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Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative.
Bennett, Tellen D; Moffitt, Richard A; Hajagos, Janos G; Amor, Benjamin; Anand, Adit; Bissell, Mark M; Bradwell, Katie Rebecca; Bremer, Carolyn; Byrd, James Brian; Denham, Alina; DeWitt, Peter E; Gabriel, Davera; Garibaldi, Brian T; Girvin, Andrew T; Guinney, Justin; Hill, Elaine L; Hong, Stephanie S; Jimenez, Hunter; Kavuluru, Ramakanth; Kostka, Kristin; Lehmann, Harold P; Levitt, Eli; Mallipattu, Sandeep K; Manna, Amin; McMurry, Julie A; Morris, Michele; Muschelli, John; Neumann, Andrew J; Palchuk, Matvey B; Pfaff, Emily R; Qian, Zhenglong; Qureshi, Nabeel; Russell, Seth; Spratt, Heidi; Walden, Anita; Williams, Andrew E; Wooldridge, Jacob T; Yoo, Yun Jae; Zhang, Xiaohan Tanner; Zhu, Richard L; Austin, Christopher P; Saltz, Joel H; Gersing, Ken R; Haendel, Melissa A; Chute, Christopher G.
  • Bennett TD; Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora.
  • Moffitt RA; Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York.
  • Hajagos JG; Stony Brook University, Stony Brook, New York.
  • Amor B; Palantir Technologies, Denver, Colorado.
  • Anand A; Stony Brook University, Stony Brook, New York.
  • Bissell MM; Palantir Technologies, Denver, Colorado.
  • Bradwell KR; Palantir Technologies, Denver, Colorado.
  • Bremer C; Stony Brook University, Stony Brook, New York.
  • Byrd JB; Department of Internal Medicine, The University of Michigan at Ann Arbor, Ann Arbor.
  • Denham A; Department of Public Health Sciences, University of Rochester Medical Center, Rochester, New York.
  • DeWitt PE; Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora.
  • Gabriel D; Institute for Clinical and Translational Research, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Garibaldi BT; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Girvin AT; Palantir Technologies, Denver, Colorado.
  • Guinney J; Sage Bionetworks, Seattle, Washington.
  • Hill EL; Department of Public Health Sciences, University of Rochester Medical Center, Rochester, New York.
  • Hong SS; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Jimenez H; Stony Brook University, Stony Brook, New York.
  • Kavuluru R; Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington.
  • Kostka K; Real World Solutions, IQVIA, Cambridge, Massachusetts.
  • Lehmann HP; Observational Health Data Sciences and Informatics, New York, New York.
  • Levitt E; Division of Health Science Informatics, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Mallipattu SK; Department of Orthopaedic Surgery, University of Alabama at Birmingham, Birmingham.
  • Manna A; Stony Brook University, Stony Brook, New York.
  • McMurry JA; Palantir Technologies, Denver, Colorado.
  • Morris M; Translational and Integrative Sciences Center, Oregon State University, Corvallis.
  • Muschelli J; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania.
  • Neumann AJ; Department of Biostatistics, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Palchuk MB; Translational and Integrative Sciences Center, Oregon State University, Corvallis.
  • Pfaff ER; TriNetX, Cambridge, Massachusetts.
  • Qian Z; North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill.
  • Qureshi N; Department of biomedical informatics, Stony Brook University, Stony Brook, New York.
  • Russell S; Palantir Technologies, Denver, Colorado.
  • Spratt H; Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora.
  • Walden A; Department of Preventive Medicine and Public Health, University of Texas Medical Branch, Galveston.
  • Williams AE; Sage Bionetworks, Seattle, Washington.
  • Wooldridge JT; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland.
  • Yoo YJ; Tufts Medical Center Clinical and Translational Science Institute, Tufts Medical Center, Boston, Massachusetts.
  • Zhang XT; Stony Brook University, Stony Brook, New York.
  • Zhu RL; Stony Brook University, Stony Brook, New York.
  • Austin CP; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Saltz JH; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Gersing KR; National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland.
  • Haendel MA; Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York.
  • Chute CG; National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland.
JAMA Netw Open ; 4(7): e2116901, 2021 07 01.
Article in English | MEDLINE | ID: covidwho-1306627
ABSTRACT
Importance The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy.

Objectives:

To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. Design, Setting, and

Participants:

In a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). Main Outcomes and

Measures:

Patient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression.

Results:

The cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, age (odds ratio [OR], 1.03 per year; 95% CI, 1.03-1.04), male sex (OR, 1.60; 95% CI, 1.51-1.69), liver disease (OR, 1.20; 95% CI, 1.08-1.34), dementia (OR, 1.26; 95% CI, 1.13-1.41), African American (OR, 1.12; 95% CI, 1.05-1.20) and Asian (OR, 1.33; 95% CI, 1.12-1.57) race, and obesity (OR, 1.36; 95% CI, 1.27-1.46) were independently associated with higher clinical severity. Conclusions and Relevance This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Severity of Illness Index / Databases, Factual / Forecasting / COVID-19 / Hospitalization / Models, Biological Type of study: Etiology study / Observational study / Prognostic study / Randomized controlled trials / Risk factors Limits: Adult / Aged / Female / Humans / Male / Middle aged / Young adult Country/Region as subject: North America Language: English Journal: JAMA Netw Open Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Severity of Illness Index / Databases, Factual / Forecasting / COVID-19 / Hospitalization / Models, Biological Type of study: Etiology study / Observational study / Prognostic study / Randomized controlled trials / Risk factors Limits: Adult / Aged / Female / Humans / Male / Middle aged / Young adult Country/Region as subject: North America Language: English Journal: JAMA Netw Open Year: 2021 Document Type: Article