DETECT-PD -- Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis
Trial Snapshot
- Phase
- Not Applicable
- Status
- Enrolling By Invitation
- Sponsor
- Tuen Mun Hospital
- Enrollment
- 350
- Locations
- 1
- Primary Endpoint
- Peritoneal Equilibration Test (PET) Parameters
Study Overview
Brief Summary
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.
Detailed Description
The DETECT-PD (Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis) study is a double-blind, prospective diagnostic test (correlation) study designed to evaluate the feasibility and effectiveness of artificial intelligence (AI) in predicting peritoneal transporter status and dialysis efficiency in patients undergoing peritoneal dialysis (PD). The study aims to develop a computational model that leverages clinical, biochemical, and peritoneal transport data to provide a non-invasive and efficient assessment tool, ultimately improving dialysis management and patient outcomes.
Patient recruitment and data collection will be conducted during routine dialysis adequacy and peritoneal transporter status assessments. The following clinical and biochemical parameters will be collected:
Demographics & Medical History Peritoneal Dialysis Data Biochemical Data
The AI model will be developed using Python 3.11 and PyTorch 2.41 for deep learning and predictive analytics.
The key methodological steps include:
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •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 Criteria
- •History of hernia or peritoneal leak, including pleuroperitoneal fistula (PPF), patent processus vaginalis (PPV) and retroperitoneal leak
- •Ongoing PD peritonitis with or without antibiotic therapy
- •Just finished PD peritonitis antibiotic treatment within recent 4 weeks
- •Pregnancy
- •Patient refusal
Outcomes
Primary Outcomes
Peritoneal Equilibration Test (PET) Parameters
Time Frame: Measured at baseline during study enrollment
Predictive Accuracy of AI Model for Peritoneal Equilibration Test (PET) Parameters Outcome: AI-predicted vs. actual dialysate-to-baseline dialysate glucose concentration ratio (D/D0 Glu) Performance Metrics: Intraclass Correlation Coefficient (ICC) Unit of Measure: ICC value (range: 0 to 1, higher values indicate better agreement)
Secondary Outcomes
- Dialysis Adequacy (Kt/V) parameters(Measured at baseline during study enrollment)
- Discriminative Ability of AI Model(Measured at baseline during study enrollment)
- Calibration Performance of AI Model(Measured at baseline during study enrollment)
Investigators
Ka Chun Leung
Resident Specialist
Tuen Mun Hospital