Findings Associated With Severe Illness and Mortality Among Hospitalized Individuals With COVID-19 | Critical Care Medicine | JN Learning | AMA Ed Hub [Skip to Content]
[Skip to Content Landing]

Laboratory Findings Associated With Severe Illness and Mortality Among Hospitalized Individuals With Coronavirus Disease 2019 in Eastern Massachusetts

Educational Objective
To identify the key insights or developments described in this article
1 Credit CME
Key Points

Question  How well can sociodemographic features, laboratory values, and comorbidities of individuals hospitalized with coronavirus disease 2019 (COVID-19) in Eastern Massachusetts through June 5, 2020, predict a severe illness course?

Findings  In this cohort study of 2511 hospitalized individuals positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by polymerase chain reaction who were admitted to 1 of 6 hospitals, 215 (8.6%) were admitted to the instensive care unit, 164 (6.5%) required mechanical ventilation, and 292 (11.6%) died. In a risk prediction model, 212 deaths (78%) occurred in the top mortality-risk quintile.

Meaning  Simple prediction models may assist in risk stratification among hospitalized patients with COVID-19.

Abstract

Importance  The coronavirus disease 2019 (COVID-19) pandemic has placed unprecedented stress on health systems across the world, and reliable estimates of risk for adverse hospital outcomes are needed.

Objective  To quantify admission laboratory and comorbidity features associated with critical illness and mortality risk across 6 Eastern Massachusetts hospitals.

Design, Setting, and Participants  Retrospective cohort study of all individuals admitted to the hospital who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by polymerase chain reaction across these 6 hospitals through June 5, 2020, using hospital course, prior diagnoses, and laboratory values in emergency department and inpatient settings from 2 academic medical centers and 4 community hospitals. The data were extracted on June 11, 2020, and the analysis was conducted from June to July 2020.

Exposures  SARS-CoV-2.

Main Outcomes and Measures  Severe illness defined by admission to intensive care unit, mechanical ventilation, or death.

Results  Of 2511 hospitalized individuals who tested positive for SARS-CoV-2 (of whom 50.9% were male, 53.9% White, and 27.0% Hispanic, with a mean [SD ]age of 62.6 [19.0] years), 215 (8.6%) were admitted to the intensive care unit, 164 (6.5%) required mechanical ventilation, and 292 (11.6%) died. L1-regression models developed in 3 of these hospitals yielded an area under the receiver operating characteristic curve of 0.807 for severe illness and 0.847 for mortality in the 3 held-out hospitals. In total, 212 of 292 deaths (72.6%) occurred in the highest-risk mortality quintile.

Conclusions and Relevance  In this cohort, specific admission laboratory studies in concert with sociodemographic features and prior diagnosis facilitated risk stratification among individuals hospitalized for COVID-19.

Sign in to take quiz and track your certificates

Buy This Activity

JN Learning™ is the home for CME and MOC from the JAMA Network. Search by specialty or US state and earn AMA PRA Category 1 CME Credit™ from articles, audio, Clinical Challenges and more. Learn more about CME/MOC

Article Information

Accepted for Publication: September 2, 2020.

Published: October 30, 2020. doi:10.1001/jamanetworkopen.2020.23934

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Castro VM et al. JAMA Network Open.

Corresponding Author: Roy H. Perlis, MD, MSc, Center for Quantitative Health, Division of Clinical Research, Massachusetts General Hospital, 185 Cambridge St, 6th Floor, Boston, MA 02114 (rperlis@mgh.harvard.edu).

Author Contributions: Dr Perlis had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: All authors.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Castro, McCoy.

Administrative, technical, or material support: All authors.

Supervision: Perlis.

Conflict of Interest Disclosures: Dr Perlis has received consulting fees from Burrage Capital, Genomind, RID Ventures, and Takeda. He holds equity in Outermost Therapeutics and Psy Therapeutics. Dr McCoy has received research funding from the Stanley Center at the Broad Institute, the Brain and Behavior Research Foundation, National Institute of Mental Health, National Human Genome Research Institute Home, and Telefonica Alfa. No other disclosures were reported.

Disclaimer: Dr Perlis is an Associate Editor at JAMA Network Open; however, he had no role in the editorial review or decision to accept the manuscript for publication.

Additional Contributions: The authors would like to thank the Partners Research Patient Data Registry and Analytics Enclave team members for their support in making up-to-date electronic health record data available for this research. No financial compensation was provided specifically for this work.

References
1.
Guan  W-J , Ni  Z-Y , Hu  Y ,  et al; China Medical Treatment Expert Group for Covid-19.  Clinical characteristics of coronavirus disease 2019 in China.   N Engl J Med. 2020;382(18):1708-1720. doi:10.1056/NEJMoa2002032PubMedGoogle ScholarCrossref
2.
Grasselli  G , Pesenti  A , Cecconi  M .  Critical care utilization for the COVID-19 outbreak in Lombardy, Italy: early experience and forecast during an emergency response.   JAMA. 2020;323(16):1545-1546. Published online March 13, 2020. doi:10.1001/jama.2020.4031PubMedGoogle ScholarCrossref
3.
Bhatraju  PK , Ghassemieh  BJ , Nichols  M ,  et al.  Covid-19 in critically ill patients in the Seattle region—case series.   N Engl J Med. 2020;382(21):2012-2022. doi:10.1056/NEJMoa2004500PubMedGoogle ScholarCrossref
4.
Richardson  S , Hirsch  JS , Narasimhan  M ,  et al; the Northwell COVID-19 Research Consortium.  Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area.   JAMA. 2020;323(20):2052-2059. doi:10.1001/jama.2020.6775PubMedGoogle ScholarCrossref
5.
Ruan  Q , Yang  K , Wang  W , Jiang  L , Song  J .  Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China.   Intensive Care Med. 2020;46(5):846-848. doi:10.1007/s00134-020-05991-xPubMedGoogle ScholarCrossref
6.
Zhou  F , Yu  T , Du  R ,  et al.  Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study.   Lancet. 2020;395(10229):1054-1062. doi:10.1016/S0140-6736(20)30566-3 PubMedGoogle ScholarCrossref
7.
Du  Y , Tu  L , Zhu  P ,  et al.  Clinical features of 85 fatal cases of COVID-19 from Wuhan: a retrospective observational study.   Am J Respir Crit Care Med. 2020;201(11):1372-1379. doi:10.1164/rccm.202003-0543OCPubMedGoogle ScholarCrossref
8.
Henry  BM , de Oliveira  MHS , Benoit  S , Plebani  M , Lippi  G .  Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis.   Clin Chem Lab Med. 2020;58(7):1021-1028. doi:10.1515/cclm-2020-0369 PubMedGoogle ScholarCrossref
9.
Consortium for Clinical Characterization of Covid19 by EHR (4CE). i2b2 tranSMART Foundation. Published April 5, 2020. Accessed April 19, 2020. https://transmartfoundation.org/covid-19-community-project/
10.
Pencina  MJ , Goldstein  BA , D’Agostino  RB .  Prediction models—development, evaluation, and clinical application.   N Engl J Med. 2020;382(17):1583-1586. doi:10.1056/NEJMp2000589 PubMedGoogle ScholarCrossref
11.
Nalichowski  R , Keogh  D , Chueh  HC , Murphy  SN .  Calculating the benefits of a Research Patient Data Repository.   AMIA Annu Symp Proc. 2006;2006:1044.PubMedGoogle Scholar
12.
Murphy  SN , Weber  G , Mendis  M ,  et al.  Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2).   J Am Med Inform Assoc. 2010;17(2):124-130. doi:10.1136/jamia.2009.000893 PubMedGoogle ScholarCrossref
13.
Charlson  M , Szatrowski  TP , Peterson  J , Gold  J .  Validation of a combined comorbidity index.   J Clin Epidemiol. 1994;47(11):1245-1251. doi:10.1016/0895-4356(94)90129-5 PubMedGoogle ScholarCrossref
14.
Healthcare Cost and Utilization Project (HCUP). HCUP-US Tools & Software Page. Clinical Classifications Software (CCS) for ICD-9-CM. Accessed September 6, 2020. https://www.hcup-us.ahrq.gov/tools_software.jsp
15.
Tibshirani  R .  Regression shrinkage and selection via the lasso.   J R Stat Soc Ser B Methodol. 1996;58(1):267-288. doi:10.1111/j.2517-6161.1996.tb02080.x Google Scholar
16.
The R Project for Statistical Computing. The R Foundation. Accessed September 6, 2020. http://www.R-project.org
17.
Pei  G , Zhang  Z , Peng  J ,  et al.  Renal involvement and early prognosis in patients with COVID-19 pneumonia.   J Am Soc Nephrol. 2020;31(6):1157-1165. doi:10.1681/ASN.2020030276PubMedGoogle ScholarCrossref
18.
Wynants  L , Van Calster  B , Collins  GS ,  et al.  Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal.   BMJ. 2020;369:m1328. doi:10.1136/bmj.m1328 PubMedGoogle ScholarCrossref
If you are not a JN Learning subscriber, you can either:
Subscribe to JN Learning for one year
Buy this activity
jn-learning_Modal_LoginSubscribe_Purchase
Close
If you are not a JN Learning subscriber, you can either:
Subscribe to JN Learning for one year
Buy this activity
jn-learning_Modal_LoginSubscribe_Purchase
Close
With a personal account, you can:
  • Access free activities and track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
Education Center Collection Sign In Modal Right
Close

Name Your Search

Save Search
Close
With a personal account, you can:
  • Track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
jn-learning_Modal_SaveSearch_NoAccess_Purchase
Close

Lookup An Activity

or

Close

My Saved Searches

You currently have no searches saved.

Close
With a personal account, you can:
  • Access free activities and track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
Education Center Collection Sign In Modal Right
Close