Individualizing risk prediction for positive COVID-19 testing: results from 11,672 patients
Autor
Jehi, Lara
Ji, Xinge
Milinovich, Alex
Erzurum, Serpil
Rubin, Brian
Gordon, Steve
Young, James
Kattan, Michael W.
Institución
Resumen
BACKGROUND: Coronavirus disease-2019 (COVID-19) is sweeping the globe. Despite multiple
case-series, actionable knowledge to tailor decision-making proactively is missing.
RESEARCH QUESTION: Can a statistical model accurately predict infection with COVID-19?
STUDY DESIGN AND METHODS: We developed a prospective registry of all patients tested for
COVID-19 in Cleveland Clinic to create individualized risk prediction models. We focus here
on the likelihood of a positive nasal or oropharyngeal COVID-19 test. A least absolute
shrinkage and selection operator logistic regression algorithm was constructed that removed
variables that were not contributing to the model’s cross-validated concordance index. After
external validation in a temporally and geographically distinct cohort, the statistical prediction model was illustrated as a nomogram and deployed in an online risk calculator.
RESULTS: In the development cohort, 11,672 patients fulfilled study criteria, including 818
patients (7.0%) who tested positive for COVID-19; in the validation cohort, 2295 patients
fulfilled criteria, including 290 patients who tested positive for COVID-19. Male, African
American, older patients, and those with known COVID-19 exposure were at higher risk of
being positive for COVID-19. Risk was reduced in those who had pneumococcal polysaccharide or influenza vaccine or who were on melatonin, paroxetine, or carvedilol. Our
model had favorable discrimination (c-statistic ¼ 0.863 in the development cohort and 0.840
in the validation cohort) and calibration. We present sensitivity, specificity, negative predictive value, and positive predictive value at different prediction cutoff points. The calculator
is freely available at https://riskcalc.org/COVID19.
INTERPRETATION: Prediction of a COVID-19 positive test is possible and could help direct
health care resources. We demonstrate relevance of age, race, sex, and socioeconomic
characteristics in COVID-19 susceptibility and suggest a potential modifying role of certain
common vaccinations and drugs that have been identified in drug-repurposing studies