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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.

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TLDR
The Severe COVID-19 Adaptive Risk Predictor (SCARP) as mentioned in this paper was developed 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.
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.

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References
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Journal ArticleDOI

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study.

TL;DR: Wang et al. as discussed by the authors used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death, including older age, high SOFA score and d-dimer greater than 1 μg/mL.
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An interactive web-based dashboard to track COVID-19 in real time.

TL;DR: The outbreak of the 2019 novel coronavirus disease (COVID-19) has induced a considerable degree of fear, emotional stress and anxiety among individuals around the world.
Journal ArticleDOI

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

TL;DR: Proposed models for covid-19 are poorly reported, at high risk of bias, and their reported performance is probably optimistic, according to a review of published and preprint reports.
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