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Background <p>Frailty is a reversible geriatric syndrome marked by diminished physiological reserve and heightened vulnerability. In elderly patients with chronic kidney disease (CKD), frailty accelerates decline and mortality, yet individualized risk stratification and management remain limited by resource-intensive geriatric assessment and incomplete data. Integrating causal learning and model knowledge distillation may enable precision modeling of frailty risk and clinically meaningful decision support.</p> Methods <p>This multicenter bidirectional cohort study (ChiCTR2500095133) will recruit 1500 adults aged ≥ 60 years with CKD (KDIGO criteria) from seven Beijing centers. Collected data will include routinely available clinical indices (demographics, medical history, anthropometric measures and laboratory tests) and resource-intensive non-clinical indices derived from comprehensive geriatric assessment (frailty, cognitive, functional, nutritional, and psychological assessments). Frailty will be assessed using the FRAIL scale. Causal feature learning will identify determinants of frailty risk, while a teacher-student knowledge-distillation framework will optimize prediction using routinely available clinical data. Internal and external validation will examine model accuracy, interpretability, and clinical applicability.</p> Discussion <p>By combining comprehensive clinical assessment with advanced artificial-intelligence methods, this study aims to develop an explainable, efficient tool for early frailty detection and individualized intervention decision support in elderly CKD. The resulting framework may enhance precision geriatric management, improve functional outcomes, and reduce healthcare burden.</p> Trial registration <p>Chinese Clinical Trial Registry: ChiCTR2500095133 (registered 2025-01-02).</p>

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Protocol for development of an AI-driven individualized frailty prediction and intervention framework for elderly patients with chronic kidney disease: causal feature learning and knowledge-distillation-based modeling study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月12日

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  • Jing Chang,
  • Jing Hu,
  • Ying Cao,
  • Xibin Jia,
  • Luo Wang,
  • Bo Liu,
  • Shaowu Xu,
  • Qing Ji,
  • Meiling Jin,
  • Qiuxia Han,
  • Yuer Liang,
  • Qianmei Sun

摘要

Background

Frailty is a reversible geriatric syndrome marked by diminished physiological reserve and heightened vulnerability. In elderly patients with chronic kidney disease (CKD), frailty accelerates decline and mortality, yet individualized risk stratification and management remain limited by resource-intensive geriatric assessment and incomplete data. Integrating causal learning and model knowledge distillation may enable precision modeling of frailty risk and clinically meaningful decision support.

Methods

This multicenter bidirectional cohort study (ChiCTR2500095133) will recruit 1500 adults aged ≥ 60 years with CKD (KDIGO criteria) from seven Beijing centers. Collected data will include routinely available clinical indices (demographics, medical history, anthropometric measures and laboratory tests) and resource-intensive non-clinical indices derived from comprehensive geriatric assessment (frailty, cognitive, functional, nutritional, and psychological assessments). Frailty will be assessed using the FRAIL scale. Causal feature learning will identify determinants of frailty risk, while a teacher-student knowledge-distillation framework will optimize prediction using routinely available clinical data. Internal and external validation will examine model accuracy, interpretability, and clinical applicability.

Discussion

By combining comprehensive clinical assessment with advanced artificial-intelligence methods, this study aims to develop an explainable, efficient tool for early frailty detection and individualized intervention decision support in elderly CKD. The resulting framework may enhance precision geriatric management, improve functional outcomes, and reduce healthcare burden.

Trial registration

Chinese Clinical Trial Registry: ChiCTR2500095133 (registered 2025-01-02).