Development of Severe COVID-19 Adaptive Risk Predictor (SCARP), a Calculator to Predict Severe Disease or Death in Hospitalized Patients With COVID-19.
Shannon Wongvibulsin,Brian T. Garibaldi,Annukka A.R. Antar,Jiyang Wen,Mei Cheng Wang,Amita Gupta,Robert C. Bollinger,Yanxun Xu,Kunbo Wang,Joshua Betz,John Muschelli,Karen Bandeen-Roche,Scott L. Zeger,Matthew L Robinson +13 more
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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.read more
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Racial and Ethnic Discrepancy in Pulse Oximetry and Delayed Identification of Treatment Eligibility Among Patients With COVID-19.
Ashraf Fawzy,Tianshi David Wu,Kunbo Wang,Matthew L Robinson,Jad Farha,Amanda Bradke,Sherita Hill Golden,Yanxun Xu,Brian T. Garibaldi +8 more
TL;DR: In this article , the authors investigated whether there is differential inaccuracy of pulse oximetry by race or ethnicity among patients with COVID-19 and estimate the association of such inaccuracies with time to recognition of eligibility for oxygen threshold-specific COVID19 therapies.
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Muscle strength and muscle mass as predictors of hospital length of stay in patients with moderate to severe COVID-19: a prospective observational study.
Saulo Gil,Wilson Jacob Filho,Samuel Katsuyuki Shinjo,Eduardo Ferriolli,Alexandre Leopold Busse,Thiago Junqueira Avelino-Silva,Igor Longobardi,Gersiel Nascimento de Oliveira Júnior,Paul Swinton,Bruno Gualano,Hamilton Roschel +10 more
TL;DR: In this article, the authors investigated whether muscle strength or muscle mass are predictive of hospital length of stay (LOS) in patients with moderate to severe COVID-19 patients, and found that the strongest patients had shorter LOS than other patients.
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Clinical factors associated with death in 3044 COVID-19 patients managed in internal medicine wards in Italy: results from the SIMI-COVID-19 study of the Italian Society of Internal Medicine (SIMI).
Elena Corradini,Paolo Ventura,Walter Ageno,Chiara Cogliati,Maria Lorenza Muiesan,Domenico Girelli,Mario Pirisi,Antonio Gasbarrini,Paolo Angeli,Patrizia Rovere Querini,Emanuele Bosi,Moreno Tresoldi,Roberto Vettor,Marco Cattaneo,Fabio Piscaglia,Antonio Brucato,Stefano Perlini,Paolo Martelletti,Roberto Pontremoli,Massimo Porta,Pietro Minuz,Oliviero Olivieri,Giorgio Sesti,Gianni Biolo,Damiano Rizzoni,Gaetano Serviddio,Francesco Cipollone,Davide Grassi,Roberto Manfredini,Guido Moreo,Antonello Pietrangelo +30 more
TL;DR: In this paper, the authors conducted a nationwide cohort multicentre study on death outcome in adult COVID-19 patients admitted and managed in IMU, and identified PaO2/FiO2 ratio at admission and comorbidity as the main alert signs to inform clinical decisions and resource allocation.
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Prediction models for severe manifestations and mortality due to COVID‐19: A systematic review
Jamie L. Miller,Masafumi Tada,Michihiko Goto,Hao Chen,Elizabeth Dang,Nicholas M. Mohr,Sangil Lee +6 more
TL;DR: There has been an immediate need for research to understand the clinical signs and symptoms of COVID‐19 that can help predict deterioration including mechanical ventilation, organ support, and death.
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Body Mass Index, Multi-Morbidity, and COVID-19 Risk Factors as Predictors of Severe COVID-19 Outcomes.
Sanjeev Nanda,Audry Chacin Suarez,Loren Toussaint,Ann Vincent,Karen M. Fischer,Ryan T. Hurt,Darrell R. Schroeder,Jose R. Medina Inojosa,John C. O’Horo,Ramona S. DeJesus,Haitham S. Abu Lebdeh,Manpreet S. Mundi,Salma Iftikhar,Ivana T. Croghan +13 more
TL;DR: In this article, the authors investigated body mass index, multi-morbidity, and COVID-19 Risk Score as predictors of severe COVID19 outcomes, and found that these predictors are significantly more likely to lead to more severe outcomes.
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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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