Predicting the risk of COVID-19 among adult patients with diabetes: A machine learning approach.

Objectives: To develop a machine learning model that accurately predicts the risk of acquiring COVID-19 in community-dwelling adults with type 1 and/or type 2 diabetes in Alberta, Canada.

Methods: This predictive supervised machine learning study included adults (>=18 years old) living in Alberta, Canada between April 1^st 2019-March 31^st 2021 with pre-existing diabetes (n=372,055, excluding n=2,541 due to migration; final sample size=369,514). The outcome of interest was a positive SARS-CoV-2 PCR test result between March 1st, 2020, and March 1st, 2021. Model features were extracted from routinely collected Alberta administrative health data from March 1^st 2015 to March 1^st 2020. Fifteen algorithms were trained on 67% of the data and the top performer (Light Gradient Boost Model, LGBoost) was validated on the remaining 33%. The model was calibrated, and model performance assessed using area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC) and threshold analyses.

Results: Among 369,514 individuals with diabetes, 140,511 were tested of whom 13,082 had a positive SARS-CoV-2 test. The LGBoost model incorporated 367 features with AUROC and AUPRC of 0.69 and 0.08 respectively. The model was well-calibrated for common risk thresholds (<0.2 probability) with high specificity (>=0.98 at all thresholds), however sensitivity and positive predictive values were low at all thresholds (<=0.08 and <=0.18 respectively).

Conclusions: The LGBoost model lacked the sensitivity to be clinically useful in predicting SARS-CoV-2 infection in Albertans with diabetes. Alternative data sources may be required to improve future COVID-19 prediction models from the community.