Predicting death from COVID-19 using pre-existing conditions: implications for vaccination triage

Authors

Shujie Xiao, Henry Ford HealthFollow
Neha Sahasrabudhe, Henry Ford HealthFollow
Samantha Hochstadt, Henry Ford HealthFollow
Whitney Cabral, Henry Ford HealthFollow
Samantha Simons, Henry Ford HealthFollow
Mao Yang, Henry Ford HealthFollow
David E. Lanfear, Henry Ford HealthFollow
Keoki L. Williams, Henry Ford HealthFollow

Recommended Citation

Xiao S, Sahasrabudhe N, Hochstadt S, Cabral W, Simons S, Yang M, Lanfear DE, and Williams LK. Predicting death from COVID-19 using pre-existing conditions: implications for vaccination triage. BMJ Open Respir Res 2021; 8(1).

Document Type

Article

Publication Date

12-1-2021

Publication Title

BMJ Open Respir Res

Abstract

INTRODUCTION: Global shortages in the supply of SARS-CoV-2 vaccines have resulted in campaigns to first inoculate individuals at highest risk for death from COVID-19. Here, we develop a predictive model of COVID-19-related death using longitudinal clinical data from patients in metropolitan Detroit.

METHODS: All individuals included in the analysis had a laboratory-confirmed SARS-CoV-2 infection. Thirty-six pre-existing conditions with a false discovery rate p<0.05 were combined with other demographic variables to develop a parsimonious prediction model using least absolute shrinkage and selection operator regression. The model was then prospectively validated in a separate set of individuals with confirmed COVID-19.

RESULTS: The study population consisted of 15 502 individuals with laboratory-confirmed SARS-CoV-2. The main prediction model was developed using data from 11 635 individuals with 709 reported deaths (case fatality ratio 6.1%). The final prediction model consisted of 14 variables with 11 comorbidities. This model was then prospectively assessed among the remaining 3867 individuals (185 deaths; case fatality ratio 4.8%). When compared with using an age threshold of 65 years, the 14-variable model detected 6% more of the individuals who would die from COVID-19. However, below age 45 years and its risk equivalent, there was no benefit to using the prediction model over age alone.

DISCUSSION: Using a prediction model, such as the one described here, may help identify individuals who would most benefit from COVID-19 inoculation, and thereby may produce more dramatic initial drops in deaths through targeted vaccination.

Medical Subject Headings

Aged; COVID-19; COVID-19 Vaccines; Humans; Middle Aged; SARS-CoV-2; Triage; Vaccination

PubMed ID

34949575

Volume

8

Issue

1