External validation of a clinical risk score to predict hospital admission and in-hospital mortality in COVID-19 patients
Creators: name: Halalau, Alexandra
name: Imam, Zaid
name: Karabon, Patrick
name: Mankuzhy, Nikhil
name: Shaheen, Aciel
name: Tu, John
name: Carpenter, Christopher
Publisher: name: Taylor & Francis
Year: 2020
Subjects: subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: Neuroscience
subject: Biotechnology
subject: Chemical Sciences not elsewhere classified
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Marine Biology
subject: Cancer
subject: Hematology
subject: Infectious Diseases
subject: FOS: Health sciences
subject: Virology
subject: Computational Biology

description: Identification of patients with novel coronavirus disease 2019 (COVID-19) requiring hospital admission or at high-risk of in-hospital mortality is essential to guide patient triage and to provide timely treatment for higher risk hospitalized patients. A retrospective multi-centre (8 hospital) cohort at Beaumont Health, Michigan, USA, reporting on COVID-19 patients diagnosed between 1 March and 1 April 2020 was used for score validation. The COVID-19 Risk of Complications Score was automatically computed by the EHR. Multivariate logistic regression models were built to predict hospital admission and in-hospital mortality using individual variables constituting the score. Validation was performed using both discrimination and calibration. Compared to Green scores, Yellow Scores (OR: 5.72) and Red Scores (OR: 19.1) had significantly higher odds of admission (both <i>p</i> &lt; .0001). Similarly, Yellow Scores (OR: 4.73) and Red Scores (OR: 13.3) had significantly higher odds of in-hospital mortality than Green Scores (both <i>p</i> &lt; .0001). The cross-validated C-Statistics for the external validation cohort showed good discrimination for both hospital admission (<i>C</i> = 0.79 (95% CI: 0.77–0.81)) and in-hospital mortality (<i>C</i> = 0.75 (95% CI: 0.71–0.78)). The COVID-19 Risk of Complications Score predicts the need for hospital admission and in-hospital mortality patients with COVID-19.Key points:Can an electronic health record generated risk score predict the risk of hospital admission and in-hospital mortality in patients diagnosed with coronavirus disease 2019 (COVID-19)?In both validation cohorts of 2,025 and 1,290 COVID-19, the cross-validated C-Statistics showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77–0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71–0.78)), respectively.The COVID-19 Risk of Complications Score may help predict the need for hospital admission if a patient contracts SARS-CoV-2 infection and in-hospital mortality for a hospitalized patient with COVID-19. Can an electronic health record generated risk score predict the risk of hospital admission and in-hospital mortality in patients diagnosed with coronavirus disease 2019 (COVID-19)? In both validation cohorts of 2,025 and 1,290 COVID-19, the cross-validated C-Statistics showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77–0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71–0.78)), respectively. The COVID-19 Risk of Complications Score may help predict the need for hospital admission if a patient contracts SARS-CoV-2 infection and in-hospital mortality for a hospitalized patient with COVID-19.