Abstract

Background: Certain patients with diabetes and COVID-19 are at high risk of severe outcomes. Identification of risk factors among this group is required to risk-stratify those who may benefit from further surveillance. We aimed to develop machine learning (ML) models predicting severe outcomes among individuals with diabetes and COVID-19 in Alberta, Canada.

Methods: Patients with diabetes and COVID-19 determined by PCR test administered in community and/or emergency department (ED) settings (March 2020-March 2021) were included. Outcomes were ED visit, hospitalization or death for those tested in the community (“Community cohort”) and hospitalization or death for those tested in ED (“ED cohort”), and in the combined cohorts (“Community+ED cohort”). Outcomes and features (socio-demographics, drug/healthcare utilization, health history) were identified using healthcare administrative data (2008-2021). Calibration plots, areas under the receiver operating curve, precision-recall curves (AUC, AUPRC), and threshold analyses were used to assess the models.

Results: The Community cohort included 11,247 individuals (1,665 ED visits; 756 hospitalizations; 421 deaths). AUCs for models predicting ED/hospitalization/death were 0.65/0.70/0.93. The AUCs for predicting death in ED (1,495 individuals; 169 deaths) and Community+ED (12,410 individuals; 582 deaths) cohorts were 0.82 and 0.93. Models predicting hospitalization in these cohorts performed poorly and are not reported. Of all models, that predicting death in the Community performed best (sensitivity 0.77, specificity 0.91, positive predictive value 0.26, negative predictive value 0.99), and improved the prediction of death at a 10% risk threshold (compared to the pre-test probability, positive likelihood ratio 9.06 and negative likelihood ratio 0.25).

Conclusion: Identifying diabetes patients at the highest risk of the worst outcomes would assist in triaging patients to ensure appropriate resource use in times of high demand. Overall, the model predicting death among patients with diabetes and COVID-19 in the community could be useful in identifying who requires additional care.

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