TY - JOUR
T1 - Development and validation of the COVID-NoLab and COVID-SimpleLab risk scores for prognosis in 6 US health systems
AU - Ebell, Mark H.
AU - Cai, Xinyan
AU - Lennon, Robert
AU - Tarn, Derjung M.
AU - Mainous, Arch G.
AU - Zgierska, Aleksandra E.
AU - Barrett, Bruce
AU - Tuan, Wen Jan
AU - Maloy, Kevin
AU - Goyal, Munish
AU - Krist, Alex
N1 - Publisher Copyright:
© 2021 American Board of Family Medicine. All rights reserved.
PY - 2021/2
Y1 - 2021/2
N2 - Purpose: 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 gathered clinical and initial laboratory variables on consecutive inpatients with COVID-19 who had either died or been discharged alive at 6 US health centers. Logistic regression was used to develop a predictive model using no laboratory values (COVID-NoLab) and one adding tests available in many outpatient settings (COVID-SimpleLab). The models were converted to point scores and their accuracy evaluated in an internal validation group. Results: We identified 1340 adult inpatients with complete data for nonlaboratory parameters and 741 with complete data for white blood cell (WBC) count, differential, c-reactive 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 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). Conclusions: Because they use simple, readily available predictors, developed risk scores have potential applicability in the outpatient setting but require prospective validation before use.
AB - Purpose: 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 gathered clinical and initial laboratory variables on consecutive inpatients with COVID-19 who had either died or been discharged alive at 6 US health centers. Logistic regression was used to develop a predictive model using no laboratory values (COVID-NoLab) and one adding tests available in many outpatient settings (COVID-SimpleLab). The models were converted to point scores and their accuracy evaluated in an internal validation group. Results: We identified 1340 adult inpatients with complete data for nonlaboratory parameters and 741 with complete data for white blood cell (WBC) count, differential, c-reactive 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 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). Conclusions: Because they use simple, readily available predictors, developed risk scores have potential applicability in the outpatient setting but require prospective validation before use.
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U2 - 10.3122/JABFM.2021.S1.200464
DO - 10.3122/JABFM.2021.S1.200464
M3 - Article
C2 - 33622827
AN - SCOPUS:85101930345
SN - 1557-2625
VL - 34
SP - S127-S135
JO - Journal of the American Board of Family Medicine
JF - Journal of the American Board of Family Medicine
ER -