Mark H. Ebell, MD, MS; Xinyan Cai, MPH; Robert Lennon, MD; Derjung M. Tarn, MD, PhD; Arch G. Mainous III, PhD; Aleksandra E. Zgierska, MD, PhD; Bruce Barrett, MD, PhD; Wen-Jan Tuan, DHA, MS, MPH; Kevin Maloy, MD; Munish Goyal, MD; Alex Krist MD, MPH, PhD
Corresponding Author: Mark H. Ebell, MD, MS; UGA Health Sciences Campus. Email: ebell@uga.edu
Section: Original Research
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Purpose: To develop and validate simple risk scores based on initial clinical data and no or minimal laboratory testing to predict mortality in hospitalized adults with COVID-19. Methods: We identified consecutive inpatients with COVID-19 who had either died or been discharged alive at six US health centers and gathered clinical and initial laboratory variables n admission. Data were divided into derivation and validation groups. Logistic regression was used to develop two predictive models, one using no laboratory values (COVID-NoLab) and one adding tests available in many outpatient settings (COVID-SimpleLab). The regression models were converted to point scores and their accuracy evaluated in the validation group. Results: We identified 1340 adult inpatients who had complete data for non-laboratory parameters and 741 who had complete data for white blood cell count (WBC), differential, creactive protein (CRP), and serum creatinine. The COVID-NoLab risk score includes age, respiratory rate, and oxygen saturation and identified risk groups with 0.8%, 11.4% and 40.4% mortality in the validation group (AUROCC=0.803). The COVID-SimpleLab risk score includes age, respiratory rate, oxygen saturation, WBC, CRP, serum creatinine and comorbid asthma, and identified risk groups with 1.0%, 9.1%, and 29.3% mortality in the validation group (AUROCC=0.833). Calibration of both models was good. Conclusions: We developed and internally validated two simple risk scores for hospitalized patients that require either no or minimal laboratory testing. Due to their limited data requirements these risk scores have potential applicability in the outpatient setting, but require prospective validation in that setting before being used.