Predictive risk factors for hospitalization and response to colchicine in patients with COVID-19

Abstract

ObjectiveA predictive model for hospitalization due to COVID-19 or death was developed in the placebo group (N=2,084) from a large clinical trial of colchicine in COVID-19 patients (N = 4,159). ResultsThe 7 variables retained in the predictive model were age, gender, body-mass index, history of respiratory disease, use of diabetes drugs, use of anticoagulants, and use of oral steroids at the time of randomization. An optimal threshold value identified from the predictive model was used to classify high-risk patients (those with a predicted probability above the optimal threshold) and low-risk patients (those with a predicted probability below the optimal threshold). The number needed to treat to prevent 1 hospitalization or death with colchicine treatment decreased from 71 in the whole study population (N = 4,159) to 29 in the high-risk subgroup (N=1,692). ConclusionThis model could serve to identify high-risk subjects who will particularly benefit from early colchicine therapy.