Predictive models for active suicidal ideation in cognitive decline: identifying risk factors

Suicide rates among older adults with cognitive decline represent a critical public health concern. Despite the association between cognitive decline and suicidality, predictive models for active suicidal ideation (ASI) in this population remain underexplored. A retrospective study of 1,889 patients with cognitive decline was conducted using electronic health records. Sociodemographic, cognitive, clinical, psychiatric, and functional variables were analyzed. Univariate logistic regression identified correlates of ASI, followed by multivariate predictive modeling using Logistic Regression (LR) and XGBoost. Recursive Feature Elimination (RFE) identified key predictors, and SHAP values provided model interpretability. Depressive symptoms, Mini-Mental-State-Exam score, duration of cognitive decline, past suicide attempts, antidementia medication use, and living arrangement emerged as key predictors. Both LR and XGBoost demonstrated robust performance (ROC AUC: 0.81–0.70; PR AUC: 0.55). Multivariate predictive models provide improved risk stratification for ASI, highlighting the need for targeted interventions among individuals with cognitive decline.