Machine learning models for the prediction of COVID-19 prognosis in the primary health care setting

Objective: This study aimed to identify prognostic factors associated with poor outcomes of COVID-19 at diagnosis in Primary Health Care (PHC). Methods: We conducted a retrospective, longitudinal study using the SIDIAP database, part of the PHC Information System of Catalonia. The analysis included COVID-19 cases diagnosed in patients aged 18 and older from March 2020 to September 2022. Follow-up was conducted for 90 days post-diagnosis or until death. Various machine learning models of differing complexities were used to predict short-term events, including mortality and hospital complications. Each model was tailored to maximize the predictive accuracy for poor outcomes, exploring algorithms such as Generalized Linear Models, flexible GLMs with Lasso, Gradient Boosting Models, and Support Vector Machines, with the model demonstrating the highest Area Under the Curve (AUC) selected for optimal performance. Results: A total of 2,162,187 COVID-19 cases were identified across five epidemic waves. Key predictors of short-term complications included age and the epidemic wave. Additional significant factors encompassed social deprivation (MEDEA), blood pressure, cardiovascular history, chronic obstructive pulmonary disease (COPD), obesity, and diabetes mellitus. The models exhibited high performance, with AUC values ranging from 0.73 to 0.95. A web application was developed to estimate the risk of adverse outcomes based on individual patient profiles (https://dapcat.shinyapps.io/CovidScore). Conclusions: In addition to age and epidemic wave, predictors such as social deprivation, diabetes mellitus, obesity, COPD, cardiovascular disease, high blood pressure, and dyslipidemia significantly indicate poor prognosis in COVID-19 patients diagnosed in PHC, and the developed application facilitates risk quantification for individual patients.