Machine learning-based prediction model for cognitive frailty in elderly patients with ischaemic stroke: a prospective cohort study - Report - MDSpire

Machine learning-based prediction model for cognitive frailty in elderly patients with ischaemic stroke: a prospective cohort study

  • By

  • Xuan Chen

  • Linjie Zhou

  • Ying Zhang

  • Tuonan Liu

  • Bo Yan

  • Yang Li

  • Yan Hua

  • June 5, 2026

  • 0 min

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Clinical Report: Development of a Machine Learning Model to Predict Cognitive Frailty

Overview

This study developed a machine learning model to predict cognitive frailty (CF) in older adults following ischaemic stroke.

Background

Cognitive frailty (CF) is prevalent among older stroke survivors and is linked to adverse outcomes. This study aims to address the gap in understanding how multiple factors contribute to CF in this population.

Data Highlights

MetricValue
AUC0.889
Accuracy0.798
Sensitivity0.909
Patients with CF149 (37.1%)

Key Findings

The random forest model showed the best performance for predicting CF risk. Key predictors included discharge NIH Stroke Scale score, age, and white matter hyperintensity burden.

Clinical Implications

The findings indicate that neurological, nutritional, and psychosocial factors may contribute to CF risk in older stroke patients.

Conclusion

The study presents a machine learning model for predicting cognitive frailty in older adults post-stroke.

Related Resources & Content

  1. American Heart Association/American Stroke Association, PMC, 2023 -- Cognitive Impairment Following Ischemic and Hemorrhagic Stroke: A Scientific Statement
  2. Epidemiology and Outcomes Associated with Cognitive Frailty and Reserve in a Stroke Population: Systematic Review and Meta-Analysis, PMC, 2025
  3. Frontiers in Medicine — Interpretable machine learning-based predictive model for fall risk in older adults receiving maintenance hemodialysis
  4. BMC Psychiatry (Springer) — Modeling Predictive Factors for Suicidal Thoughts in Individuals Experiencing Cognitive Decline
  5. Journal of Medical Internet Research (JMIR) — Machine Learning and Deep Learning Models for Predicting Future Falls in Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Longitudinal Evidence
  6. Frontiers in Medicine — Comparing manual vs. automated machine learning and deep learning models for predicting one-year mortality in elderly hip fracture patients
  7. Interpretable machine learning-based predictive model for fall risk in older adults receiving maintenance hemodialysis
  8. Machine Learning and Deep Learning Models for Predicting Future Falls in Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Longitudinal Evidence
  9. Cognitive Impairment Following Ischemic and Hemorrhagic Stroke: A Scientific Statement from the American Heart Association/American Stroke Association - PMC
  10. Epidemiology and Outcomes Associated with Cognitive Frailty and Reserve in a Stroke Population: Systematic Review and Meta-Analysis - PMC
  11. Frontiers | Risk prediction models for frailty in stroke patients: a systematic review and meta-analysis

Original Source(s)

Machine learning-based prediction model for cognitive frailty in elderly patients with ischaemic stroke: a prospective cohort study

  • by Xuan Chen, Linjie Zhou, Ying Zhang, Tuonan Liu, Bo Yan, Yang Li, Yan Hua

  • June 5, 2026

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