In-hospital mortality predictors of stroke patients with diabetes mellitus

Objectives: The incidence of stroke is higher among type 2 diabetes mellitus (T2DM) patients with a higher mortality rate. Prognostic scores for stroke patients can assist with treatment planning and counseling. The objective of this study was to create a machine-learning-based prognostic score to estimate in-hospital mortality in acute stroke with T2DM. Materials and Methods: This study used data from claims-based diabetes registry at Dr. Sardjito General Hospital, Yogyakarta, Indonesia, to identify patients diagnosed with acute stroke and T2DM between January 2016 and December 2020. Four machine learning algorithms were trained and evaluated based on standard performance metrics. Important features were selected from the best-performing model and implemented in a web-based in-hospital mortality prediction scoring system. Results: Of the 18,652 patients in the registry, the final analytic dataset comprised 749 patients (557 survivors and 192 non-survivors). The random forest showed superiority compared to other models. The six most important features were length of stay, sepsis, pneumonia, age, dyslipidemia, and hemiplegia. Using these features, the web-based system estimates the probability of in-hospital death for an individual patient. Conclusion: Machine learning analysis may support an in-hospital mortality prediction score for patients with acute stroke and T2DM patients by leveraging the key features identified by the random forest model.