# 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

## TL;DR

This study aims to develop an AI tool to predict and manage frailty in elderly patients with kidney disease using clinical data and advanced modeling techniques.

## Contribution

The novel approach combines causal feature learning and knowledge distillation to create an individualized frailty prediction framework for CKD patients.

## Key findings

- The study will recruit 1500 elderly CKD patients across seven Beijing centers for data collection.
- Causal learning and knowledge distillation will be used to identify frailty determinants and optimize prediction models.
- The framework aims to improve early detection and personalized intervention for frailty in CKD patients.

## Abstract

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.

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.

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.

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

The online version contains supplementary material available at 10.1186/s12877-026-07143-0.