25 October 2025
RESOURCES
Real World Validation and Optimization of CKD Progression Prediction Models Using U.S. Mayo Clinic Data
Authors
- David R. Chang, Division of Nephrology, China Medical University Hospital, Taichung, Taiwan
- Chin-Chi Kuo, Big Data Center, China Medical University Hospital, Taichung, Taiwan
- Yu-Ting Lin, Big Data Center, China Medical University Hospital, Taichung, Taiwan
- Yi-Chun Chen, Big Data Center, China Medical University Hospital, Taichung, Taiwan
- Priyanka Arya, Vivance, Singapore
- Ricardo Aguilar, Vivance, Singapore
- Arsh Jain, London Health Sciences Centre, London, Ontario, Canada
Background
Accurate chronic-kidney-disease (CKD) risk prediction tools stall in practice because data elements are often missing. Two deep-learning models, RRT Onset and Rapid Progression (RP), originally developed in Taiwan (China Medical University Hospital, CMUH) and granted FDA Breakthrough Device Designation were tested and optimized using a large, racially diverse U.S. cohort from the Mayo Clinic Platform (MCP).
Methods
We analyzed 232,613 stage 3–5 CKD patients (25,214 under nephrology care) treated at Mayo Clinic sites (AZ, FL, MN) between 2018 and 2024. Outcomes were (1) RRT initiation and (2) eGFR decline (≥40%, slope ≤-5 mL/min/1.73m2/yr, or eGFR <15) within 2 and 5 years. Model variants assessed were: original, fine-tuned, and de novo (trained exclusively on MCP data). Discrimination (AUC), sensitivity, and specificity were compared with 4-variable Kidney Failure Risk Equation (KFRE).
Results
The fine-tuned AI models delivered robust discrimination (RRT-AUC 0.85–0.86; RP-AUC 0.78–0.81). uACR was absent in 46% of records, resulting in an inability to calculate KFRE. Against KFRE (AUC 0.81), fine-tuned RRT predictions improved event reclassification by 12.9–41.4%, while maintaining sensitivity >78% and specificity >75%. Notably, de novo model did not offer significant performance advantages over the fine-tuned model (Table 1). Event rates increased with higher risk scores supporting effective risk stratification (Figure 1).
Conclusion
FDA-designated AI models preserved accuracy despite nearly 50% data missingness and outperformed KFRE across U.S. centers. A rapid fine-tuning step, rather than de novo training, enables seamless local deployment, facilitating prospective trials and accelerating clinical adoption.
Presented at ASN Kidney Week 2025, Houston, TX, Nov 5 – Nov 9, 2025
Copyright © 2025 by the American Society of Nephrology
Published abstract available at: 36(10S):10.1681/ASN.2025re6x6kqf, October 2025.