Development of a multivariable model predicting mortality risk from comorbidities in an Italian cohort of 18,286 confirmed COVID-19 cases aged 40 years or older.

Objectives: to develop a risk prediction model for 30-day mortality from COVID‑19 in an Italian cohort aged 40 years or older.

Design: a population-based retrospective cohort study on prospectively collected data was conducted.

Setting And Participants: the cohort included all swab positive cases aged 40 years older (No. 18,286) among residents in the territory of the Milan's Agency for Health Protection (ATS-MI) up to 27.04.2020. Data on comorbidities were obtained from the ATS administrative database of chronic conditions.

Main Outcome Measures: to predict 30-day mortality risk, a multivariable logistic regression model, including age, gender, and the selected conditions, was developed following the TRIPOD guidelines. Discrimination and calibration of the model were assessed.

Results: after age and gender, the most important predictors of 30-day mortality were diabetes, tumour in first-line treatment, chronic heart failure, and complicated diabetes. The bootstrap-validated c-index was 0.78, which suggests that this model is useful in predicting death after COVID-19 infection in swab positive cases. The model had good discrimination (Brier score 0.13) and was well calibrated (Index of prediction accuracy of 14.8%).

Conclusions: a risk prediction model for 30-day mortality in a large COVID-19 cohort aged 40 years or older was developed. In a new epidemic wave, it would help to define groups at different risk and to identify high-risk subjects to target for specific prevention and therapeutic strategies.