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Development of Severe COVID-19 Adaptive Risk Predictor (SCARP), a Calculator to Predict Severe Disease or Death in Hospitalized Patients With COVID-19.
Wongvibulsin, Shannon; Garibaldi, Brian T; Antar, Annukka A R; Wen, Jiyang; Wang, Mei-Cheng; Gupta, Amita; Bollinger, Robert; Xu, Yanxun; Wang, Kunbo; Betz, Joshua F; Muschelli, John; Bandeen-Roche, Karen; Zeger, Scott L; Robinson, Matthew L.
  • Wongvibulsin S; Johns Hopkins University School of Medicine, Baltimore, Maryland (S.W., B.T.G., A.A.A., A.G., R.B., M.L.R.).
  • Garibaldi BT; Johns Hopkins University School of Medicine, Baltimore, Maryland (S.W., B.T.G., A.A.A., A.G., R.B., M.L.R.).
  • Antar AAR; Johns Hopkins University School of Medicine, Baltimore, Maryland (S.W., B.T.G., A.A.A., A.G., R.B., M.L.R.).
  • Wen J; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.W., M.W., J.F.B., J.M., K.B., S.L.Z.).
  • Wang MC; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.W., M.W., J.F.B., J.M., K.B., S.L.Z.).
  • Gupta A; Johns Hopkins University School of Medicine, Baltimore, Maryland (S.W., B.T.G., A.A.A., A.G., R.B., M.L.R.).
  • Bollinger R; Johns Hopkins University School of Medicine, Baltimore, Maryland (S.W., B.T.G., A.A.A., A.G., R.B., M.L.R.).
  • Xu Y; Johns Hopkins University, Baltimore, Maryland (Y.X., K.W.).
  • Wang K; Johns Hopkins University, Baltimore, Maryland (Y.X., K.W.).
  • Betz JF; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.W., M.W., J.F.B., J.M., K.B., S.L.Z.).
  • Muschelli J; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.W., M.W., J.F.B., J.M., K.B., S.L.Z.).
  • Bandeen-Roche K; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.W., M.W., J.F.B., J.M., K.B., S.L.Z.).
  • Zeger SL; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.W., M.W., J.F.B., J.M., K.B., S.L.Z.).
  • Robinson ML; Johns Hopkins University School of Medicine, Baltimore, Maryland (S.W., B.T.G., A.A.A., A.G., R.B., M.L.R.).
Ann Intern Med ; 174(6): 777-785, 2021 06.
Article in English | MEDLINE | ID: covidwho-1110712
ABSTRACT

BACKGROUND:

Predicting the clinical trajectory of individual patients hospitalized with coronavirus disease 2019 (COVID-19) is challenging but necessary to inform clinical care. The majority of COVID-19 prognostic tools use only data present upon admission and do not incorporate changes occurring after admission.

OBJECTIVE:

To develop the Severe COVID-19 Adaptive Risk Predictor (SCARP) (https//rsconnect.biostat.jhsph.edu/covid_trajectory/), a novel tool that can provide dynamic risk predictions for progression from moderate disease to severe illness or death in patients with COVID-19 at any time within the first 14 days of their hospitalization.

DESIGN:

Retrospective observational cohort study. SETTINGS Five hospitals in Maryland and Washington, D.C. PATIENTS Patients who were hospitalized between 5 March and 4 December 2020 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed by nucleic acid test and symptomatic disease. MEASUREMENTS A clinical registry for patients hospitalized with COVID-19 was the primary data source; data included demographic characteristics, admission source, comorbid conditions, time-varying vital signs, laboratory measurements, and clinical severity. Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict the 1-day and 7-day risks for progression to severe disease or death for any given day during the first 14 days of hospitalization.

RESULTS:

Among 3163 patients admitted with moderate COVID-19, 228 (7%) became severely ill or died in the next 24 hours; an additional 355 (11%) became severely ill or died in the next 7 days. The area under the receiver-operating characteristic curve (AUC) for 1-day risk predictions for progression to severe disease or death was 0.89 (95% CI, 0.88 to 0.90) and 0.89 (CI, 0.87 to 0.91) during the first and second weeks of hospitalization, respectively. The AUC for 7-day risk predictions for progression to severe disease or death was 0.83 (CI, 0.83 to 0.84) and 0.87 (CI, 0.86 to 0.89) during the first and second weeks of hospitalization, respectively.

LIMITATION:

The SCARP tool was developed by using data from a single health system.

CONCLUSION:

Using the predictive power of RF-SLAM and longitudinal data from more than 3000 patients hospitalized with COVID-19, an interactive tool was developed that rapidly and accurately provides the probability of an individual patient's progression to severe illness or death on the basis of readily available clinical information. PRIMARY FUNDING SOURCE Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Hospital Mortality / Risk Assessment / Patient Acuity / COVID-19 Type of study: Cohort study / Observational study / Prognostic study / Randomized controlled trials Limits: Aged / Female / Humans / Male / Middle aged Country/Region as subject: North America Language: English Journal: Ann Intern Med Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Hospital Mortality / Risk Assessment / Patient Acuity / COVID-19 Type of study: Cohort study / Observational study / Prognostic study / Randomized controlled trials Limits: Aged / Female / Humans / Male / Middle aged Country/Region as subject: North America Language: English Journal: Ann Intern Med Year: 2021 Document Type: Article