Jehi L, et al. Chest 2020.
BACKGROUND: Coronavirus disease-2019 (COVID-19) is sweeping the globe. Despite multiple case-series, actionable knowledge to proactively tailor decision-making is missing.
RESEARCH QUESTION: Can a statistical model accurately predict infection with COVID?
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 likelihood of a positive nasal or oropharyngeal COVID-19 test [COVID-19 (+)]. A least absolute shrinkage and selection operator (LASSO) logistic regression algorithm was constructed, which removed variables that were not contributing to the model's cross-validated concordance index. Following 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: 11,672 patients fulfilled study criteria in the development cohort, including 818 (7.0%) COVID-19 (+), and 2,295 patients fulfilled criteria in the validation cohort including 290 COVID-19 (+). Males, African Americans, older patients, and those with known COVID-19 exposure were at higher risk of being COVID-19 (+). Risk was reduced in those who had pneumococcal polysaccharide or influenza vaccine, or were on melatonin, paroxetine, or carvedilol. Our model had favorable discrimination (c-statistic=0.863 in development; 0.840 in validation cohort) and calibration. We present sensitivity, specificity, negative predictive value, and positive predictive value at different prediction cut-offs.The calculator is freely available at https://riskcalc.org/COVID19.
INTERPRETATION: Prediction of a COVID-19 (+) test is possible and could help direct healthcare resources. We demonstrate relevance of age, race, gender, and socioeconomic characteristics in COVID-19-susceptibility and suggest a potential modifying role of certain common vaccinations and drugs identified in drug-repurposing studies.