Kidney Week

Abstract: TH-PO006

Validation of Artificial Intelligence (AI)-Based Kidney Disease Progression Prediction (KDPP) Models in the US Population

Session Information

• Augmented Intelligence for Prediction and Image Analysis
  October 24, 2024 | Location: Exhibit Hall, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Augmented Intelligence, Digital Health, and Data Science

• 300 Augmented Intelligence, Digital Health, and Data Science

Authors

• Kuo, Chin-Chi, China Medical University Hospital, Taichung, Taiwan

Chin-Chi Kuo, MD, PhD, MPH

• Chen, Yichun, Ever Fortune.AI, Taichung, Taiwan

Yichun Chen, MS

• Chang, Yi-Ching, China Medical University Hospital, Taichung, Taiwan

Yi-Ching Chang, MS

• Lin, Yu-Ting, China Medical University Hospital, Taichung, Taiwan

Yu-Ting Lin, PhD

• Arya, Priyanka, AWAK Technologies, Singapore, Singapore

Priyanka Arya

• Aguilar, Ricardo, AWAK Technologies, Singapore, Singapore

Ricardo Aguilar, PhD

• Jain, Arsh, London Health Sciences Center, London, Ontario, Canada

Arsh Jain, MD, MSc

Background

AI and big data are revolutionizing personalized CKD management. KDPP models use deep learning to risk stratify patients for optimal treatment planning. Initially validated in Taiwan and granted FDA breakthrough device designation, this study tests KDPP's generalizability in U.S. using NIDDK's CRIC database.

Methods

KDPP includes 2 deep-learning models built using 9,529 CKD stage 3-5 patients from CMUH Taiwan: KDPP-RP for predicting rapid disease progression and KDPP-IR for forecasting renal replacement therapy (RRT). KDPP-IR was validated with CRIC (4,465 participants); KDPP-RP validation was limited by data scarcity. The models categorize patients into risk tiers (low, moderate, high) and are evaluated using AUC, sensitivity, specificity, and PPV/NPV.

Results

The CMUH cohort is older, has lower median eGFR (27 vs. 41.9 mL/min/1.73m²) than CRIC. KDPP-IR accurately predicted RRT with AUCs of 0.96 for CMUH and 0.89-0.92 for CRIC, performing well across races (Table 1). KDPP-IR's sensitivity and precision slightly decreased, but specificity improved. It reliably identified low-risk CRIC patients with NPVs of 0.99-1.00, though PPVs for high-risk varied (0.31-0.79). Kaplan-Meier curves showed distinct risk stratification, and calibration plots showed better agreement between observed and predicted risks than KFRE. (Figure 1).

Conclusion

The validation demonstrated robust AUCs and generalizability for risk prediction. Future enhancements will optimize both models for diverse ethnic groups to broaden their applicability.

Funding

• Commercial Support – AWAK Technologies