Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis

The goal of this prospective diagnostic test (correlation) study is to develop and investigate the performance of artificial intelligence in predicting peritoneum transporter status and dialysis efficiency in adult patients undergoing peritoneal dialysis (PD). The main questions it aims to answer are: Can artificial intelligence predict peritoneal transporter status based on simple clinical and biochemical measurements? Can artificial intelligence predict dialysis adequacy (Kt/V) using these features? Researchers will compare the performance of the AI model with the gold standard Peritoneal Equilibration Test (PET) and Kt/V to evaluate its accuracy and reliability. Participants will: Provide peritoneal dialysate and spot urine samples for biochemical analysis. Undergo routine dialysis adequacy and peritoneal equilibration testing (PET). Have clinical and laboratory data collected for AI model training and validation. The study will recruit approximately 350 peritoneal dialysis patients, with 280 participants in the training/validation arm and 70 participants in the test arm. The study duration is 12 months following enrollment.

NCT Number	NCT06842927
Lead Sponsor	Tuen Mun Hospital
Conditions	End-Stage Kidney Disease, End Stage Renal Disease (ESRD), End Stage Renal Disease on Dialysis (Diagnosis), End Stage Renal Failure on Dialysis, Peritoneal Dialysis, Peritoneal Dialysis Patients
Enrollment	350 participants
Start Date	2025-03-03
Primary Completion	2026-02-28 (estimated)
Study Completion	2026-03-31 (estimated)
Updated on ClinicalTrials.gov	2025-04-09