F, Abdesselem HB, Suhre K, Vaikath NN, Sohail MU, Al-Nesf M, Bensmail I, Mashod F, Sarwath H, Bernhardt J, Schaefer-Ramadan S, Tan TM, Morris PE, Schenck EJ, Price D, Mohamed-Ali V, Al-Maadheed M, Arredouani A, Decock J, Blackburn JM, Choi AMK, El-Agnaf OM. Auto-immunoproteomics analysis of COVID-19 ICU patients revealed increased levels of autoantibodies related to the male reproductive system. Front Physiol 2023; 14:1203723. [PMID: 37520825 PMCID: PMC10374950 DOI: 10.3389/fphys.2023.1203723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
Background: Coronavirus disease (COVID-19) manifests many clinical symptoms, including an exacerbated immune response and cytokine storm. Autoantibodies in COVID-19 may have severe prodromal effects that are poorly understood. The interaction between these autoantibodies and self-antigens can result in systemic inflammation and organ dysfunction. However, the role of autoantibodies in COVID-19 complications has yet to be fully understood. Methods: The current investigation screened two independent cohorts of 97 COVID-19 patients [discovery (Disc) cohort from Qatar (case = 49 vs. control = 48) and replication (Rep) cohort from New York (case = 48 vs. control = 28)] utilizing high-throughput KoRectly Expressed (KREX) Immunome protein-array technology. Total IgG autoantibody responses were evaluated against 1,318 correctly folded and full-length human proteins. Samples were randomly applied on the precoated microarray slides for 2 h. Cy3-labeled secondary antibodies were used to detect IgG autoantibody response. Slides were scanned at a fixed gain setting using the Agilent fluorescence microarray scanner, generating a 16-bit TIFF file. Group comparisons were performed using a linear model and Fisher's exact test. Differentially expressed proteins were used for KEGG and WIKIpathway annotation to determine pathways in which the proteins of interest were significantly over-represented. Results and conclusion: Autoantibody responses to 57 proteins were significantly altered in the COVID-19 Disc cohort compared to healthy controls (p ≤ 0.05). The Rep cohort had altered autoantibody responses against 26 proteins compared to non-COVID-19 ICU patients who served as controls. Both cohorts showed substantial similarities (r 2 = 0.73) and exhibited higher autoantibody responses to numerous transcription factors, immunomodulatory proteins, and human disease markers. Analysis of the combined cohorts revealed elevated autoantibody responses against SPANXN4, STK25, ATF4, PRKD2, and CHMP3 proteins in COVID-19 patients. The sequences for SPANXN4 and STK25 were cross-validated using sequence alignment tools. ELISA and Western blot further verified the autoantigen-autoantibody response of SPANXN4. SPANXN4 is essential for spermiogenesis and male fertility, which may predict a potential role for this protein in COVID-19-associated male reproductive tract complications, and warrants further research.
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Affiliation(s)
- Frank Schmidt
- Proteomics Core, Weill Cornell Medicine—Qatar, Doha, Qatar
| | - Houari B. Abdesselem
- Proteomics Core Facility, Qatar Biomedical Research Institute (QBRI), Qatar Foundation, Hamad Bin Khalifa University (HBKU), Doha, Qatar
- Neurological Disorders Research Center, QBRI, HBKU, Qatar Foundation, Doha, Qatar
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine—Qatar, Doha, Qatar
| | - Nishant N. Vaikath
- Neurological Disorders Research Center, QBRI, HBKU, Qatar Foundation, Doha, Qatar
| | | | - Maryam Al-Nesf
- Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar
- Center of Metabolism and Inflammation, Division of Medicine, University College London, London, United Kingdom
| | - Ilham Bensmail
- Proteomics Core Facility, Qatar Biomedical Research Institute (QBRI), Qatar Foundation, Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Fathima Mashod
- Proteomics Core, Weill Cornell Medicine—Qatar, Doha, Qatar
| | - Hina Sarwath
- Proteomics Core, Weill Cornell Medicine—Qatar, Doha, Qatar
| | - Joerg Bernhardt
- Institute for Microbiology, University of Greifswald, Greifswald, Germany
| | | | - Ti-Myen Tan
- Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Sengenics Corporation, Damansara Heights, Kuala Lumpur, Malaysia
- Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Priscilla E. Morris
- Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Sengenics Corporation, Damansara Heights, Kuala Lumpur, Malaysia
- Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Edward J. Schenck
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, New York Presbyterian Hospital—Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, United States
| | - David Price
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, New York Presbyterian Hospital—Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, United States
| | - Vidya Mohamed-Ali
- Center of Metabolism and Inflammation, Division of Medicine, University College London, London, United Kingdom
- Anti-Doping Laboratory Qatar, Doha, Qatar
| | - Mohammed Al-Maadheed
- Center of Metabolism and Inflammation, Division of Medicine, University College London, London, United Kingdom
- Anti-Doping Laboratory Qatar, Doha, Qatar
| | - Abdelilah Arredouani
- Diabetes Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha, Qatar
| | - Julie Decock
- College of Health and Life Sciences, Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha, Qatar
- Translational Cancer and Immunity Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha, Qatar
| | - Jonathan M. Blackburn
- Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Sengenics Corporation, Damansara Heights, Kuala Lumpur, Malaysia
- Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Augustine M. K. Choi
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, New York Presbyterian Hospital—Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, United States
| | - Omar M. El-Agnaf
- Neurological Disorders Research Center, QBRI, HBKU, Qatar Foundation, Doha, Qatar
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14
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Xie Q, Schenck EJ, Yang HS, Chen Y, Peng Y, Wang F. Faithful AI in Medicine: A Systematic Review with Large Language Models and Beyond. medRxiv 2023:2023.04.18.23288752. [PMID: 37398329 PMCID: PMC10312867 DOI: 10.1101/2023.04.18.23288752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Artificial intelligence (AI), especially the most recent large language models (LLMs), holds great promise in healthcare and medicine, with applications spanning from biological scientific discovery and clinical patient care to public health policymaking. However, AI methods have the critical concern for generating factually incorrect or unfaithful information, posing potential long-term risks, ethical issues, and other serious consequences. This review aims to provide a comprehensive overview of the faithfulness problem in existing research on AI in healthcare and medicine, with a focus on the analysis of the causes of unfaithful results, evaluation metrics, and mitigation methods. We systematically reviewed the recent progress in optimizing the factuality across various generative medical AI methods, including knowledge-grounded LLMs, text-to-text generation, multimodality-to-text generation, and automatic medical fact-checking tasks. We further discussed the challenges and opportunities of ensuring the faithfulness of AI-generated information in these applications. We expect that this review will assist researchers and practitioners in understanding the faithfulness problem in AI-generated information in healthcare and medicine, as well as the recent progress and challenges in related research. Our review can also serve as a guide for researchers and practitioners who are interested in applying AI in medicine and healthcare.
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Affiliation(s)
- Qianqian Xie
- Department of Population Health Science, Weill Cornell Medicine, Cornell University
| | - Edward J Schenck
- Division of Pulmonary and Critical Care Medicine, New York-Presbyterian Hospital/Weill Cornell Medical Center, 425 E. 61st Street, 4th Floor, Suite 402, New York, NY, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - He S Yang
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Yong Chen
- School of Medicine, University of Pennsylvania
| | - Yifan Peng
- Department of Population Health Science, Weill Cornell Medicine, Cornell University
| | - Fei Wang
- Department of Population Health Science, Weill Cornell Medicine, Cornell University
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15
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Zang C, Zhang Y, Xu J, Bian J, Morozyuk D, Schenck EJ, Khullar D, Nordvig AS, Shenkman EA, Rothman RL, Block JP, Lyman K, Weiner MG, Carton TW, Wang F, Kaushal R. Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative. Nat Commun 2023; 14:1948. [PMID: 37029117 PMCID: PMC10080528 DOI: 10.1038/s41467-023-37653-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 03/24/2023] [Indexed: 04/09/2023] Open
Abstract
Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with specific patient populations which makes their generalizability unclear. This study aims to characterize PASC using the EHR data warehouses from two large Patient-Centered Clinical Research Networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) area and 16.8 million patients in Florida respectively. With a high-throughput screening pipeline based on propensity score and inverse probability of treatment weighting, we identified a broad list of diagnoses and medications which exhibited significantly higher incidence risk for patients 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We identified more PASC diagnoses in NYC than in Florida regarding our screening criteria, and conditions including dementia, hair loss, pressure ulcers, pulmonary fibrosis, dyspnea, pulmonary embolism, chest pain, abnormal heartbeat, malaise, and fatigue, were replicated across both cohorts. Our analyses highlight potentially heterogeneous risks of PASC in different populations.
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Affiliation(s)
- Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Yongkang Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Dmitry Morozyuk
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Edward J Schenck
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Dhruv Khullar
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Anna S Nordvig
- Department of Neurology, Weill Cornell Medicine, New York, NY, USA
| | - Elizabeth A Shenkman
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Russell L Rothman
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jason P Block
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA
| | - Kristin Lyman
- Louisiana Public Health Institute, New Orleans, LA, USA
| | - Mark G Weiner
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | | | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
| | - Rainu Kaushal
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
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16
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Ackerman KS, Hoffman KL, Díaz I, Simmons W, Ballman KV, Kodiyanplakkal RP, Schenck EJ. Effect of Sepsis on Death as Modified by Solid Organ Transplantation. Open Forum Infect Dis 2023; 10:ofad148. [PMID: 37056981 PMCID: PMC10086309 DOI: 10.1093/ofid/ofad148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 03/15/2023] [Indexed: 03/20/2023] Open
Abstract
Abstract
Background
Patients with solid organ transplants (SOT) have an increased risk for sepsis compared to the general population. Paradoxically, studies suggest that SOT patients with sepsis may experience better outcomes compared to those without a SOT. However, these analyses used previous definitions of sepsis. It remains unknown whether the more recent definitions of sepsis and modern analytic approaches demonstrate a similar relationship.
Methods
Using the Weill Cornell-Critical Care Database for Advanced Research (WC-CEDAR) we analyzed granular physiologic, microbiologic, comorbidity, and therapeutic data in patients with and without SOT admitted to intensive care units (ICU’s). We used a survival analysis with a targeted minimum loss-based estimation, adjusting for within group (SOT and non-SOT) potential confounders to ascertain whether the effect of sepsis, defined by sepsis-3, on 28-day mortality was modified by SOT status. We performed additional analyses on restricted populations.
Results
We analyzed 28,431 patients: 439 with SOT and sepsis, 281 with SOT without sepsis, 6793 with sepsis and without SOT, and 20918 with neither. The most common SOT types were kidney (475) and liver (163). Despite a higher severity of illness in both sepsis groups, the adjusted sepsis-attributable effect on 28-day mortality for non-SOT patients was 4.1% (3.8, 4.5) and -14.4% (-16.8, -12) for SOT patients. The adjusted SOT effect modification was -18.5% (-21.2, -15.9). The adjusted sepsis-attributable effect for immunocompromised controls was -3.5% (-4.5, -2.6).
Conclusions
Across a large database of patients admitted to ICU’s, the sepsis associated 28-day mortality effect was significantly lower in SOT patients compared to controls.
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Affiliation(s)
- Kevin S Ackerman
- Department of Medicine, Hospital of the University of Pennsylvania , Philadelphia, PA , USA
| | - Katherine L Hoffman
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine , New York, NY , USA
| | - Iván Díaz
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine , New York, NY , USA
| | - Will Simmons
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine , New York, NY , USA
| | - Karla V Ballman
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine , New York, NY , USA
| | - Rosy P Kodiyanplakkal
- Division of Infectious Diseases, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine , New York, NY , USA
- NewYork-Presbyterian Hospital, Weill Cornell Medicine , New York, NY , USA
| | - Edward J Schenck
- NewYork-Presbyterian Hospital, Weill Cornell Medicine , New York, NY , USA
- Division of Pulmonary & Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine , New York, NY , USA
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17
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Varma JK, Zang C, Carton TW, Block JP, Khullar DJ, Zhang Y, Weiner MG, Rothman RL, Schenck EJ, Xu Z, Lyman K, Bian J, Xu J, Shenkman EA, Maughan C, Castro-Baucom L, O’Brien L, Wang F, Kaushal R. Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER Program. medRxiv 2023:2023.02.15.23286012. [PMID: 36865304 PMCID: PMC9980238 DOI: 10.1101/2023.02.15.23286012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Importance The frequency and characteristics of post-acute sequelae of SARS-CoV-2 infection (PASC) may vary by SARS-CoV-2 variant. Objective To characterize PASC-related conditions among individuals likely infected by the ancestral strain in 2020 and individuals likely infected by the Delta variant in 2021. Design Retrospective cohort study of electronic medical record data for approximately 27 million patients from March 1, 2020-November 30, 2021. Setting Healthcare facilities in New York and Florida. Participants Patients who were at least 20 years old and had diagnosis codes that included at least one SARS-CoV-2 viral test during the study period. Exposure Laboratory-confirmed COVID-19 infection, classified by the most common variant prevalent in those regions at the time. Main Outcomes and Measures Relative risk (estimated by adjusted hazard ratio [aHR]) and absolute risk difference (estimated by adjusted excess burden) of new conditions, defined as new documentation of symptoms or diagnoses, in persons between 31-180 days after a positive COVID-19 test compared to persons with only negative tests during the 31-180 days after the last negative test. Results We analyzed data from 560,752 patients. The median age was 57 years; 60.3% were female, 20.0% non-Hispanic Black, and 19.6% Hispanic. During the study period, 57,616 patients had a positive SARS-CoV-2 test; 503,136 did not. For infections during the ancestral strain period, pulmonary fibrosis, edema (excess fluid), and inflammation had the largest aHR, comparing those with a positive test to those with a negative test, (aHR 2.32 [95% CI 2.09 2.57]), and dyspnea (shortness of breath) carried the largest excess burden (47.6 more cases per 1,000 persons). For infections during the Delta period, pulmonary embolism had the largest aHR comparing those with a positive test to a negative test (aHR 2.18 [95% CI 1.57, 3.01]), and abdominal pain carried the largest excess burden (85.3 more cases per 1,000 persons). Conclusions and Relevance We documented a substantial relative risk of pulmonary embolism and large absolute risk difference of abdomen-related symptoms after SARS-CoV-2 infection during the Delta variant period. As new SARS-CoV-2 variants emerge, researchers and clinicians should monitor patients for changing symptoms and conditions that develop after infection.
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Affiliation(s)
- Jay K. Varma
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | | | - Jason P. Block
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA
| | - Dhruv J. Khullar
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
- Department of Medicine, Weill Cornell Medicine, New York, NY
| | - Yongkang Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Mark G. Weiner
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Russell L. Rothman
- Institute for Medicine and Public Health, Vanderbilt University Medical Center Nashville, TN
| | | | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Kristin Lyman
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Jiang Bian
- Health Outcomes and Biomedical Informatics, University of Florida Health, Gainesville, FL
| | - Jie Xu
- Health Outcomes and Biomedical Informatics, University of Florida Health, Gainesville, FL
| | - Elizabeth A. Shenkman
- Health Outcomes and Biomedical Informatics, University of Florida Health, Gainesville, FL
| | | | | | | | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Rainu Kaushal
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
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18
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Krishnan JK, Schenck EJ. Author's response: "Assessing mortality differences across acute respiratory failure management strategies in Covid-19". J Crit Care 2023:154238. [PMID: 36588004 PMCID: PMC9805834 DOI: 10.1016/j.jcrc.2022.154238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 12/12/2022] [Indexed: 01/03/2023]
Affiliation(s)
- Jamuna K. Krishnan
- Corresponding author at: Division of Pulmonary and Critical Care Medicine, 1305 York Avenue Y-1047, Box 96, United States of America
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19
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Zhang H, Zang C, Xu Z, Zhang Y, Xu J, Bian J, Morozyuk D, Khullar D, Zhang Y, Nordvig AS, Schenck EJ, Shenkman EA, Rothman RL, Block JP, Lyman K, Weiner MG, Carton TW, Wang F, Kaushal R. Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes. Nat Med 2023; 29:226-235. [PMID: 36456834 PMCID: PMC9873564 DOI: 10.1038/s41591-022-02116-3] [Citation(s) in RCA: 47] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 11/02/2022] [Indexed: 12/05/2022]
Abstract
The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated or newly incident in the period after acute SARS-CoV-2 infection. Most studies have examined these conditions individually without providing evidence on co-occurring conditions. In this study, we leveraged the electronic health record data of two large cohorts, INSIGHT and OneFlorida+, from the national Patient-Centered Clinical Research Network. We created a development cohort from INSIGHT and a validation cohort from OneFlorida+ including 20,881 and 13,724 patients, respectively, who were SARS-CoV-2 infected, and we investigated their newly incident diagnoses 30-180 days after a documented SARS-CoV-2 infection. Through machine learning analysis of over 137 symptoms and conditions, we identified four reproducible PASC subphenotypes, dominated by cardiac and renal (including 33.75% and 25.43% of the patients in the development and validation cohorts); respiratory, sleep and anxiety (32.75% and 38.48%); musculoskeletal and nervous system (23.37% and 23.35%); and digestive and respiratory system (10.14% and 12.74%) sequelae. These subphenotypes were associated with distinct patient demographics, underlying conditions before SARS-CoV-2 infection and acute infection phase severity. Our study provides insights into the heterogeneity of PASC and may inform stratified decision-making in the management of PASC conditions.
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Affiliation(s)
- Hao Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Yongkang Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Dmitry Morozyuk
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Dhruv Khullar
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Yiye Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Anna S Nordvig
- Department of Neurology, Weill Cornell Medicine, New York, NY, USA
| | - Edward J Schenck
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Elizabeth A Shenkman
- Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Russell L Rothman
- Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jason P Block
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA
| | - Kristin Lyman
- Louisiana Public Health Institute, New Orleans, LA, USA
| | - Mark G Weiner
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | | | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
| | - Rainu Kaushal
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
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20
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Hoffman KL, Schenck EJ, Satlin MJ, Whalen W, Pan D, Williams N, Díaz I. Comparison of a Target Trial Emulation Framework vs Cox Regression to Estimate the Association of Corticosteroids With COVID-19 Mortality. JAMA Netw Open 2022; 5:e2234425. [PMID: 36190729 PMCID: PMC9530966 DOI: 10.1001/jamanetworkopen.2022.34425] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
IMPORTANCE Communication and adoption of modern study design and analytical techniques is of high importance for the improvement of clinical research from observational data. OBJECTIVE To compare a modern method for statistical inference, including a target trial emulation framework and doubly robust estimation, with approaches common in the clinical literature, such as Cox proportional hazards models. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study used longitudinal electronic health record data for outcomes at 28-days from time of hospitalization within a multicenter New York, New York, hospital system. Participants included adult patients hospitalized between March 1 and May 15, 2020, with COVID-19 and not receiving corticosteroids for chronic use. Data were analyzed from October 2021 to March 2022. EXPOSURES Corticosteroid exposure was defined as more than 0.5 mg/kg methylprednisolone equivalent in a 24-hour period. For target trial emulation, exposures were corticosteroids for 6 days if and when a patient met criteria for severe hypoxia vs no corticosteroids. For approaches common in clinical literature, treatment definitions used for variables in Cox regression models varied by study design (no time frame, 1 day, and 5 days from time of severe hypoxia). MAIN OUTCOMES AND MEASURES The main outcome was 28-day mortality from time of hospitalization. The association of corticosteroids with mortality for patients with moderate to severe COVID-19 was assessed using the World Health Organization (WHO) meta-analysis of corticosteroid randomized clinical trials as a benchmark. RESULTS A total of 3298 patients (median [IQR] age, 65 [53-77] years; 1970 [60%] men) were assessed, including 423 patients who received corticosteroids at any point during hospitalization and 699 patients who died within 28 days of hospitalization. Target trial emulation analysis found corticosteroids were associated with a reduced 28-day mortality rate, from 32.2%; (95% CI, 30.9%-33.5%) to 25.7% (95% CI, 24.5%-26.9%). This estimate is qualitatively identical to the WHO meta-analysis odds ratio of 0.66 (95% CI, 0.53-0.82). Hazard ratios using methods comparable with current corticosteroid research range in size and direction, from 0.50 (95% CI, 0.41-0.62) to 1.08 (95% CI, 0.80-1.47). CONCLUSIONS AND RELEVANCE These findings suggest that clinical research based on observational data can be used to estimate findings similar to those from randomized clinical trials; however, the correctness of these estimates requires designing the study and analyzing the data based on principles that are different from the current standard in clinical research.
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Affiliation(s)
- Katherine L. Hoffman
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, New York
| | - Edward J. Schenck
- Division of Pulmonary and Critical Care, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Michael J. Satlin
- Division of Infectious Disease, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - William Whalen
- Division of Pulmonary and Critical Care, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Di Pan
- Division of Pulmonary and Critical Care, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Nicholas Williams
- Mailman School of Public Health, Department of Epidemiology, Columbia University, New York, New York
| | - Iván Díaz
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, New York
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21
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Hoffman KL, Schenck EJ, Satlin MJ, Whalen W, Pan D, Williams N, Díaz I. Comparison of a Target Trial Emulation Framework to Cox Regression to Estimate the Effect of Corticosteroids on COVID-19 Mortality. medRxiv 2022:2022.05.27.22275037. [PMID: 35702149 PMCID: PMC9196111 DOI: 10.1101/2022.05.27.22275037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Importance Communication and adoption of modern study design and analytical techniques is of high importance for the improvement of clinical research from observational data. Objective To compare (1) a modern method for causal inference including a target trial emulation framework and doubly robust estimation to (2) approaches common in the clinical literature such as Cox proportional hazards models. To do this, we estimate the effect of corticosteroids on mortality for moderate-to-severe coronavirus disease 2019 (COVID-19) patients. We use the World Health Organization's (WHO) meta-analysis of corticosteroid randomized controlled trials (RCTs) as a benchmark. Design Retrospective cohort study using longitudinal electronic health record data for 28 days from time of hospitalization. Settings Multi-center New York City hospital system. Participants Adult patients hospitalized between March 1-May 15, 2020 with COVID-19 and not on corticosteroids for chronic use. Intervention Corticosteroid exposure defined as >0.5mg/kg methylprednisolone equivalent in a 24-hour period. For target trial emulation, interventions are (1) corticosteroids for six days if and when patient meets criteria for severe hypoxia and (2) no corticosteroids. For approaches common in clinical literature, treatment definitions used for variables in Cox regression models vary by study design (no time frame, one-, and five-days from time of severe hypoxia). Main outcome 28-day mortality from time of hospitalization. Results 3,298 patients (median age 65 (IQR 53-77), 60% male). 423 receive corticosteroids at any point during hospitalization, 699 die within 28 days of hospitalization. Target trial emulation estimates corticosteroids to reduce 28-day mortality from 32.2% (95% CI 30.9-33.5) to 25.7% (24.5-26.9). This estimate is qualitatively identical to the WHO's RCT meta-analysis odds ratio of 0.66 (0.53-0.82)). Hazard ratios using methods comparable to current corticosteroid research range in size and direction from 0.50 (0.41-0.62) to 1.08 (0.80-1.47). Conclusion and Relevance Clinical research based on observational data can unveil true causal relationships; however, the correctness of these effect estimates requires designing the study and analyzing the data based on principles which are different from the current standard in clinical research.
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Affiliation(s)
- Katherine L Hoffman
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Edward J Schenck
- Division of Pulmonary and Critical Care, Department of Medicine, Weill Cornell Medicine, New York, NY
| | - Michael J Satlin
- Division of Infectious Disease, Department of Medicine, Weill Cornell Medicine, New York, NY
| | - William Whalen
- Division of Pulmonary and Critical Care, Department of Medicine, Weill Cornell Medicine, New York, NY
| | - Di Pan
- Division of Pulmonary and Critical Care, Department of Medicine, Weill Cornell Medicine, New York, NY
| | - Nicholas Williams
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY
| | - Iván Díaz
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
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22
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Krishnan JK, Rajan M, Baer BR, Hoffman KL, Alshak MN, Aronson KI, Goyal P, Ezeomah C, Hill SS, Martinez FJ, Turetz ML, Wells MT, Safford MM, Schenck EJ. Assessing mortality differences across acute respiratory failure management strategies in Covid-19. J Crit Care 2022; 70:154045. [PMID: 35490502 PMCID: PMC9049881 DOI: 10.1016/j.jcrc.2022.154045] [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] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 02/14/2022] [Accepted: 04/06/2022] [Indexed: 12/17/2022]
Abstract
PURPOSE Prolonged observation could avoid invasive mechanical ventilation (IMV) and related risks in patients with Covid-19 acute respiratory failure (ARF) compared to initiating early IMV. We aimed to determine the association between ARF management strategy and in-hospital mortality. MATERIALS AND METHODS Patients in the Weill Cornell Covid-19 registry who developed ARF between March 5 - March 25, 2020 were exposed to an early IMV strategy; between March 26 - April 1, 2020 to an intermediate strategy; and after April 2 to prolonged observation. Cox proportional hazards regression was used to model in-hospital mortality and test an interaction between ARF management strategy and modified sequential organ failure assessment (mSOFA). RESULTS Among 632 patients with ARF, 24% of patients in the early IMV strategy died versus 28% in prolonged observation. At lower mSOFA, prolonged observation was associated with lower mortality compared to early IMV (at mSOFA = 0, HR 0.16 [95% CI 0.04-0.57]). Mortality risk increased in the prolonged observation strategy group with each point increase in mSOFA score (HR 1.29 [95% CI 1.10-1.51], p = 0.002). CONCLUSION In Covid-19 ARF, prolonged observation was associated with a mortality benefit at lower mSOFA scores, and increased mortality at higher mSOFA scores compared to early IMV.
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Affiliation(s)
- Jamuna K Krishnan
- Divison of Pulmonary and Critical Care Medicine, Weill Cornell Department of Medicine, NY, NY, United States of America.
| | - Mangala Rajan
- Division of General Internal Medicine, Weill Cornell Department of Medicine, NY, NY, United States of America
| | - Benjamin R Baer
- Department of Statistics and Data Science, Cornell University, Ithaca, New York, United States of America
| | - Katherine L Hoffman
- Division of Biostatistics and Epidemiology, Weill Cornell Medicine, NY, NY, United States of America
| | - Mark N Alshak
- Division of General Internal Medicine, Weill Cornell Department of Medicine, NY, NY, United States of America
| | - Kerri I Aronson
- Divison of Pulmonary and Critical Care Medicine, Weill Cornell Department of Medicine, NY, NY, United States of America
| | - Parag Goyal
- Division of General Internal Medicine, Weill Cornell Department of Medicine, NY, NY, United States of America; Division of Cardiology, Weill Cornell Department of Medicine, NY, NY, United States of America
| | - Chiomah Ezeomah
- Division of General Internal Medicine, Weill Cornell Department of Medicine, NY, NY, United States of America
| | - Shanna S Hill
- Department of Anesthesiology, Weill Cornell Medicine, NY, NY, United States of America
| | - Fernando J Martinez
- Divison of Pulmonary and Critical Care Medicine, Weill Cornell Department of Medicine, NY, NY, United States of America
| | - Meredith L Turetz
- Divison of Pulmonary and Critical Care Medicine, Weill Cornell Department of Medicine, NY, NY, United States of America
| | - Martin T Wells
- Department of Statistics and Data Science, Cornell University, Ithaca, New York, United States of America; Department of Population Health Sciences, Weill Cornell Medicine, NY, NY, United States of America
| | - Monika M Safford
- Division of General Internal Medicine, Weill Cornell Department of Medicine, NY, NY, United States of America
| | - Edward J Schenck
- Divison of Pulmonary and Critical Care Medicine, Weill Cornell Department of Medicine, NY, NY, United States of America
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23
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Basem JI, Roth AF, White RS, Tangel VE, Jiang SY, Choi JM, Hoffman KL, Schenck EJ, Turnbull ZA, Pryor KO, Ivascu NS, Memtsoudis SG, Goldstein PA. Patient care in rapid-expansion intensive care units during the COVID-19 pandemic crisis. BMC Anesthesiol 2022; 22:209. [PMID: 35794523 PMCID: PMC9261025 DOI: 10.1186/s12871-022-01752-z] [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] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/23/2022] [Indexed: 12/20/2022] Open
Abstract
Background The coronavirus-2019 (COVID-19) pandemic highlighted the unfortunate reality that many hospitals have insufficient intensive care unit (ICU) capacity to meet massive, unanticipated increases in demand. To drastically increase ICU capacity, NewYork-Presbyterian/Weill Cornell Medical Center modified its existing operating rooms and post-anaesthesia care units during the initial expansion phase to accommodate the surge of critically ill patients. Methods This retrospective chart review examined patient care in non-standard Expansion ICUs as compared to standard ICUs. We compared clinical data between the two settings to determine whether the expeditious development and deployment of critical care resources during an evolving medical crisis could provide appropriate care. Results Sixty-six patients were admitted to Expansion ICUs from March 1st to April 30th, 2020 and 343 were admitted to standard ICUs. Most patients were male (70%), White (30%), 45–64 years old (35%), non-smokers (73%), had hypertension (58%), and were hospitalized for a median of 40 days. For patients that died, there was no difference in treatment management, but the Expansion cohort had a higher median ICU length of stay (q = 0.037) and ventilatory length (q = 0.015). The cohorts had similar rates of discharge to home, but the Expansion ICU cohort had higher rates of discharge to a rehabilitation facility and overall lower mortality. Conclusions We found no significantly worse outcomes for the Expansion ICU cohort compared to the standard ICU cohort at our institution during the COVID-19 pandemic, which demonstrates the feasibility of providing safe and effective care for patients in an Expansion ICU.
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Affiliation(s)
- Jade I Basem
- Department of Anesthesiology, Weill Cornell Medicine, 1300 York Avenue, Room A-1050, NY, 10065, New York, USA
| | - Anna F Roth
- Department of Anesthesiology, Weill Cornell Medicine, 1300 York Avenue, Room A-1050, NY, 10065, New York, USA
| | - Robert S White
- Department of Anesthesiology, Weill Cornell Medicine, 1300 York Avenue, Room A-1050, NY, 10065, New York, USA
| | - Virginia E Tangel
- Department of Anesthesiology, Weill Cornell Medicine, 1300 York Avenue, Room A-1050, NY, 10065, New York, USA
| | - Silis Y Jiang
- Department of Anesthesiology, Weill Cornell Medicine, 1300 York Avenue, Room A-1050, NY, 10065, New York, USA
| | - Jacky M Choi
- Department of Population Health Sciences, Division of Biostatistics and Epidemiology, Weill Cornell Medicine, New York, NY, USA
| | - Katherine L Hoffman
- Department of Population Health Sciences, Division of Biostatistics and Epidemiology, Weill Cornell Medicine, New York, NY, USA
| | - Edward J Schenck
- Department of Population Health Sciences, Division of Biostatistics and Epidemiology, Weill Cornell Medicine, New York, NY, USA
| | - Zachary A Turnbull
- Department of Anesthesiology, Weill Cornell Medicine, 1300 York Avenue, Room A-1050, NY, 10065, New York, USA
| | - Kane O Pryor
- Department of Anesthesiology, Weill Cornell Medicine, 1300 York Avenue, Room A-1050, NY, 10065, New York, USA
| | - Natalia S Ivascu
- Department of Anesthesiology, Weill Cornell Medicine, 1300 York Avenue, Room A-1050, NY, 10065, New York, USA
| | - Stavros G Memtsoudis
- Department of Anesthesiology, Weill Cornell Medicine, 1300 York Avenue, Room A-1050, NY, 10065, New York, USA.,Department of Anesthesiology, Critical Care & Pain Management, Hospital for Special Surgery, New York, NY, USA
| | - Peter A Goldstein
- Department of Anesthesiology, Weill Cornell Medicine, 1300 York Avenue, Room A-1050, NY, 10065, New York, USA. .,Department of Medicine, Weill Cornell Medicine, New York, NY, USA. .,Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
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24
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Xu Z, Mao C, Su C, Zhang H, Siempos I, Torres LK, Pan D, Luo Y, Schenck EJ, Wang F. Sepsis subphenotyping based on organ dysfunction trajectory. Crit Care 2022; 26:197. [PMID: 35786445 PMCID: PMC9250715 DOI: 10.1186/s13054-022-04071-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 06/25/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Sepsis is a heterogeneous syndrome, and the identification of clinical subphenotypes is essential. Although organ dysfunction is a defining element of sepsis, subphenotypes of differential trajectory are not well studied. We sought to identify distinct Sequential Organ Failure Assessment (SOFA) score trajectory-based subphenotypes in sepsis. METHODS We created 72-h SOFA score trajectories in patients with sepsis from four diverse intensive care unit (ICU) cohorts. We then used dynamic time warping (DTW) to compute heterogeneous SOFA trajectory similarities and hierarchical agglomerative clustering (HAC) to identify trajectory-based subphenotypes. Patient characteristics were compared between subphenotypes and a random forest model was developed to predict subphenotype membership at 6 and 24 h after being admitted to the ICU. The model was tested on three validation cohorts. Sensitivity analyses were performed with alternative clustering methodologies. RESULTS A total of 4678, 3665, 12,282, and 4804 unique sepsis patients were included in development and three validation cohorts, respectively. Four subphenotypes were identified in the development cohort: Rapidly Worsening (n = 612, 13.1%), Delayed Worsening (n = 960, 20.5%), Rapidly Improving (n = 1932, 41.3%), and Delayed Improving (n = 1174, 25.1%). Baseline characteristics, including the pattern of organ dysfunction, varied between subphenotypes. Rapidly Worsening was defined by a higher comorbidity burden, acidosis, and visceral organ dysfunction. Rapidly Improving was defined by vasopressor use without acidosis. Outcomes differed across the subphenotypes, Rapidly Worsening had the highest in-hospital mortality (28.3%, P-value < 0.001), despite a lower SOFA (mean: 4.5) at ICU admission compared to Rapidly Improving (mortality:5.5%, mean SOFA: 5.5). An overall prediction accuracy of 0.78 (95% CI, [0.77, 0.8]) was obtained at 6 h after ICU admission, which increased to 0.87 (95% CI, [0.86, 0.88]) at 24 h. Similar subphenotypes were replicated in three validation cohorts. The majority of patients with sepsis have an improving phenotype with a lower mortality risk; however, they make up over 20% of all deaths due to their larger numbers. CONCLUSIONS Four novel, clinically-defined, trajectory-based sepsis subphenotypes were identified and validated. Identifying trajectory-based subphenotypes has immediate implications for the powering and predictive enrichment of clinical trials. Understanding the pathophysiology of these differential trajectories may reveal unanticipated therapeutic targets and identify more precise populations and endpoints for clinical trials.
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Affiliation(s)
- Zhenxing Xu
- grid.5386.8000000041936877XDivision of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, 425 E. 61st Street, 3rd Floor, Suite 301, New York, NY USA
| | - Chengsheng Mao
- grid.16753.360000 0001 2299 3507Division of Health and Biomedical Informatics, Department of Preventive Medicine Center for Health Information Partnerships, Feinberg School of Medicine, Northwestern University, Rubloff Building 11th Floor, 750 N Lake Shore, Chicago, IL USA
| | - Chang Su
- grid.264727.20000 0001 2248 3398Department of Health Service Administration and Policy, College of Public Health, Temple University, Philadelphia, PA USA
| | - Hao Zhang
- grid.5386.8000000041936877XDivision of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, 425 E. 61st Street, 3rd Floor, Suite 301, New York, NY USA
| | - Ilias Siempos
- grid.413734.60000 0000 8499 1112Division of Pulmonary and Critical Care Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medical Center, 425 E. 61st Street, 4th Floor, Suite 402, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, Weill Cornell Medical College, New York, NY USA
| | - Lisa K. Torres
- grid.413734.60000 0000 8499 1112Division of Pulmonary and Critical Care Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medical Center, 425 E. 61st Street, 4th Floor, Suite 402, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, Weill Cornell Medical College, New York, NY USA
| | - Di Pan
- grid.413734.60000 0000 8499 1112Division of Pulmonary and Critical Care Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medical Center, 425 E. 61st Street, 4th Floor, Suite 402, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, Weill Cornell Medical College, New York, NY USA
| | - Yuan Luo
- Division of Health and Biomedical Informatics, Department of Preventive Medicine Center for Health Information Partnerships, Feinberg School of Medicine, Northwestern University, Rubloff Building 11th Floor, 750 N Lake Shore, Chicago, IL, USA.
| | - Edward J. Schenck
- grid.413734.60000 0000 8499 1112Division of Pulmonary and Critical Care Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medical Center, 425 E. 61st Street, 4th Floor, Suite 402, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, Weill Cornell Medical College, New York, NY USA
| | - Fei Wang
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, 425 E. 61st Street, 3rd Floor, Suite 301, New York, NY, USA.
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25
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Price DR, Benedetti E, Hoffman KL, Gomez-Escobar L, Alvarez-Mulett S, Capili A, Sarwath H, Parkhurst CN, Lafond E, Weidman K, Ravishankar A, Cheong JG, Batra R, Büyüközkan M, Chetnik K, Easthausen I, Schenck EJ, Racanelli AC, Outtz Reed H, Laurence J, Josefowicz SZ, Lief L, Choi ME, Schmidt F, Borczuk AC, Choi AMK, Krumsiek J, Rafii S. Angiopoietin 2 Is Associated with Vascular Necroptosis Induction in Coronavirus Disease 2019 Acute Respiratory Distress Syndrome. Am J Pathol 2022; 192:1001-1015. [PMID: 35469796 PMCID: PMC9027298 DOI: 10.1016/j.ajpath.2022.04.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 03/10/2022] [Accepted: 04/04/2022] [Indexed: 12/12/2022]
Abstract
Vascular injury is a well-established, disease-modifying factor in acute respiratory distress syndrome (ARDS) pathogenesis. Recently, coronavirus disease 2019 (COVID-19)-induced injury to the vascular compartment has been linked to complement activation, microvascular thrombosis, and dysregulated immune responses. This study sought to assess whether aberrant vascular activation in this prothrombotic context was associated with the induction of necroptotic vascular cell death. To achieve this, proteomic analysis was performed on blood samples from COVID-19 subjects at distinct time points during ARDS pathogenesis (hospitalized at risk, N = 59; ARDS, N = 31; and recovery, N = 12). Assessment of circulating vascular markers in the at-risk cohort revealed a signature of low vascular protein abundance that tracked with low platelet levels and increased mortality. This signature was replicated in the ARDS cohort and correlated with increased plasma angiopoietin 2 levels. COVID-19 ARDS lung autopsy immunostaining confirmed a link between vascular injury (angiopoietin 2) and platelet-rich microthrombi (CD61) and induction of necrotic cell death [phosphorylated mixed lineage kinase domain-like (pMLKL)]. Among recovery subjects, the vascular signature identified patients with poor functional outcomes. Taken together, this vascular injury signature was associated with low platelet levels and increased mortality and can be used to identify ARDS patients most likely to benefit from vascular targeted therapies.
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Affiliation(s)
- David R Price
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Elisa Benedetti
- Institute of Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Katherine L Hoffman
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, New York
| | - Luis Gomez-Escobar
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York
| | - Sergio Alvarez-Mulett
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York
| | - Allyson Capili
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York
| | - Hina Sarwath
- Proteomics Core, Weill Cornell Medicine-Qatar, Qatar Foundation-Education City, Doha, Qatar
| | - Christopher N Parkhurst
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Elyse Lafond
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Karissa Weidman
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Arjun Ravishankar
- Laboratory of Epigenetics and Immunity, Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - Jin Gyu Cheong
- Laboratory of Epigenetics and Immunity, Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - Richa Batra
- Institute of Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Mustafa Büyüközkan
- Institute of Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Kelsey Chetnik
- Institute of Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Imaani Easthausen
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, New York
| | - Edward J Schenck
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Alexandra C Racanelli
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Hasina Outtz Reed
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Jeffrey Laurence
- Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York; Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Steven Z Josefowicz
- Laboratory of Epigenetics and Immunity, Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - Lindsay Lief
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Mary E Choi
- Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York; Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Frank Schmidt
- Proteomics Core, Weill Cornell Medicine-Qatar, Qatar Foundation-Education City, Doha, Qatar
| | - Alain C Borczuk
- Department of Pathology and Laboratory Medicine, New York Presbyterian-Weill Cornell Medicine, New York, New York
| | - Augustine M K Choi
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, New York; Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York
| | - Jan Krumsiek
- Institute of Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York.
| | - Shahin Rafii
- Department of Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, New York, New York; Ansary Stem Cell Institute, Division of Regenerative Medicine, Department of Medicine, Weill Cornell Medicine, New York, New York.
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Zhang H, Zang C, Xu Z, Zhang Y, Xu J, Bian J, Morozyuk D, Khullar D, Zhang Y, Nordvig AS, Schenck EJ, Shenkman EA, Rothman RL, Block JP, Lyman K, Weiner M, Carton TW, Wang F, Kaushal R. Machine Learning for Identifying Data-Driven Subphenotypes of Incident Post-Acute SARS-CoV-2 Infection Conditions with Large Scale Electronic Health Records: Findings from the RECOVER Initiative. medRxiv 2022. [PMID: 35665007 DOI: 10.1101/2022.05.21.22275412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated, or newly incident in the post-acute SARS-CoV-2 infection period of COVID-19 patients. Most studies have examined these conditions individually without providing concluding evidence on co-occurring conditions. To answer this question, this study leveraged electronic health records (EHRs) from two large clinical research networks from the national Patient-Centered Clinical Research Network (PCORnet) and investigated patients' newly incident diagnoses that appeared within 30 to 180 days after a documented SARS-CoV-2 infection. Through machine learning, we identified four reproducible subphenotypes of PASC dominated by blood and circulatory system, respiratory, musculoskeletal and nervous system, and digestive system problems, respectively. We also demonstrated that these subphenotypes were associated with distinct patterns of patient demographics, underlying conditions present prior to SARS-CoV-2 infection, acute infection phase severity, and use of new medications in the post-acute period. Our study provides novel insights into the heterogeneity of PASC and can inform stratified decision-making in the treatment of COVID-19 patients with PASC conditions.
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Waldrop G, Safavynia SA, Barra ME, Agarwal S, Berlin DA, Boehme AK, Brodie D, Choi JM, Doyle K, Fins JJ, Ganglberger W, Hoffman K, Mittel AM, Roh D, Mukerji SS, Der Nigoghossian C, Park S, Schenck EJ, Salazar‐Schicchi J, Shen Q, Sholle E, Velazquez AG, Walline MC, Westover MB, Brown EN, Victor J, Edlow BL, Schiff ND, Claassen J. Prolonged Unconsciousness is Common in COVID-19 and Associated with Hypoxemia. Ann Neurol 2022; 91:740-755. [PMID: 35254675 PMCID: PMC9082460 DOI: 10.1002/ana.26342] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 02/20/2022] [Accepted: 02/28/2022] [Indexed: 12/16/2022]
Abstract
OBJECTIVE The purpose of this study was to estimate the time to recovery of command-following and associations between hypoxemia with time to recovery of command-following. METHODS In this multicenter, retrospective, cohort study during the initial surge of the United States' pandemic (March-July 2020) we estimate the time from intubation to recovery of command-following, using Kaplan Meier cumulative-incidence curves and Cox proportional hazard models. Patients were included if they were admitted to 1 of 3 hospitals because of severe coronavirus disease 2019 (COVID-19), required endotracheal intubation for at least 7 days, and experienced impairment of consciousness (Glasgow Coma Scale motor score <6). RESULTS Five hundred seventy-one patients of the 795 patients recovered command-following. The median time to recovery of command-following was 30 days (95% confidence interval [CI] = 27-32 days). Median time to recovery of command-following increased by 16 days for patients with at least one episode of an arterial partial pressure of oxygen (PaO2 ) value ≤55 mmHg (p < 0.001), and 25% recovered ≥10 days after cessation of mechanical ventilation. The time to recovery of command-following was associated with hypoxemia (PaO2 ≤55 mmHg hazard ratio [HR] = 0.56, 95% CI = 0.46-0.68; PaO2 ≤70 HR = 0.88, 95% CI = 0.85-0.91), and each additional day of hypoxemia decreased the likelihood of recovery, accounting for confounders including sedation. These findings were confirmed among patients without any imagining evidence of structural brain injury (n = 199), and in a non-overlapping second surge cohort (N = 427, October 2020 to April 2021). INTERPRETATION Survivors of severe COVID-19 commonly recover consciousness weeks after cessation of mechanical ventilation. Long recovery periods are associated with more severe hypoxemia. This relationship is not explained by sedation or brain injury identified on clinical imaging and should inform decisions about life-sustaining therapies. ANN NEUROL 2022;91:740-755.
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Affiliation(s)
- Greer Waldrop
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
- New York Presbyterian HospitalNew YorkNYUSA
| | - Seyed A. Safavynia
- New York Presbyterian HospitalNew YorkNYUSA
- Department of AnesthesiologyWeill Cornell Medical CollegeNew YorkNYUSA
| | - Megan E. Barra
- Department of PharmacyMassachusetts General HospitalBostonMAUSA
- Center for Neurotechnology and NeurorecoveryMassachusetts General HospitalBostonMAUSA
| | - Sachin Agarwal
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
- New York Presbyterian HospitalNew YorkNYUSA
| | - David A. Berlin
- New York Presbyterian HospitalNew YorkNYUSA
- Department of MedicineWeill Cornell Medical CollegeNew YorkNYUSA
| | - Amelia K Boehme
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
| | - Daniel Brodie
- New York Presbyterian HospitalNew YorkNYUSA
- Department of MedicineColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
| | - Jacky M. Choi
- Division of Biostatistics, Department of Population Health SciencesWeill Cornell Medical CollegeNew YorkNYUSA
| | - Kevin Doyle
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
- New York Presbyterian HospitalNew YorkNYUSA
| | - Joseph J. Fins
- New York Presbyterian HospitalNew YorkNYUSA
- Division of Medical Ethics, Department of MedicineWeill Cornell Medical CollegeNew YorkNYUSA
| | - Wolfgang Ganglberger
- Department of NeurologyMassachusetts General Hospital and Harvard Medical SchoolBostonMAUSA
| | - Katherine Hoffman
- Division of Biostatistics, Department of Population Health SciencesWeill Cornell Medical CollegeNew YorkNYUSA
| | - Aaron M. Mittel
- New York Presbyterian HospitalNew YorkNYUSA
- Department of AnesthesiaColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
| | - David Roh
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
- New York Presbyterian HospitalNew YorkNYUSA
| | - Shibani S. Mukerji
- Department of NeurologyMassachusetts General Hospital and Harvard Medical SchoolBostonMAUSA
| | - Caroline Der Nigoghossian
- New York Presbyterian HospitalNew YorkNYUSA
- Department of PharmacyNew York Presbyterian HospitalNew YorkNYUSA
| | - Soojin Park
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
- New York Presbyterian HospitalNew YorkNYUSA
| | - Edward J. Schenck
- New York Presbyterian HospitalNew YorkNYUSA
- Department of MedicineWeill Cornell Medical CollegeNew YorkNYUSA
| | - John Salazar‐Schicchi
- New York Presbyterian HospitalNew YorkNYUSA
- Department of MedicineColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
| | - Qi Shen
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
- New York Presbyterian HospitalNew YorkNYUSA
| | - Evan Sholle
- Information Technologies & Services DepartmentWeill Cornell MedicineNew YorkNYUSA
| | - Angela G. Velazquez
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
- New York Presbyterian HospitalNew YorkNYUSA
| | - Maria C. Walline
- New York Presbyterian HospitalNew YorkNYUSA
- Department of AnesthesiologyWeill Cornell Medical CollegeNew YorkNYUSA
| | - M. Brandon Westover
- Department of NeurologyMassachusetts General Hospital and Harvard Medical SchoolBostonMAUSA
| | - Emery N. Brown
- Department of Brain and Cognitive ScienceInstitute of Medical Engineering and Sciences, the Picower Institute for Learning and Memory, and the Institute for Data Systems and Society, Massachusetts Institute of TechnologyBostonMAUSA
- Department of AnesthesiaCritical Care and Pain Medicine, Massachusetts General HospitalBostonMAUSA
| | - Jonathan Victor
- New York Presbyterian HospitalNew YorkNYUSA
- Department of NeurologyWeill Cornell Medical CollegeNew YorkNYUSA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medical CenterNew YorkNYUSA
| | - Brian L. Edlow
- Center for Neurotechnology and NeurorecoveryMassachusetts General HospitalBostonMAUSA
- Department of NeurologyMassachusetts General Hospital and Harvard Medical SchoolBostonMAUSA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical SchoolBostonMAUSA
| | - Nicholas D. Schiff
- New York Presbyterian HospitalNew YorkNYUSA
- Department of NeurologyWeill Cornell Medical CollegeNew YorkNYUSA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medical CenterNew YorkNYUSA
| | - Jan Claassen
- Department of NeurologyColumbia University Irving Medical Center, Columbia UniversityNew YorkNYUSA
- New York Presbyterian HospitalNew YorkNYUSA
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Gavrielatou E, Vaporidi K, Tsolaki V, Tserlikakis N, Zakynthinos GE, Papoutsi E, Maragkuti A, Mantelou AG, Karayiannis D, Mastora Z, Georgopoulos D, Zakynthinos E, Routsi C, Zakynthinos SG, Schenck EJ, Kotanidou A, Siempos II. Rapidly improving acute respiratory distress syndrome in COVID-19: a multi-centre observational study. Respir Res 2022; 23:94. [PMID: 35422037 PMCID: PMC9008400 DOI: 10.1186/s12931-022-02015-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 04/02/2022] [Indexed: 11/23/2022] Open
Abstract
Background Before the pandemic of coronavirus disease (COVID-19), rapidly improving acute respiratory distress syndrome (ARDS), mostly defined by early extubation, had been recognized as an increasingly prevalent subphenotype (making up 15–24% of all ARDS cases), associated with good prognosis (10% mortality in ARDSNet trials). We attempted to determine the prevalence and prognosis of rapidly improving ARDS and of persistent severe ARDS related to COVID-19. Methods We included consecutive patients with COVID-19 receiving invasive mechanical ventilation in three intensive care units (ICU) during the second pandemic wave in Greece. We defined rapidly improving ARDS as extubation or a partial pressure of arterial oxygen to fraction of inspired oxygen ratio (PaO2:FiO2) greater than 300 on the first day following intubation. We defined persistent severe ARDS as PaO2:FiO2 of equal to or less than 100 on the second day following intubation. Results A total of 280 intubated patients met criteria of ARDS with a median PaO2:FiO2 of 125.0 (interquartile range 93.0–161.0) on day of intubation, and overall ICU-mortality of 52.5% (ranging from 24.3 to 66.9% across the three participating sites). Prevalence of rapidly improving ARDS was 3.9% (11 of 280 patients); no extubation occurred on the first day following intubation. ICU-mortality of patients with rapidly improving ARDS was 54.5%. This low prevalence and high mortality rate of rapidly improving ARDS were consistent across participating sites. Prevalence of persistent severe ARDS was 12.1% and corresponding mortality was 82.4%. Conclusions Rapidly improving ARDS was not prevalent and was not associated with good prognosis among patients with COVID-19. This is starkly different from what has been previously reported for patients with ARDS not related to COVID-19. Our results on both rapidly improving ARDS and persistent severe ARDS may contribute to our understanding of trajectory of ARDS and its association with prognosis in patients with COVID-19. Supplementary Information The online version contains supplementary material available at 10.1186/s12931-022-02015-8.
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Bruce SS, Navi BB, Zhang C, Kim J, Devereux RB, Schenck EJ, Sedrakyan A, Díaz I, Kamel H. Transesophageal echocardiography and risk of respiratory failure in patients who had ischemic stroke or transient ischemic attack: an IDEAL phase 4 study. BMJ Surg Interv Health Technol 2022; 4:e000116. [PMID: 35187480 PMCID: PMC8823208 DOI: 10.1136/bmjsit-2021-000116] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 01/24/2022] [Indexed: 11/17/2022] Open
Abstract
Objective Transesophageal echocardiography (TEE) is sometimes used to search for cardioembolic sources after ischemic stroke or transient ischemic attack (TIA). TEE visualizes some sources better than transthoracic echocardiography, but TEE is invasive and may cause aspiration. Few data exist on the risk of respiratory complications after TEE in patients who had stroke or TIA. Our objective was to determine whether TEE was associated with increased risk of respiratory failure in patients who had ischemic stroke or TIA. Design This is a retrospective cohort study using administrative data from inpatient and outpatient insurance claims collected by the US federal government’s Centers for Medicare and Medicaid Services. Setting Hospitals and outpatient clinics throughout the USA. Participants 99 081 patients ≥65 years old hospitalized for out-of-hospital ischemic stroke or TIA, defined by validated International Classification of Disease-9/10 diagnosis codes and present-on-admission codes, using claims data from 2008 to 2018 in a random 5% sample of Medicare beneficiaries. Main outcome measures Acute respiratory failure, defined as endotracheal intubation and/or mechanical ventilation, starting on the first day after admission through 28 days afterward. Results Of 99 081 patients included in this analysis, 73 733 (74.4%) had an ischemic stroke and 25 348 (25.6%) a TIA. TEE was performed in 4677 (4.7%) patients and intubation and/or mechanical ventilation in 1403 (1.4%) patients. The 28-day cumulative risk of respiratory failure after TEE (1.4%; 95% CI 0.8% to 2.7%) was similar to that seen in those without TEE (1.4%; 95% CI 1.4% to 1.5%) (p=0.84). After adjustment for age, sex, race, Charlson comorbidities, diagnosis of stroke versus TIA, intravenous thrombolysis, and mechanical thrombectomy, TEE was not associated with an increased risk of respiratory failure (HR, 0.9; 95% CI 0.6 to 1.2). Conclusions In a cohort of older patients who had ischemic stroke or TIA, TEE was not associated with an increased risk of subsequent respiratory failure.
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Affiliation(s)
- Samuel S Bruce
- Clinical and Translational Neuroscience Unit, Department of Neurology and Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
| | - Babak B Navi
- Clinical and Translational Neuroscience Unit, Department of Neurology and Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
| | - Cenai Zhang
- Clinical and Translational Neuroscience Unit, Department of Neurology and Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
| | - Jiwon Kim
- Division of Cardiology, Weill Cornell Medicine, New York, New York, USA
| | | | - Edward J Schenck
- Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Art Sedrakyan
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Iván Díaz
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Hooman Kamel
- Clinical and Translational Neuroscience Unit, Department of Neurology and Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
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Reiterer M, Rajan M, Gómez-Banoy N, Lau JD, Gomez-Escobar LG, Ma L, Gilani A, Alvarez-Mulett S, Sholle ET, Chandar V, Bram Y, Hoffman K, Bhardwaj P, Piloco P, Rubio-Navarro A, Uhl S, Carrau L, Houhgton S, Redmond D, Shukla AP, Goyal P, Brown KA, tenOever BR, Alonso LC, Schwartz RE, Schenck EJ, Safford MM, Lo JC. Hyperglycemia in acute COVID-19 is characterized by insulin resistance and adipose tissue infectivity by SARS-CoV-2. Cell Metab 2021; 33:2484. [PMID: 34879241 PMCID: PMC8650200 DOI: 10.1016/j.cmet.2021.10.014] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Long SM, Feit NZ, Chern A, Cooley V, Hill SS, Rajwani K, Schenck EJ, Stiles B, Tassler AB. Percutaneous and Open Tracheostomy in Patients With COVID-19: The Weill Cornell Experience in New York City. Laryngoscope 2021; 131:E2849-E2856. [PMID: 34037983 PMCID: PMC8242792 DOI: 10.1002/lary.29669] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/09/2021] [Accepted: 05/20/2021] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Report long-term tracheostomy outcomes in patients with COVID-19. STUDY DESIGN Review of prospectively collected data. METHODS Prospectively collected data were extracted for adults with COVID-19 undergoing percutaneous or open tracheostomy between April 4, 2020 and June 2, 2020 at a major medical center in New York City. The primary endpoint was weaning from mechanical ventilation. Secondary outcomes included sedation weaning, decannulation, and discharge. RESULTS One hundred one patients underwent tracheostomy, including 48 percutaneous (48%) and 53 open (52%), after a median intubation time of 24 days (IQR 20, 31). The most common complication was minor bleeding (n = 18, 18%). The all-cause mortality rate was 15% and no deaths were attributable to the tracheostomy. Eighty-three patients (82%) were weaned off mechanical ventilation, 88 patients (87%) were weaned off sedation, and 72 patients (71%) were decannulated. Censored median times from tracheostomy to sedation and ventilator weaning were 8 (95% CI 6-11) and 18 (95% CI 14-22) days, respectively (uncensored: 7 and 15 days). Median time from tracheostomy to decannulation was 36 (95% CI 32-47) days (uncensored: 32 days). Of those decannulated, 82% were decannulated during their index admission. There were no differences in outcomes or complication rates between percutaneous and open tracheostomy. Likelihood of discharge from the ICU was inversely related to intubation time, though the clinical relevance of this was small (HR 0.97, 95% CI 0.943-0.998; P = .037). CONCLUSION Tracheostomy by either percutaneous or open technique facilitated sedation and ventilator weaning in patients with COVID-19 after prolonged intubation. Additional study on the optimal timing of tracheostomy in patients with COVID-19 is warranted. LEVEL OF EVIDENCE 3 Laryngoscope, 131:E2849-E2856, 2021.
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Affiliation(s)
- Sallie M. Long
- Department of Otolaryngology—Head and Neck SurgeryNewYork‐Presbyterian Hospital/Weill Cornell MedicineNew YorkNew YorkU.S.A.
| | - Noah Z. Feit
- Weill Cornell Medical CollegeNew YorkNew YorkU.S.A.
| | - Alexander Chern
- Department of Otolaryngology—Head and Neck SurgeryNewYork‐Presbyterian Hospital/Weill Cornell MedicineNew YorkNew YorkU.S.A.
| | - Victoria Cooley
- Division of Biostatistics and EpidemiologyWeill Cornell MedicineNew YorkNew YorkU.S.A.
| | - Shanna S. Hill
- Department of AnesthesiologyNewYork‐Presbyterian Hospital/Weill Cornell MedicineNew YorkNew YorkU.S.A.
| | - Kapil Rajwani
- Division of Pulmonary and Critical Care MedicineNewYork‐Presbyterian Hospital/Weill Cornell MedicineNew YorkNew YorkU.S.A.
| | - Edward J. Schenck
- Division of Pulmonary and Critical Care MedicineNewYork‐Presbyterian Hospital/Weill Cornell MedicineNew YorkNew YorkU.S.A.
| | - Brendon Stiles
- Department of Cardiothoracic SurgeryNewYork‐Presbyterian Hospital/Weill Cornell MedicineNew YorkNew YorkU.S.A.
| | - Andrew B. Tassler
- Department of Otolaryngology—Head and Neck SurgeryNewYork‐Presbyterian Hospital/Weill Cornell MedicineNew YorkNew YorkU.S.A.
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Reiterer M, Rajan M, Gómez-Banoy N, Lau JD, Gomez-Escobar LG, Ma L, Gilani A, Alvarez-Mulett S, Sholle ET, Chandar V, Bram Y, Hoffman K, Bhardwaj P, Piloco P, Rubio-Navarro A, Uhl S, Carrau L, Houhgton S, Redmond D, Shukla AP, Goyal P, Brown KA, tenOever BR, Alonso LC, Schwartz RE, Schenck EJ, Safford MM, Lo JC. Hyperglycemia in acute COVID-19 is characterized by insulin resistance and adipose tissue infectivity by SARS-CoV-2. Cell Metab 2021; 33:2174-2188.e5. [PMID: 34599884 PMCID: PMC8443335 DOI: 10.1016/j.cmet.2021.09.009] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 07/15/2021] [Accepted: 09/13/2021] [Indexed: 01/08/2023]
Abstract
Individuals infected with SARS-CoV-2 who also display hyperglycemia suffer from longer hospital stays, higher risk of developing acute respiratory distress syndrome (ARDS), and increased mortality. Nevertheless, the pathophysiological mechanism of hyperglycemia in COVID-19 remains poorly characterized. Here, we show that hyperglycemia is similarly prevalent among patients with ARDS independent of COVID-19 status. Yet among patients with ARDS and COVID-19, insulin resistance is the prevalent cause of hyperglycemia, independent of glucocorticoid treatment, which is unlike patients with ARDS but without COVID-19, where pancreatic beta cell failure predominates. A screen of glucoregulatory hormones revealed lower levels of adiponectin in patients with COVID-19. Hamsters infected with SARS-CoV-2 demonstrated a strong antiviral gene expression program in the adipose tissue and diminished expression of adiponectin. Moreover, we show that SARS-CoV-2 can infect adipocytes. Together these data suggest that SARS-CoV-2 may trigger adipose tissue dysfunction to drive insulin resistance and adverse outcomes in acute COVID-19.
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Affiliation(s)
- Moritz Reiterer
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Mangala Rajan
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Nicolás Gómez-Banoy
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Jennifer D Lau
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Luis G Gomez-Escobar
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Lunkun Ma
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Ankit Gilani
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Sergio Alvarez-Mulett
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Evan T Sholle
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Vasuretha Chandar
- Division of Gastroenterology and Hepatology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Yaron Bram
- Division of Gastroenterology and Hepatology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Katherine Hoffman
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Priya Bhardwaj
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Phoebe Piloco
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Alfonso Rubio-Navarro
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Skyler Uhl
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lucia Carrau
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sean Houhgton
- Division of Regenerative Medicine, Ansary Stem Cell Institute, Weill Cornell Medicine, New York, NY, USA
| | - David Redmond
- Division of Regenerative Medicine, Ansary Stem Cell Institute, Weill Cornell Medicine, New York, NY, USA
| | - Alpana P Shukla
- Weill Center for Metabolic Health, Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Parag Goyal
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Kristy A Brown
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA; Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Benjamin R tenOever
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Laura C Alonso
- Weill Center for Metabolic Health, Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Robert E Schwartz
- Division of Gastroenterology and Hepatology, Departments of Medicine and Physiology, Biophysics and Systems Biology, Weill Cornell Medicine, New York, NY, USA
| | - Edward J Schenck
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Monika M Safford
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - James C Lo
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
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Schenck EJ, Hoffman KL, Oromendia C, Sanchez E, Finkelsztein EJ, Hong KS, Kabariti J, Torres LK, Harrington JS, Siempos II, Choi AMK, Campion TR. A Comparative Analysis of the Respiratory Subscore of the Sequential Organ Failure Assessment Scoring System. Ann Am Thorac Soc 2021; 18:1849-1860. [PMID: 33760709 PMCID: PMC8641830 DOI: 10.1513/annalsats.202004-399oc] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 03/23/2021] [Indexed: 11/20/2022] Open
Abstract
Rationale: The Sequential Organ Failure Assessment (SOFA) tool is a commonly used measure of illness severity. Calculation of the respiratory subscore of SOFA is frequently limited by missing arterial oxygen pressure (PaO2) data. Although missing PaO2 data are commonly replaced with normal values, the performance of different methods of substituting PaO2 for SOFA calculation is unclear. Objectives: The study objective was to compare the performance of different substitution strategies for missing PaO2 data for SOFA score calculation. Methods: This retrospective cohort study was performed using the Weill Cornell Critical Care Database for Advanced Research from a tertiary care hospital in the United States. All adult patients admitted to an intensive care unit (ICU) from 2011 to 2019 with an available respiratory SOFA score were included. We analyzed the availability of the PaO2/fraction of inspired oxygen (FiO2) ratio on the first day of ICU admission. In those without a PaO2/FiO2 ratio available, the ratio of oxygen saturation as measured by pulse oximetry to FiO2 was used to calculate a respiratory SOFA subscore according to four methods (linear substitution [Rice], nonlinear substitution [Severinghaus], modified respiratory SOFA, and multiple imputation by chained equations [MICE]) as well as the missing-as-normal technique. We then compared how well the different total SOFA scores discriminated in-hospital mortality. We performed several subgroup and sensitivity analyses. Results: We identified 35,260 unique visits, of which 9,172 included predominant respiratory failure. PaO2 data were available for 14,939 (47%). The area under the receiver operating characteristic curve for each substitution technique for discriminating in-hospital mortality was higher than that for the missing-as-normal technique (0.78 [0.77-0.79]) in all analyses (modified, 0.80 [0.79-0.81]; Rice, 0.80 [0.79-0.81]; Severinghaus, 0.80 [0.79-0.81]; and MICE, 0.80 [0.79-0.81]) (P < 0.01). Each substitution method had a higher accuracy for discriminating in-hospital mortality (MICE, 0.67; Rice, 0.67; modified, 0.66; and Severinghaus, 0.66) than the missing-as-normal technique. Model calibration for in-hospital mortality was less precise for the missing-as-normal technique than for the other substitution techniques at the lower range of SOFA and among the subgroups. Conclusions: Using physiologic and statistical substitution methods improved the total SOFA score's ability to discriminate mortality compared with the missing-as-normal technique. Treating missing data as normal may result in underreporting the severity of illness compared with using substitution. The simplicity of a direct oxygen saturation as measured by pulse oximetry/FiO2 ratio-modified SOFA technique makes it an attractive choice for electronic health record-based research. This knowledge can inform comparisons of severity of illness across studies that used different techniques.
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Affiliation(s)
- Edward J Schenck
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine
- NewYork-Presbyterian Hospital, Weill Cornell Medicine, New York, New York; and
| | | | | | - Elizabeth Sanchez
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine
| | - Eli J Finkelsztein
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine
| | - Kyung Sook Hong
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine
- Department of Surgery and Critical Care Medicine, College of Medicine, Ewha Womans University, Seoul, Republic of Korea
| | | | - Lisa K Torres
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine
- NewYork-Presbyterian Hospital, Weill Cornell Medicine, New York, New York; and
| | - John S Harrington
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine
- NewYork-Presbyterian Hospital, Weill Cornell Medicine, New York, New York; and
| | - Ilias I Siempos
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine
| | - Augustine M K Choi
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine
- NewYork-Presbyterian Hospital, Weill Cornell Medicine, New York, New York; and
| | - Thomas R Campion
- Department of Population Health Sciences
- Information Technologies and Services, and
- Clinical and Translational Science Center, Weill Cornell Medicine, Cornell University, New York, New York
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Affiliation(s)
- Iván Díaz
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York
| | - Nicholas Williams
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York
| | - Katherine L. Hoffman
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York
| | - Edward J. Schenck
- Division of Pulmonary & Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York
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Su C, Xu Z, Hoffman K, Goyal P, Safford MM, Lee J, Alvarez-Mulett S, Gomez-Escobar L, Price DR, Harrington JS, Torres LK, Martinez FJ, Campion TR, Wang F, Schenck EJ. Identifying organ dysfunction trajectory-based subphenotypes in critically ill patients with COVID-19. Sci Rep 2021; 11:15872. [PMID: 34354174 PMCID: PMC8342520 DOI: 10.1038/s41598-021-95431-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 07/14/2021] [Indexed: 12/13/2022] Open
Abstract
COVID-19-associated respiratory failure offers the unprecedented opportunity to evaluate the differential host response to a uniform pathogenic insult. Understanding whether there are distinct subphenotypes of severe COVID-19 may offer insight into its pathophysiology. Sequential Organ Failure Assessment (SOFA) score is an objective and comprehensive measurement that measures dysfunction severity of six organ systems, i.e., cardiovascular, central nervous system, coagulation, liver, renal, and respiration. Our aim was to identify and characterize distinct subphenotypes of COVID-19 critical illness defined by the post-intubation trajectory of SOFA score. Intubated COVID-19 patients at two hospitals in New York city were leveraged as development and validation cohorts. Patients were grouped into mild, intermediate, and severe strata by their baseline post-intubation SOFA. Hierarchical agglomerative clustering was performed within each stratum to detect subphenotypes based on similarities amongst SOFA score trajectories evaluated by Dynamic Time Warping. Distinct worsening and recovering subphenotypes were identified within each stratum, which had distinct 7-day post-intubation SOFA progression trends. Patients in the worsening suphenotypes had a higher mortality than those in the recovering subphenotypes within each stratum (mild stratum, 29.7% vs. 10.3%, p = 0.033; intermediate stratum, 29.3% vs. 8.0%, p = 0.002; severe stratum, 53.7% vs. 22.2%, p < 0.001). Pathophysiologic biomarkers associated with progression were distinct at each stratum, including findings suggestive of inflammation in low baseline severity of illness versus hemophagocytic lymphohistiocytosis in higher baseline severity of illness. The findings suggest that there are clear worsening and recovering subphenotypes of COVID-19 respiratory failure after intubation, which are more predictive of outcomes than baseline severity of illness. Distinct progression biomarkers at differential baseline severity of illness suggests a heterogeneous pathobiology in the progression of COVID-19 respiratory failure.
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Affiliation(s)
- Chang Su
- Department of Population Health Sciences, Weill Cornell Medicine, 425 E 61 St., New York, NY, 10065, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, 425 E 61 St., New York, NY, 10065, USA
| | - Katherine Hoffman
- Department of Population Health Sciences, Weill Cornell Medicine, 425 E 61 St., New York, NY, 10065, USA
| | - Parag Goyal
- Division of General Internal Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- New York-Presbyterian Hospital, Weill Cornell Medicine, 1300 York Ave., Box 96, New York, NY, 10065, USA
| | - Monika M Safford
- Division of General Internal Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- New York-Presbyterian Hospital, Weill Cornell Medicine, 1300 York Ave., Box 96, New York, NY, 10065, USA
| | - Jerry Lee
- Weill Cornell Medical College, Weill Cornell Medicine, New York, NY, USA
| | - Sergio Alvarez-Mulett
- New York-Presbyterian Hospital, Weill Cornell Medicine, 1300 York Ave., Box 96, New York, NY, 10065, USA
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Luis Gomez-Escobar
- New York-Presbyterian Hospital, Weill Cornell Medicine, 1300 York Ave., Box 96, New York, NY, 10065, USA
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - David R Price
- New York-Presbyterian Hospital, Weill Cornell Medicine, 1300 York Ave., Box 96, New York, NY, 10065, USA
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - John S Harrington
- New York-Presbyterian Hospital, Weill Cornell Medicine, 1300 York Ave., Box 96, New York, NY, 10065, USA
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Lisa K Torres
- New York-Presbyterian Hospital, Weill Cornell Medicine, 1300 York Ave., Box 96, New York, NY, 10065, USA
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Fernando J Martinez
- New York-Presbyterian Hospital, Weill Cornell Medicine, 1300 York Ave., Box 96, New York, NY, 10065, USA
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Thomas R Campion
- Department of Population Health Sciences, Weill Cornell Medicine, 425 E 61 St., New York, NY, 10065, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, 425 E 61 St., New York, NY, 10065, USA.
| | - Edward J Schenck
- New York-Presbyterian Hospital, Weill Cornell Medicine, 1300 York Ave., Box 96, New York, NY, 10065, USA.
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
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Plataki M, Pan D, Goyal P, Hoffman K, Choi JMK, Huang H, Safford MM, Schenck EJ. Association of body mass index with morbidity in patients hospitalised with COVID-19. BMJ Open Respir Res 2021; 8:e000970. [PMID: 34417256 PMCID: PMC8382668 DOI: 10.1136/bmjresp-2021-000970] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 08/07/2021] [Indexed: 12/15/2022] Open
Abstract
PURPOSE To evaluate the association between body mass index (BMI) and clinical outcomes other than death in patients hospitalised and intubated with COVID-19. METHODS This is a single-centre cohort study of adults with COVID-19 admitted to New York Presbyterian Hospital-Weill Cornell Medicine from 3 March 2020 through 15 May 2020. Baseline and outcome variables, as well as lab and ventilatory parameters, were generated for the admitted and intubated cohorts after stratifying by BMI category. Linear regression models were used for continuous, and logistic regression models were used for categorical outcomes. RESULTS The study included 1337 admitted patients with a subset of 407 intubated patients. Among admitted patients, hospital length of stay (LOS) and home discharge was not significantly different across BMI categories independent of demographic characteristics and comorbidities. In the intubated cohort, there was no difference in in-hospital events and treatments, including renal replacement therapy, neuromuscular blockade and prone positioning. Ventilatory ratio was higher with increasing BMI on days 1, 3 and 7. There was no significant difference in ventilator free days (VFD) at 28 or 60 days, need for tracheostomy, hospital LOS, and discharge disposition based on BMI in the intubated cohort after adjustment. CONCLUSIONS In our COVID-19 population, there was no association between obesity and morbidity outcomes, such as hospital LOS, home discharge or VFD. Further research is needed to clarify the mechanisms underlying the reported effects of BMI on outcomes, which may be population dependent.
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Affiliation(s)
- Maria Plataki
- Department of Medicine, Division of Pulmonary Critical Care, New York Presbyterian Hospital - Weill Cornell Medicine, New York, New York, USA
| | - Di Pan
- Department of Medicine, Division of Pulmonary Critical Care, New York Presbyterian Hospital - Weill Cornell Medicine, New York, New York, USA
| | - Parag Goyal
- Department of Medicine, Division of Cardiology, New York Presbyterian Hospital - Weill Cornell Medicine, New York, New York, USA
- Department of Medicine, Division of General Internal Medicine, New York Presbyterian Hospital - Weill Cornell Medicine, New York, New York, USA
| | - Katherine Hoffman
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, New York, USA
| | - Jacky Man Kwan Choi
- Department of Population Health Sciences, Division of Biostatistics, Weill Cornell Medicine, New York, New York, USA
| | - Hao Huang
- Department of Medicine, Division of General Internal Medicine, New York Presbyterian Hospital - Weill Cornell Medicine, New York, New York, USA
| | - Monika M Safford
- Department of Medicine, Division of General Internal Medicine, New York Presbyterian Hospital - Weill Cornell Medicine, New York, New York, USA
| | - Edward J Schenck
- Department of Medicine, Division of Pulmonary Critical Care, New York Presbyterian Hospital - Weill Cornell Medicine, New York, New York, USA
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Khemani RG, Lee JT, Wu D, Schenck EJ, Hayes MM, Kritek PA, Mutlu GM, Gershengorn HB, Coudroy R. Update in Critical Care 2020. Am J Respir Crit Care Med 2021; 203:1088-1098. [PMID: 33734938 DOI: 10.1164/rccm.202102-0336up] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Robinder G Khemani
- Pediatric ICU, Department of Anesthesiology and Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, California.,Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Jessica T Lee
- Division of Pulmonary, Allergy and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - David Wu
- Section of Pulmonary and Critical Care, Department of Medicine, University of Chicago, Chicago, Illinois
| | - Edward J Schenck
- Division of Pulmonary and Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, New York.,NewYork-Presbyterian Hospital, Weill Cornell Medical Center, New York, New York
| | - Margaret M Hayes
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts
| | - Patricia A Kritek
- Division of Pulmonary, Critical Care and Sleep Medicine, School of Medicine, University of Washington Seattle, Washington
| | - Gökhan M Mutlu
- Section of Pulmonary and Critical Care, Department of Medicine, University of Chicago, Chicago, Illinois
| | - Hayley B Gershengorn
- Division of Pulmonary, Critical Care, and Sleep Medicine, Miller School of Medicine, University of Miami, Miami, Florida.,Division of Critical Care Medicine, Albert Einstein College of Medicine, Bronx, New York
| | - Rémi Coudroy
- Institut National de la Santé et de la Recherche Médicale, Poitiers, France; and.,Médecine Intensive Réanimation, Centre Hospitalier Universitaire de Poitiers, Poitiers, France
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38
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Su C, Zhang Y, Flory JH, Weiner MG, Kaushal R, Schenck EJ, Wang F. Clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health. NPJ Digit Med 2021; 4:110. [PMID: 34262117 PMCID: PMC8280198 DOI: 10.1038/s41746-021-00481-w] [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] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/21/2021] [Indexed: 02/08/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) is heterogeneous and our understanding of the biological mechanisms of host response to the viral infection remains limited. Identification of meaningful clinical subphenotypes may benefit pathophysiological study, clinical practice, and clinical trials. Here, our aim was to derive and validate COVID-19 subphenotypes using machine learning and routinely collected clinical data, assess temporal patterns of these subphenotypes during the pandemic course, and examine their interaction with social determinants of health (SDoH). We retrospectively analyzed 14418 COVID-19 patients in five major medical centers in New York City (NYC), between March 1 and June 12, 2020. Using clustering analysis, 4 biologically distinct subphenotypes were derived in the development cohort (N = 8199). Importantly, the identified subphenotypes were highly predictive of clinical outcomes (especially 60-day mortality). Sensitivity analyses in the development cohort, and rederivation and prediction in the internal (N = 3519) and external (N = 3519) validation cohorts confirmed the reproducibility and usability of the subphenotypes. Further analyses showed varying subphenotype prevalence across the peak of the outbreak in NYC. We also found that SDoH specifically influenced mortality outcome in Subphenotype IV, which is associated with older age, worse clinical manifestation, and high comorbidity burden. Our findings may lead to a better understanding of how COVID-19 causes disease in different populations and potentially benefit clinical trial development. The temporal patterns and SDoH implications of the subphenotypes may add insights to health policy to reduce social disparity in the pandemic.
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Affiliation(s)
- Chang Su
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Yongkang Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - James H Flory
- Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Mark G Weiner
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Rainu Kaushal
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
- New York-Presbyterian Hospital, Weill Cornell Medicine, New York, NY, USA.
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA.
| | - Edward J Schenck
- New York-Presbyterian Hospital, Weill Cornell Medicine, New York, NY, USA.
- Division of Pulmonary & Critical Care Medicine, Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
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Gómez-Escobar LG, Hoffman KL, Choi JJ, Borczuk A, Salvatore S, Alvarez-Mulett SL, Galvan MD, Zhao Z, Racine-Brzostek SE, Yang HS, Stout-Delgado HW, Choi ME, Choi AMK, Cho SJ, Schenck EJ. Cytokine signatures of end organ injury in COVID-19. Sci Rep 2021; 11:12606. [PMID: 34131192 PMCID: PMC8206105 DOI: 10.1038/s41598-021-91859-z] [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] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 05/26/2021] [Indexed: 02/08/2023] Open
Abstract
Increasing evidence has shown that Coronavirus disease 19 (COVID-19) severity is driven by a dysregulated immunologic response. We aimed to assess the differences in inflammatory cytokines in COVID-19 patients compared to contemporaneously hospitalized controls and then analyze the relationship between these cytokines and the development of Acute Respiratory Distress Syndrome (ARDS), Acute Kidney Injury (AKI) and mortality. In this cohort study of hospitalized patients, done between March third, 2020 and April first, 2020 at a quaternary referral center in New York City we included adult hospitalized patients with COVID-19 and negative controls. Serum specimens were obtained on the first, second, and third hospital day and cytokines were measured by Luminex. Autopsies of nine cohort patients were examined. We identified 90 COVID-19 patients and 51 controls. Analysis of 48 inflammatory cytokines revealed upregulation of macrophage induced chemokines, T-cell related interleukines and stromal cell producing cytokines in COVID-19 patients compared to the controls. Moreover, distinctive cytokine signatures predicted the development of ARDS, AKI and mortality in COVID-19 patients. Specifically, macrophage-associated cytokines predicted ARDS, T cell immunity related cytokines predicted AKI and mortality was associated with cytokines of activated immune pathways, of which IL-13 was universally correlated with ARDS, AKI and mortality. Histopathological examination of the autopsies showed diffuse alveolar damage with significant mononuclear inflammatory cell infiltration. Additionally, the kidneys demonstrated glomerular sclerosis, tubulointerstitial lymphocyte infiltration and cortical and medullary atrophy. These patterns of cytokine expression offer insight into the pathogenesis of COVID-19 disease, its severity, and subsequent lung and kidney injury suggesting more targeted treatment strategies.
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Affiliation(s)
- Luis G Gómez-Escobar
- Division of Pulmonary and Critical Care Medicine, Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Katherine L Hoffman
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Justin J Choi
- Division of General Internal Medicine, Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medicine, New York, NY, USA
| | - Alain Borczuk
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medicine, New York, NY, USA
| | - Steven Salvatore
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medicine, New York, NY, USA
| | - Sergio L Alvarez-Mulett
- Division of Pulmonary and Critical Care Medicine, Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Manuel D Galvan
- Advanced Diagnostics Complement Laboratory, National Jewish Health, Denver, CO, USA
| | - Zhen Zhao
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medicine, New York, NY, USA
| | - Sabrina E Racine-Brzostek
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medicine, New York, NY, USA
| | - He S Yang
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medicine, New York, NY, USA
| | - Heather W Stout-Delgado
- Division of Pulmonary and Critical Care Medicine, Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Mary E Choi
- Division of Nephrology and Hypertension, Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medicine, New York, NY, USA
| | - Augustine M K Choi
- Division of Pulmonary and Critical Care Medicine, Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medicine, New York, NY, USA
| | - Soo Jung Cho
- Division of Pulmonary and Critical Care Medicine, Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
- Department of Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medicine, New York, NY, USA.
| | - Edward J Schenck
- Division of Pulmonary and Critical Care Medicine, Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
- Department of Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medicine, New York, NY, USA.
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Torres LK, Hoffman KL, Oromendia C, Diaz I, Harrington JS, Schenck EJ, Price DR, Gomez-Escobar L, Higuera A, Vera MP, Baron RM, Fredenburgh LE, Huh JW, Choi AMK, Siempos II. Attributable mortality of acute respiratory distress syndrome: a systematic review, meta-analysis and survival analysis using targeted minimum loss-based estimation. Thorax 2021; 76:1176-1185. [PMID: 33863829 DOI: 10.1136/thoraxjnl-2020-215950] [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] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 03/15/2021] [Accepted: 03/24/2021] [Indexed: 01/01/2023]
Abstract
BACKGROUND Although acute respiratory distress syndrome (ARDS) is associated with high mortality, its direct causal link with death is unclear. Clarifying this link is important to justify costly research on prevention of ARDS. OBJECTIVE To estimate the attributable mortality, if any, of ARDS. DESIGN First, we performed a systematic review and meta-analysis of observational studies reporting mortality of critically ill patients with and without ARDS matched for underlying risk factor. Next, we conducted a survival analysis of prospectively collected patient-level data from subjects enrolled in three intensive care unit (ICU) cohorts to estimate the attributable mortality of critically ill septic patients with and without ARDS using a novel causal inference method. RESULTS In the meta-analysis, 44 studies (47 cohorts) involving 56 081 critically ill patients were included. Mortality was higher in patients with versus without ARDS (risk ratio 2.48, 95% CI 1.86 to 3.30; p<0.001) with a numerically stronger association between ARDS and mortality in trauma than sepsis. In the survival analysis of three ICU cohorts enrolling 1203 critically ill patients, 658 septic patients were included. After controlling for confounders, ARDS was found to increase the mortality rate by 15% (95% CI 3% to 26%; p=0.015). Significant increases in mortality were seen for severe (23%, 95% CI 3% to 44%; p=0.028) and moderate (16%, 95% CI 2% to 31%; p=0.031), but not for mild ARDS. CONCLUSIONS ARDS has a direct causal link with mortality. Our findings provide information about the extent to which continued funding of ARDS prevention trials has potential to impart survival benefit. PROSPERO REGISTRATION NUMBER CRD42017078313.
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Affiliation(s)
- Lisa K Torres
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medical Center, New York, New York, USA
| | - Katherine L Hoffman
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Clara Oromendia
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Ivan Diaz
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - John S Harrington
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medical Center, New York, New York, USA
| | - Edward J Schenck
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medical Center, New York, New York, USA
| | - David R Price
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medical Center, New York, New York, USA
| | - Luis Gomez-Escobar
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medical Center, New York, New York, USA
| | - Angelica Higuera
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Mayra Pinilla Vera
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Rebecca M Baron
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Laura E Fredenburgh
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Jin-Won Huh
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center/University of Ulsan College of Medicine, Seoul, South Korea
| | - Augustine M K Choi
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medical Center, New York, New York, USA
| | - Ilias I Siempos
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, NewYork-Presbyterian Hospital/Weill Cornell Medical Center, New York, New York, USA .,First Department of Critical Care Medicine and Pulmonary Services, Evangelismos Athens General Hospital/National and Kapodistrian University of Athens Medical School, Athens, Greece
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Schenck EJ, Hoffman KL, Cusick M, Kabariti J, Sholle ET, Campion TR. Critical carE Database for Advanced Research (CEDAR): An automated method to support intensive care units with electronic health record data. J Biomed Inform 2021; 118:103789. [PMID: 33862230 DOI: 10.1016/j.jbi.2021.103789] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 02/12/2021] [Accepted: 04/10/2021] [Indexed: 12/28/2022]
Abstract
Patients treated in an intensive care unit (ICU) are critically ill and require life-sustaining organ failure support. Existing critical care data resources are limited to a select number of institutions, contain only ICU data, and do not enable the study of local changes in care patterns. To address these limitations, we developed the Critical carE Database for Advanced Research (CEDAR), a method for automating extraction and transformation of data from an electronic health record (EHR) system. Compared to an existing gold standard of manually collected data at our institution, CEDAR was statistically similar in most measures, including patient demographics and sepsis-related organ failure assessment (SOFA) scores. Additionally, CEDAR automated data extraction obviated the need for manual collection of 550 variables. Critically, during the spring 2020 COVID-19 surge in New York City, a modified version of CEDAR supported pandemic response efforts, including clinical operations and research. Other academic medical centers may find value in using the CEDAR method to automate data extraction from EHR systems to support ICU activities.
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Affiliation(s)
- Edward J Schenck
- Weill Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Katherine L Hoffman
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Marika Cusick
- Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, United States
| | - Joseph Kabariti
- Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, United States
| | - Evan T Sholle
- Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, United States
| | - Thomas R Campion
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States; Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, United States; Department of Pediatrics, Weill Cornell Medicine, New York, NY, United States; Clinical & Translational Science Center, Weill Cornell Medicine, New York, NY, United States
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42
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Reiterer M, Rajan M, Gómez-Banoy N, Lau JD, Gomez-Escobar LG, Gilani A, Alvarez-Mulett S, Sholle ET, Chandar V, Bram Y, Hoffman K, Rubio-Navarro A, Uhl S, Shukla AP, Goyal P, tenOever BR, Alonso LC, Schwartz RE, Schenck EJ, Safford MM, Lo JC. Hyperglycemia in Acute COVID-19 is Characterized by Adipose Tissue Dysfunction and Insulin Resistance. medRxiv 2021:2021.03.21.21254072. [PMID: 33791724 PMCID: PMC8010756 DOI: 10.1101/2021.03.21.21254072] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
COVID-19 has proven to be a metabolic disease resulting in adverse outcomes in individuals with diabetes or obesity. Patients infected with SARS-CoV-2 and hyperglycemia suffer from longer hospital stays, higher risk of developing acute respiratory distress syndrome (ARDS), and increased mortality compared to those who do not develop hyperglycemia. Nevertheless, the pathophysiological mechanism(s) of hyperglycemia in COVID-19 remains poorly characterized. Here we show that insulin resistance rather than pancreatic beta cell failure is the prevalent cause of hyperglycemia in COVID-19 patients with ARDS, independent of glucocorticoid treatment. A screen of protein hormones that regulate glucose homeostasis reveals that the insulin sensitizing adipokine adiponectin is reduced in hyperglycemic COVID-19 patients. Hamsters infected with SARS-CoV-2 also have diminished expression of adiponectin. Together these data suggest that adipose tissue dysfunction may be a driver of insulin resistance and adverse outcomes in acute COVID-19.
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Affiliation(s)
- Moritz Reiterer
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Mangala Rajan
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Nicolás Gómez-Banoy
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Jennifer D. Lau
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Luis G. Gomez-Escobar
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Ankit Gilani
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Sergio Alvarez-Mulett
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Evan T. Sholle
- Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, USA
| | - Vasuretha Chandar
- Division of Gastroenterology and Hepatology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Yaron Bram
- Division of Gastroenterology and Hepatology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Katherine Hoffman
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Alfonso Rubio-Navarro
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Skyler Uhl
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alpana P. Shukla
- Weill Center for Metabolic Health, Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Parag Goyal
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Benjamin R. tenOever
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Laura C. Alonso
- Weill Center for Metabolic Health, Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Robert E. Schwartz
- Division of Gastroenterology and Hepatology, Departments of Medicine and Physiology, Biophysics and Systems Biology, Weill Cornell Medicine, New York, NY, USA
| | - Edward J. Schenck
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | | | - James C. Lo
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
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43
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Butler D, Mozsary C, Meydan C, Foox J, Rosiene J, Shaiber A, Danko D, Afshinnekoo E, MacKay M, Sedlazeck FJ, Ivanov NA, Sierra M, Pohle D, Zietz M, Gisladottir U, Ramlall V, Sholle ET, Schenck EJ, Westover CD, Hassan C, Ryon K, Young B, Bhattacharya C, Ng DL, Granados AC, Santos YA, Servellita V, Federman S, Ruggiero P, Fungtammasan A, Chin CS, Pearson NM, Langhorst BW, Tanner NA, Kim Y, Reeves JW, Hether TD, Warren SE, Bailey M, Gawrys J, Meleshko D, Xu D, Couto-Rodriguez M, Nagy-Szakal D, Barrows J, Wells H, O'Hara NB, Rosenfeld JA, Chen Y, Steel PAD, Shemesh AJ, Xiang J, Thierry-Mieg J, Thierry-Mieg D, Iftner A, Bezdan D, Sanchez E, Campion TR, Sipley J, Cong L, Craney A, Velu P, Melnick AM, Shapira S, Hajirasouliha I, Borczuk A, Iftner T, Salvatore M, Loda M, Westblade LF, Cushing M, Wu S, Levy S, Chiu C, Schwartz RE, Tatonetti N, Rennert H, Imielinski M, Mason CE. Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions. Nat Commun 2021; 12:1660. [PMID: 33712587 PMCID: PMC7954844 DOI: 10.1038/s41467-021-21361-7] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 01/25/2021] [Indexed: 02/08/2023] Open
Abstract
In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin-angiotensin-aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.
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Affiliation(s)
- Daniel Butler
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Christopher Mozsary
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Cem Meydan
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA
| | - Jonathan Foox
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Joel Rosiene
- New York Genome Center, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Alon Shaiber
- New York Genome Center, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine and the Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - David Danko
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Ebrahim Afshinnekoo
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA
| | - Matthew MacKay
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Nikolay A Ivanov
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Clinical & Translational Science Center, Weill Cornell Medicine, New York, NY, USA
| | - Maria Sierra
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Diana Pohle
- Institute of Medical Virology and Epidemiology of Viral Diseases, University Hospital Tuebingen, Tuebingen, Germany
| | - Michael Zietz
- Department of Biomedical Informatics, Department of Systems Biology, Department of Medicine, Institute for Genomic Medicine, Columbia University, Columbia, NY, USA
| | - Undina Gisladottir
- Department of Biomedical Informatics, Department of Systems Biology, Department of Medicine, Institute for Genomic Medicine, Columbia University, Columbia, NY, USA
| | - Vijendra Ramlall
- Department of Biomedical Informatics, Department of Systems Biology, Department of Medicine, Institute for Genomic Medicine, Columbia University, Columbia, NY, USA
- Department of Cellular, Molecular Physiology & Biophysics, Columbia University, Columbia, NY, USA
| | - Evan T Sholle
- Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, USA
| | - Edward J Schenck
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Craig D Westover
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Ciaran Hassan
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Krista Ryon
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Benjamin Young
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | | | - Dianna L Ng
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
| | - Andrea C Granados
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
- UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA, USA
| | - Yale A Santos
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
- UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA, USA
| | - Venice Servellita
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
- UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA, USA
| | - Scot Federman
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
- UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA, USA
| | - Phyllis Ruggiero
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | | | | | | | | | | | | | | | | | | | | | - Justyna Gawrys
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Dmitry Meleshko
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Tri-Institutional Computational Biology & Medicine Program, Weill Cornell Medicine, New York, NY, USA
| | - Dong Xu
- Genomics Resources Core Facility, Weill Cornell Medicine, New York, NY, USA
| | | | - Dorottya Nagy-Szakal
- Biotia, Inc., New York, NY, USA
- Department of Cell Biology, SUNY Downstate Health Sciences University, New York, NY, USA
| | | | | | - Niamh B O'Hara
- Biotia, Inc., New York, NY, USA
- Department of Cell Biology, SUNY Downstate Health Sciences University, New York, NY, USA
| | - Jeffrey A Rosenfeld
- Rutgers Cancer Institute of New Jersey, New York, NJ, USA
- Department of Pathology, Robert Wood Johnson Medical School, New York, NJ, USA
| | - Ying Chen
- Rutgers Cancer Institute of New Jersey, New York, NJ, USA
| | - Peter A D Steel
- Department of Emergency Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Amos J Shemesh
- Department of Emergency Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Jenny Xiang
- Genomics Resources Core Facility, Weill Cornell Medicine, New York, NY, USA
| | - Jean Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Danielle Thierry-Mieg
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Angelika Iftner
- Institute of Medical Virology and Epidemiology of Viral Diseases, University Hospital Tuebingen, Tuebingen, Germany
| | - Daniela Bezdan
- Institute of Medical Virology and Epidemiology of Viral Diseases, University Hospital Tuebingen, Tuebingen, Germany
| | | | - Thomas R Campion
- Information Technologies & Services Department, Weill Cornell Medicine, New York, NY, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - John Sipley
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Lin Cong
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Arryn Craney
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Priya Velu
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Ari M Melnick
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Sagi Shapira
- Department of Biomedical Informatics, Department of Systems Biology, Department of Medicine, Institute for Genomic Medicine, Columbia University, Columbia, NY, USA
| | - Iman Hajirasouliha
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine and the Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Alain Borczuk
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Thomas Iftner
- Institute of Medical Virology and Epidemiology of Viral Diseases, University Hospital Tuebingen, Tuebingen, Germany
| | - Mirella Salvatore
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Massimo Loda
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Lars F Westblade
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Melissa Cushing
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Shixiu Wu
- Hangzhou Cancer Institute, Hangzhou Cancer Hospital, Hangzhou, China
- Department of Radiation Oncology, Hangzhou Cancer Hospital, Hangzhou, China
| | - Shawn Levy
- HudsonAlpha Discovery Institute, Huntsville, AL, USA
| | - Charles Chiu
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
- UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA, USA
- Department of Medicine, Division of Infectious Diseases, University of California, San Francisco, CA, USA
| | | | - Nicholas Tatonetti
- Department of Biomedical Informatics, Department of Systems Biology, Department of Medicine, Institute for Genomic Medicine, Columbia University, Columbia, NY, USA.
| | - Hanna Rennert
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
| | - Marcin Imielinski
- New York Genome Center, New York, NY, USA.
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
- Englander Institute for Precision Medicine and the Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
- WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA.
- The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
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44
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Price DR, Hoffman KL, Oromendia C, Torres LK, Schenck EJ, Choi ME, Choi AMK, Baron RM, Huh JW, Siempos II. Effect of Neutropenic Critical Illness on Development and Prognosis of Acute Respiratory Distress Syndrome. Am J Respir Crit Care Med 2021; 203:504-508. [PMID: 32986956 DOI: 10.1164/rccm.202003-0753le] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
| | | | | | | | | | | | | | | | | | - Ilias I Siempos
- Weill Cornell Medicine New York, New York.,National and Kapodistrian University of Athens Medical School Athens, Greece
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45
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Gupta S, Wang W, Hayek SS, Chan L, Mathews KS, Melamed ML, Brenner SK, Leonberg-Yoo A, Schenck EJ, Radbel J, Reiser J, Bansal A, Srivastava A, Zhou Y, Finkel D, Green A, Mallappallil M, Faugno AJ, Zhang J, Velez JCQ, Shaefi S, Parikh CR, Charytan DM, Athavale AM, Friedman AN, Redfern RE, Short SAP, Correa S, Pokharel KK, Admon AJ, Donnelly JP, Gershengorn HB, Douin DJ, Semler MW, Hernán MA, Leaf DE. Association Between Early Treatment With Tocilizumab and Mortality Among Critically Ill Patients With COVID-19. JAMA Intern Med 2021; 181:41-51. [PMID: 33080002 PMCID: PMC7577201 DOI: 10.1001/jamainternmed.2020.6252] [Citation(s) in RCA: 313] [Impact Index Per Article: 104.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
IMPORTANCE Therapies that improve survival in critically ill patients with coronavirus disease 2019 (COVID-19) are needed. Tocilizumab, a monoclonal antibody against the interleukin 6 receptor, may counteract the inflammatory cytokine release syndrome in patients with severe COVID-19 illness. OBJECTIVE To test whether tocilizumab decreases mortality in this population. DESIGN, SETTING, AND PARTICIPANTS The data for this study were derived from a multicenter cohort study of 4485 adults with COVID-19 admitted to participating intensive care units (ICUs) at 68 hospitals across the US from March 4 to May 10, 2020. Critically ill adults with COVID-19 were categorized according to whether they received or did not receive tocilizumab in the first 2 days of admission to the ICU. Data were collected retrospectively until June 12, 2020. A Cox regression model with inverse probability weighting was used to adjust for confounding. EXPOSURES Treatment with tocilizumab in the first 2 days of ICU admission. MAIN OUTCOMES AND MEASURES Time to death, compared via hazard ratios (HRs), and 30-day mortality, compared via risk differences. RESULTS Among the 3924 patients included in the analysis (2464 male [62.8%]; median age, 62 [interquartile range {IQR}, 52-71] years), 433 (11.0%) received tocilizumab in the first 2 days of ICU admission. Patients treated with tocilizumab were younger (median age, 58 [IQR, 48-65] vs 63 [IQR, 52-72] years) and had a higher prevalence of hypoxemia on ICU admission (205 of 433 [47.3%] vs 1322 of 3491 [37.9%] with mechanical ventilation and a ratio of partial pressure of arterial oxygen to fraction of inspired oxygen of <200 mm Hg) than patients not treated with tocilizumab. After applying inverse probability weighting, baseline and severity-of-illness characteristics were well balanced between groups. A total of 1544 patients (39.3%) died, including 125 (28.9%) treated with tocilizumab and 1419 (40.6%) not treated with tocilizumab. In the primary analysis, during a median follow-up of 27 (IQR, 14-37) days, patients treated with tocilizumab had a lower risk of death compared with those not treated with tocilizumab (HR, 0.71; 95% CI, 0.56-0.92). The estimated 30-day mortality was 27.5% (95% CI, 21.2%-33.8%) in the tocilizumab-treated patients and 37.1% (95% CI, 35.5%-38.7%) in the non-tocilizumab-treated patients (risk difference, 9.6%; 95% CI, 3.1%-16.0%). CONCLUSIONS AND RELEVANCE Among critically ill patients with COVID-19 in this cohort study, the risk of in-hospital mortality in this study was lower in patients treated with tocilizumab in the first 2 days of ICU admission compared with patients whose treatment did not include early use of tocilizumab. However, the findings may be susceptible to unmeasured confounding, and further research from randomized clinical trials is needed.
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Affiliation(s)
- Shruti Gupta
- Division of Renal Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Wei Wang
- Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Salim S Hayek
- Division of Cardiology, Department of Medicine, University of Michigan, Ann Arbor
| | - Lili Chan
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kusum S Mathews
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Michal L Melamed
- Department of Medicine, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, New York
| | - Samantha K Brenner
- Department of Internal Medicine, Hackensack Meridian School of Medicine at Seton Hall, Nutley, New Jersey.,Department of Internal Medicine, Hackensack Meridian Health, Hackensack University Medical Center, Hackensack, New Jersey
| | - Amanda Leonberg-Yoo
- Renal-Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Edward J Schenck
- Divison of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine Center, New York, New York
| | - Jared Radbel
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey
| | - Jochen Reiser
- Department of Medicine, Rush University Medical Center, Chicago, Illinois
| | - Anip Bansal
- Division of Renal Diseases and Hypertension, University of Colorado Anschutz Medical Campus Aurora, Aurora
| | - Anand Srivastava
- Center for Translational Metabolism and Health, Institute for Public Health and Medicine, Division of Nephrology and Hypertension, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Yan Zhou
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Medical College of Wisconsin, Milwaukee
| | - Diana Finkel
- Department of Medicine, Division of Infectious Diseases, New Jersey Medical School, Rutgers University, Newark
| | - Adam Green
- Division of Critical Care, Cooper University Health Care, Camden, New Jersey
| | - Mary Mallappallil
- Division of Nephrology, Kings County Hospital Center, New York City Health and Hospital Corporation, Brooklyn, New York
| | - Anthony J Faugno
- Division of Pulmonary, Critical Care and Sleep Medicine, Tufts Medical Center, Boston, Massachusetts
| | - Jingjing Zhang
- Division of Nephrology, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania
| | - Juan Carlos Q Velez
- Department of Nephrology, Ochsner Health System, New Orleans, Louisiana.,Ochsner Clinical School, University of Queensland, Brisbane, Australia
| | - Shahzad Shaefi
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Chirag R Parikh
- Division of Nephrology, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - David M Charytan
- Division of Nephrology, Department of Medicine, NYU (New York University) Langone Medical Center, New York, New York
| | | | - Allon N Friedman
- Department of Medicine, Indiana University School of Medicine/Indiana University Health, Indianapolis
| | | | | | - Simon Correa
- Division of Renal Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Kapil K Pokharel
- Division of Renal Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Andrew J Admon
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor
| | - John P Donnelly
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor.,Institute for Healthcare Policy & Innovation, University of Michigan, Ann Arbor
| | - Hayley B Gershengorn
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami Miller School of Medicine, Miami, Florida.,Division of Critical Care Medicine, Albert Einstein College of Medicine, Bronx, New York
| | - David J Douin
- Department of Anesthesiology, University of Colorado School of Medicine, Aurora
| | - Matthew W Semler
- Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Miguel A Hernán
- Department of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.,Harvard-MIT (Massachusetts Institute of Technology) Program in Health Sciences and Technology, Boston, Massachusetts
| | - David E Leaf
- Division of Renal Medicine, Brigham and Women's Hospital, Boston, Massachusetts
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Gupta S, Coca SG, Chan L, Melamed ML, Brenner SK, Hayek SS, Sutherland A, Puri S, Srivastava A, Leonberg-Yoo A, Shehata AM, Flythe JE, Rashidi A, Schenck EJ, Goyal N, Hedayati SS, Dy R, Bansal A, Athavale A, Nguyen HB, Vijayan A, Charytan DM, Schulze CE, Joo MJ, Friedman AN, Zhang J, Sosa MA, Judd E, Velez JCQ, Mallappallil M, Redfern RE, Bansal AD, Neyra JA, Liu KD, Renaghan AD, Christov M, Molnar MZ, Sharma S, Kamal O, Boateng JO, Short SA, Admon AJ, Sise ME, Wang W, Parikh CR, Leaf DE. AKI Treated with Renal Replacement Therapy in Critically Ill Patients with COVID-19. J Am Soc Nephrol 2021; 32:161-176. [PMID: 33067383 PMCID: PMC7894677 DOI: 10.1681/asn.2020060897] [Citation(s) in RCA: 173] [Impact Index Per Article: 57.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 08/27/2020] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND AKI is a common sequela of coronavirus disease 2019 (COVID-19). However, few studies have focused on AKI treated with RRT (AKI-RRT). METHODS We conducted a multicenter cohort study of 3099 critically ill adults with COVID-19 admitted to intensive care units (ICUs) at 67 hospitals across the United States. We used multivariable logistic regression to identify patient-and hospital-level risk factors for AKI-RRT and to examine risk factors for 28-day mortality among such patients. RESULTS A total of 637 of 3099 patients (20.6%) developed AKI-RRT within 14 days of ICU admission, 350 of whom (54.9%) died within 28 days of ICU admission. Patient-level risk factors for AKI-RRT included CKD, men, non-White race, hypertension, diabetes mellitus, higher body mass index, higher d-dimer, and greater severity of hypoxemia on ICU admission. Predictors of 28-day mortality in patients with AKI-RRT were older age, severe oliguria, and admission to a hospital with fewer ICU beds or one with greater regional density of COVID-19. At the end of a median follow-up of 17 days (range, 1-123 days), 403 of the 637 patients (63.3%) with AKI-RRT had died, 216 (33.9%) were discharged, and 18 (2.8%) remained hospitalized. Of the 216 patients discharged, 73 (33.8%) remained RRT dependent at discharge, and 39 (18.1%) remained RRT dependent 60 days after ICU admission. CONCLUSIONS AKI-RRT is common among critically ill patients with COVID-19 and is associated with a hospital mortality rate of >60%. Among those who survive to discharge, one in three still depends on RRT at discharge, and one in six remains RRT dependent 60 days after ICU admission.
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Affiliation(s)
- Shruti Gupta
- Division of Renal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Steven G. Coca
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Lili Chan
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Michal L. Melamed
- Department of Medicine, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, New York
| | - Samantha K. Brenner
- Department of Internal Medicine, Hackensack Meridian School of Medicine, Seton Hall, Nutley, New Jersey
- Department of Internal Medicine, Heart and Vascular Hospital, Hackensack Meridian Health Hackensack University Medical Center, Hackensack, New Jersey
| | - Salim S. Hayek
- Division of Cardiology, University of Michigan Medical Center, Ann Arbor, Michigan
| | - Anne Sutherland
- Division of Pulmonary and Critical Care Medicine, Rutgers New Jersey Medical School, Newark, New Jersey
| | - Sonika Puri
- Division of Nephrology, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey
| | - Anand Srivastava
- Division of Nephrology and Hypertension, Center for Translational Metabolism and Health, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Amanda Leonberg-Yoo
- Renal-Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Alexandre M. Shehata
- Department of Medicine, Hackensack Meridian Health Mountainside Medical Center, Glen Ridge, New Jersey
| | - Jennifer E. Flythe
- Division of Nephrology and Hypertension, Department of Medicine, University of North Carolina Kidney Center, University of North Carolina School of Medicine, Chapel Hill, North Carolina
- Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill, North Carolina
| | - Arash Rashidi
- Division of Nephrology and Hypertension, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Edward J. Schenck
- Divison of Pulmonary and Critical Care Medicine, Department of Medicine Weill Cornell Medicine, New York, New York
| | - Nitender Goyal
- Division of Nephrology, Tufts Medical Center, Boston, Massachusetts
| | - S. Susan Hedayati
- Division of Nephrology, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Rajany Dy
- Division of Pulmonary and Critical Care Medicine, University Medical Center, University of Nevada, Las Vegas, Nevada
| | - Anip Bansal
- Division of Renal Diseases and Hypertension, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | | | - H. Bryant Nguyen
- Division of Pulmonary, Critical Care, Hyperbaric, Allergy, and Sleep Medicine, Loma Linda University Health, Loma Linda, California
| | - Anitha Vijayan
- Division of Nephrology, Washington University, St. Louis, Missouri
| | - David M. Charytan
- Division of Nephrology, New York University Grossman School of Medicine, New York, New York
| | - Carl E. Schulze
- Division of Nephrology, Department of Medicine, University of California, Los Angeles, California
| | - Min J. Joo
- Department of Medicine, Section of Pulmonary, Critical Care, Sleep, and Allergy, University of Illinois, Chicago, Illinois
| | - Allon N. Friedman
- Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana
| | - Jingjing Zhang
- Division of Nephrology, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania
| | - Marie Anne Sosa
- Division of Nephrology, Department of Medicine, University of Miami Miller School of Medicine and Jackson Memorial Hospital, Miami, Florida
| | - Eric Judd
- Division of Nephrology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Juan Carlos Q. Velez
- Department of Nephrology, Ochsner Health System, New Orleans, Louisiana
- Ochsner Clinical School, The University of Queensland, Brisbane, Queensland, Australia
| | - Mary Mallappallil
- Division of Nephrology, Kings County Hospital Center, New York City Health and Hospital Corporation, Brooklyn, New York
| | - Roberta E. Redfern
- Research Department, ProMedica Research, ProMedica Toledo Hospital, Toledo, Ohio
| | - Amar D. Bansal
- Renal and Electrolyte Division, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Javier A. Neyra
- Division of Nephrology, Department of Internal Medicine, Bone and Mineral Metabolism, University of Kentucky, Lexington, Kentucky
| | - Kathleen D. Liu
- Division of Nephrology and Critical Care Medicine, University of California, San Francisco, California
| | - Amanda D. Renaghan
- Division of Nephrology, University of Virginia Health System, Charlottesville, Virginia
| | - Marta Christov
- Department of Medicine-Nephrology, Westchester Medical Center, New York Medical College, New York, New York
| | - Miklos Z. Molnar
- Department of Surgery, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Shreyak Sharma
- Division of Renal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Omer Kamal
- Division of Renal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Jeffery Owusu Boateng
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts
| | - Samuel A.P. Short
- University of Vermont Larner College of Medicine, Burlington, Vermont
| | - Andrew J. Admon
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Meghan E. Sise
- Division of Nephrology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Wei Wang
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Chirag R. Parikh
- Division of Nephrology, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - David E. Leaf
- Division of Renal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
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Arbo JE, Lessing JK, Ford WJH, Clark S, Finkelsztein E, Schenck EJ, Sharma R, Heerdt PM. Heart rate variability measures for prediction of severity of illness and poor outcome in ED patients with sepsis. Am J Emerg Med 2020; 38:2607-2613. [PMID: 31982224 PMCID: PMC7338243 DOI: 10.1016/j.ajem.2020.01.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [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: 08/28/2019] [Revised: 12/30/2019] [Accepted: 01/06/2020] [Indexed: 12/29/2022] Open
Abstract
INTRODUCTION This study evaluates the utility of heart rate variability (HRV) for assessment of severity of illness and poor outcome in Emergency Department (ED) patients with sepsis. HRV measures evaluated included low frequency (LF) signal, high frequency (HF) signal, and deviations in LF and HF signal from age-adjusted reference values. METHODS This was a prospective, observational study. Seventy-two adult ED patients were assessed within 6 h of arrival. RESULTS Severity of illness as defined by sepsis subtype correlated with decreased LF signal (sepsis: 70.68 ± 22.95, severe sepsis: 54.00 ± 28.41, septic shock: 45.54 ± 23.31, p = 0.02), increased HF signal (sepsis: 27.87 ± 19.42, severe sepsis: 44.63 ± 27.29, septic shock: 47.66 ± 20.98, p = 0.01), increasingly negative deviations in LF signal (sepsis: 0.41 ± 24.53, severe sepsis: -21.43 ± 30.09, septic shock -30.39 ± 26.09, p = 0.005) and increasingly positive deviations in HF signal (sepsis: -1.86 ± 21.09, severe sepsis: 20.07 ± 29.03, septic shock: 23.6 ± 24.17, p = 0.004). Composite poor outcome correlated with decreased LF signal (p = 0.008), increased HF signal (p = 0.03), large negative deviations in LF signal (p = 0.004) and large positive deviations in HF signal (p = 0.02). Deviations in LF and HF signal from age-adjusted reference values correlated with individual measures of poor outcome with greater consistency than LF or HF signal. DISCUSSION Accounting for the influence of age on baseline HRV signal improves the predictive value of HRV measures in ED patients with sepsis.
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Affiliation(s)
- John E Arbo
- Department of Emergency Medicine, Weill Medical College of Cornell University, New York, NY, United States of America; Division of Pulmonary and Critical Care Medicine, Weill Medical College of Cornell University, New York, NY, United States of America; Department of Emergency Medicine, Albert Einstein College of Medicine, Bronx, NY, United States of America.
| | - Jeremy K Lessing
- Department of Emergency Medicine, Weill Medical College of Cornell University, New York, NY, United States of America
| | - William J H Ford
- Department of Emergency Medicine, Weill Medical College of Cornell University, New York, NY, United States of America
| | - Sunday Clark
- Department of Emergency Medicine, Weill Medical College of Cornell University, New York, NY, United States of America
| | - Eli Finkelsztein
- Department of Emergency Medicine, Weill Medical College of Cornell University, New York, NY, United States of America
| | - Edward J Schenck
- Division of Pulmonary and Critical Care Medicine, Weill Medical College of Cornell University, New York, NY, United States of America
| | - Rahul Sharma
- Department of Emergency Medicine, Weill Medical College of Cornell University, New York, NY, United States of America
| | - Paul M Heerdt
- Department of Anesthesiology, Division of Applied Hemodynamics, Yale School of Medicine, United States of America
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Goyal P, Ringel JB, Rajan M, Choi JJ, Pinheiro LC, Li HA, Wehmeyer GT, Alshak MN, Jabri A, Schenck EJ, Chen R, Satlin MJ, Campion TR, Nahid M, Plataki M, Hoffman KL, Reshetnyak E, Hupert N, Horn EM, Martinez FJ, Gulick RM, Safford MM. Obesity and COVID-19 in New York City: A Retrospective Cohort Study. Ann Intern Med 2020; 173:855-858. [PMID: 32628537 PMCID: PMC7384267 DOI: 10.7326/m20-2730] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Affiliation(s)
- Parag Goyal
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Joanna Bryan Ringel
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Mangala Rajan
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Justin J Choi
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Laura C Pinheiro
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Han A Li
- Weill Cornell Medical College, New York, New York (H.A.L., G.T.W., M.N.A.)
| | - Graham T Wehmeyer
- Weill Cornell Medical College, New York, New York (H.A.L., G.T.W., M.N.A.)
| | - Mark N Alshak
- Weill Cornell Medical College, New York, New York (H.A.L., G.T.W., M.N.A.)
| | - Assem Jabri
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Edward J Schenck
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Ruijun Chen
- Weill Cornell Medicine and Columbia University, New York, New York (R.C.)
| | - Michael J Satlin
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Thomas R Campion
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Musarrat Nahid
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Maria Plataki
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Katherine L Hoffman
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Evgeniya Reshetnyak
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Nathaniel Hupert
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Evelyn M Horn
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Fernando J Martinez
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Roy M Gulick
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
| | - Monika M Safford
- Weill Cornell Medicine, New York, New York (P.G., J.B.R., M.R., J.J.C., L.C.P., A.J., E.J.S., M.J.S., T.R.C., M.N., M.P., K.L.H., E.R., N.H., E.M.H., F.J.M., R.M.G., M.M.S.)
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Gupta S, Hayek SS, Wang W, Chan L, Mathews KS, Melamed ML, Brenner SK, Leonberg-Yoo A, Schenck EJ, Radbel J, Reiser J, Bansal A, Srivastava A, Zhou Y, Sutherland A, Green A, Shehata AM, Goyal N, Vijayan A, Velez JCQ, Shaefi S, Parikh CR, Arunthamakun J, Athavale AM, Friedman AN, Short SAP, Kibbelaar ZA, Abu Omar S, Admon AJ, Donnelly JP, Gershengorn HB, Hernán MA, Semler MW, Leaf DE. Factors Associated With Death in Critically Ill Patients With Coronavirus Disease 2019 in the US. JAMA Intern Med 2020; 180:1436-1447. [PMID: 32667668 PMCID: PMC7364338 DOI: 10.1001/jamainternmed.2020.3596] [Citation(s) in RCA: 608] [Impact Index Per Article: 152.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 06/19/2020] [Indexed: 01/08/2023]
Abstract
Importance The US is currently an epicenter of the coronavirus disease 2019 (COVID-19) pandemic, yet few national data are available on patient characteristics, treatment, and outcomes of critical illness from COVID-19. Objectives To assess factors associated with death and to examine interhospital variation in treatment and outcomes for patients with COVID-19. Design, Setting, and Participants This multicenter cohort study assessed 2215 adults with laboratory-confirmed COVID-19 who were admitted to intensive care units (ICUs) at 65 hospitals across the US from March 4 to April 4, 2020. Exposures Patient-level data, including demographics, comorbidities, and organ dysfunction, and hospital characteristics, including number of ICU beds. Main Outcomes and Measures The primary outcome was 28-day in-hospital mortality. Multilevel logistic regression was used to evaluate factors associated with death and to examine interhospital variation in treatment and outcomes. Results A total of 2215 patients (mean [SD] age, 60.5 [14.5] years; 1436 [64.8%] male; 1738 [78.5%] with at least 1 chronic comorbidity) were included in the study. At 28 days after ICU admission, 784 patients (35.4%) had died, 824 (37.2%) were discharged, and 607 (27.4%) remained hospitalized. At the end of study follow-up (median, 16 days; interquartile range, 8-28 days), 875 patients (39.5%) had died, 1203 (54.3%) were discharged, and 137 (6.2%) remained hospitalized. Factors independently associated with death included older age (≥80 vs <40 years of age: odds ratio [OR], 11.15; 95% CI, 6.19-20.06), male sex (OR, 1.50; 95% CI, 1.19-1.90), higher body mass index (≥40 vs <25: OR, 1.51; 95% CI, 1.01-2.25), coronary artery disease (OR, 1.47; 95% CI, 1.07-2.02), active cancer (OR, 2.15; 95% CI, 1.35-3.43), and the presence of hypoxemia (Pao2:Fio2<100 vs ≥300 mm Hg: OR, 2.94; 95% CI, 2.11-4.08), liver dysfunction (liver Sequential Organ Failure Assessment score of 2-4 vs 0: OR, 2.61; 95% CI, 1.30-5.25), and kidney dysfunction (renal Sequential Organ Failure Assessment score of 4 vs 0: OR, 2.43; 95% CI, 1.46-4.05) at ICU admission. Patients admitted to hospitals with fewer ICU beds had a higher risk of death (<50 vs ≥100 ICU beds: OR, 3.28; 95% CI, 2.16-4.99). Hospitals varied considerably in the risk-adjusted proportion of patients who died (range, 6.6%-80.8%) and in the percentage of patients who received hydroxychloroquine, tocilizumab, and other treatments and supportive therapies. Conclusions and Relevance This study identified demographic, clinical, and hospital-level risk factors that may be associated with death in critically ill patients with COVID-19 and can facilitate the identification of medications and supportive therapies to improve outcomes.
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Affiliation(s)
- Shruti Gupta
- Division of Renal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Salim S. Hayek
- Division of Cardiology, Department of Medicine, University of Michigan, Ann Arbor
| | - Wei Wang
- Department of Medicine, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Lili Chan
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kusum S. Mathews
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Michal L. Melamed
- Montefiore Medical Center, Department of Medicine, Albert Einstein College of Medicine, Bronx, New York
| | - Samantha K. Brenner
- Department of Internal Medicine, Hackensack Meridian School of Medicine at Seton Hall, Nutley, New Jersey
- Heart and Vascular Hospital, Hackensack Meridian Health Hackensack University Medical Center, Hackensack, New Jersey
| | - Amanda Leonberg-Yoo
- Renal-Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Edward J. Schenck
- Divison of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Jared Radbel
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey
| | - Jochen Reiser
- Department of Medicine, Rush University Medical Center, Chicago, Illinois
| | - Anip Bansal
- Division of Renal Diseases and Hypertension, University of Colorado Anschutz Medical Campus, Aurora
| | - Anand Srivastava
- Center for Translational Metabolism and Health, Institute for Public Health and Medicine, Division of Nephrology and Hypertension, Northwestern University Feinberg School of Medicine
| | - Yan Zhou
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Medical College of Wisconsin, Milwaukee
| | - Anne Sutherland
- Division of Pulmonary and Critical Care Medicine, Rutgers New Jersey Medical School, Newark
| | - Adam Green
- Cooper University Health Care, Camden, New Jersey
| | - Alexandre M. Shehata
- Department of Medicine, Hackensack Meridian Health Mountainside Medical Center, Glen Ridge, New Jersey
| | - Nitender Goyal
- Division of Nephrology, Tufts Medical Center, Boston, Massachusetts
| | - Anitha Vijayan
- Division of Nephrology, Washington University in St Louis, St Louis, Missouri
| | - Juan Carlos Q. Velez
- Department of Nephrology, Ochsner Health System, New Orleans, Louisiana
- Ochsner Clinical School, The University of Queensland, Brisbane, Queensland, Australia
| | - Shahzad Shaefi
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Chirag R. Parikh
- Division of Nephrology, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Justin Arunthamakun
- Division of Cardiology, Department of Internal Medicine, Baylor University Medical Center, Dallas, Texas
| | | | - Allon N. Friedman
- Department of Medicine, Indiana University School of Medicine, Indianapolis
| | | | | | - Samah Abu Omar
- Division of Renal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Andrew J. Admon
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor
- Institute for Healthcare Policy & Innovation, University of Michigan, Ann Arbor
| | - John P. Donnelly
- Institute for Healthcare Policy & Innovation, University of Michigan, Ann Arbor
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor
| | - Hayley B. Gershengorn
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami Miller School of Medicine, Miami, Florida
- Division of Critical Care Medicine, Albert Einstein College of Medicine, Bronx, New York
| | - Miguel A. Hernán
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Harvard–Massachusetts Institute of Technology Division of Health Sciences and Technology, Boston, Massachusetts
| | - Matthew W. Semler
- Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - David E. Leaf
- Division of Renal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
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Laurence J, Mulvey JJ, Seshadri M, Racanelli A, Harp J, Schenck EJ, Zappetti D, Horn EM, Magro CM. Anti-complement C5 therapy with eculizumab in three cases of critical COVID-19. Clin Immunol 2020; 219:108555. [PMID: 32771488 PMCID: PMC7410014 DOI: 10.1016/j.clim.2020.108555] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 07/29/2020] [Accepted: 08/03/2020] [Indexed: 01/01/2023]
Abstract
Respiratory failure and acute kidney injury (AKI) are associated with high mortality in SARS-CoV-2-associated Coronavirus disease 2019 (COVID-19). These manifestations are linked to a hypercoaguable, pro-inflammatory state with persistent, systemic complement activation. Three critical COVID-19 patients recalcitrant to multiple interventions had skin biopsies documenting deposition of the terminal complement component C5b-9, the lectin complement pathway enzyme MASP2, and C4d in microvascular endothelium. Administration of anti-C5 monoclonal antibody eculizumab led to a marked decline in D-dimers and neutrophil counts in all three cases, and normalization of liver functions and creatinine in two. One patient with severe heart failure and AKI had a complete remission. The other two individuals had partial remissions, one with resolution of his AKI but ultimately succumbing to respiratory failure, and another with a significant decline in FiO2 requirements, but persistent renal failure. In conclusion, anti-complement therapy may be beneficial in at least some patients with critical COVID-19.
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Affiliation(s)
- Jeffrey Laurence
- Department of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY, USA.
| | - J Justin Mulvey
- Department of Laboratory Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Madhav Seshadri
- Department of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY, USA
| | - Alexandra Racanelli
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Joanna Harp
- Department of Dermatology, Weill Cornell Medicine, New York, NY, USA
| | - Edward J Schenck
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Dana Zappetti
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Evelyn M Horn
- Department of Medicine, Division of Cardiology, Weill Cornell Medicine, New York, NY, USA
| | - Cynthia M Magro
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
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