AI tool to predict dialysis efficiency in peritoneal dialysis patients
Tests whether artificial intelligence can predict dialysis efficiency in adults on peritoneal dialysis.
Plain-language title generated from the registry record
Registry title: Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis
Key facts
- Where
- Tuenmen, Hong KongSee the site
Where in Hong Kong
- Tuen Mun HospitalTuenmen, Hong KongNo longer taking part
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Tuen Mun Hospital
- Trial code
- NCT06842927
- Record updated
- 9 April 2025
- Original description
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).Read full original descriptionShow less
DETECT-PD -- 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.
Inclusion: Age 18 years or older; Diagnosis of end-stage renal failure requiring peritoneal dialysis as renal replacement therapy; Ability to give informed consent and comply with study procedures.
Exclusion: Ongoing PD peritonitis with or without antibiotic therapy; History of hernia or peritoneal leak, including pleuroperitoneal fistula (PPF), patent processus vaginalis (PPV) and retroperitoneal leak; Just finished PD peritonitis antibiotic treatment within recent 4 weeks; Pregnancy; Patient refusal.