Abstract: TH-PO1021
Vector Field Model of CKD Stage and Its Directional Derivative Mathematically Enable Accurate Kidney Prognosis Prediction
Session Information
- CKD: Epidemiology, Risk Factors, and Prevention - 1
October 24, 2024 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: CKD (Non-Dialysis)
- 2301 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention
Authors
- Kanda, Eiichiro, Kawasaki Ika Daigaku, Kurashiki, Okayama, Japan
- Epureanu, Bogdan I., University of Michigan, Ann Arbor, Michigan, United States
- Adachi, Taiji, Kyoto Daigaku, Kyoto, Japan
- Sasaki, Tamaki, Kawasaki Ika Daigaku, Kurashiki, Okayama, Japan
- Kashihara, Naoki, Kawasaki Ika Daigaku, Kurashiki, Okayama, Japan
Background
Chronic kidney disease (CKD) is the cause of end-stage kidney disease (ESKD), cardiovascular disease, and death, and is categorized into 18 stages on the basis of the estimated glomerular filtration rate (eGFR) and proteinuria. It is difficult to accurately predict CKD progression, because CKD stage cannot be mathematically analyzed in terms of scale and cut-off values. In this study, we determined whether CKD stage transformed into a vector field accurately predicts ESKD risk (CKD vector field model).
Methods
The distance from stage G1 A1 to a patient’s current stage in terms of on eGFR and proteinuria was defined, r. The model was constructed to reflect ESKD risk on the basis of systematic review of large cohort studies: ESKD risk=exp(r). Then, the model was validated using data from a cohort study of CKD patients in Japan followed up for three years (n=1,564). Moreover, the directional derivative of the model was developed as an index of CKD progression velocity.
Results
Cox proportional hazards models showed the exponential association between r and ESKD risk (p<0.0001). The CKD potential model more accurately predicted ESKD with the areas under the receiver operating characteristic curves adjusted for baseline characteristics 0.81 (95% CI 0.76, 0.87) than CKD stage 0.59 (95% CI 0.54, 0.63) (p<0.0001). Moreover, the directional derivative of the model better predicted the ESKD risk 0.77 (95% CI 0.71, 0.83) than eGFR slope 0.53 (95% CI 0.47, 0.60) (p<0.0001).
Conclusion
Those results indicated that the vector field model mathematically unifies CKD stage and eGFR slope and enables the accurate estimation of CKD progression.