Predicting post-stroke cognitive impairment using machine learning: A prospective cohort study

Abstract

BackgroundPost-stroke cognitive impairment (PSCI) is a serious complication of stroke that warrants prompt detection and management. Consequently, the development of a diagnostic prediction model holds clinical significance. Objective: Machine learning algorithms were employed to identify crucial variables and forecast PSCI occurrence within 3–6 months following acute ischemic stroke (AIS). MethodsA prospective study was conducted on a developed cohort (331 patients) utilizing data from the Affiliated Zhongda Hospital of Southeast University between January 2022 and August 2022, as well as an external validation cohort (66 patients) from December 2022 to January 2023. The optimal model was determined by integrating nine machine learning classification models, and personalized risk assessment was facilitated by a Shapley Additive exPlanations (SHAP) interpretation. ResultsAge, education, baseline National Institutes of Health Scale (NIHSS), Cerebral white matter degeneration (CWMD), Homocysteine (Hcy), and C-reactive protein (CRP) were identified as predictors of PSCI occurrence. Gaussian Naïve Bayes (GNB) model was determined to be the optimal model, surpassing other classifier models in the validation set (area under the curve [AUC]: 0.925, 95 % confidence interval [CI]: 0.861 - 0.988) and achieving the lowest Brier score. The GNB model performed well in the test sets (AUC: 0.919, accuracy: 0.864, sensitivity: 0.818, and specificity: 0.932). ConclusionsThe present study involved the development of a GNB model and its elucidation through employment of the SHAP method. These findings provide compelling evidence for preventing PSCI, which could serve as a guide for high-risk patients to undertake appropriate preventive measures.