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Background <p>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.</p> Methods <p>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.</p> Results <p>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).</p> Conclusion <p>Multivariate predictive models provide improved risk stratification for ASI, highlighting the need for targeted interventions among individuals with cognitive decline.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月21日

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  • Eva Vidovič,
  • Jernej Rudi Finžgar,
  • Anja Kokalj Palandacic,
  • Polona Rus Prelog

摘要

Background

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.

Methods

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.

Results

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).

Conclusion

Multivariate predictive models provide improved risk stratification for ASI, highlighting the need for targeted interventions among individuals with cognitive decline.