Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis

• Conditions: End-Stage Kidney Disease; End Stage Renal Disease on Dialysis (Diagnosis); Peritoneal Dialysis; Peritoneal Dialysis Patients; End Stage Renal Disease (ESRD); End Stage Renal Failure on Dialysis

• Registration Number: NCT06842927
• Lead Sponsor: Tuen Mun Hospital

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:

Data Preprocessing: Handling missing values, feature scaling, and one-hot encoding for categorical variables.

Feature Selection: Identifying the most predictive clinical and biochemical markers.

Model Training: Using deep learning regression models to predict PET and Kt/V outcomes.

Performance Evaluation: Evaluating model accuracy using:

Mean Absolute Error (MAE) Mean Squared Error (MSE) R² score (coefficient of determination) Bland-Altman plots and correlation coefficients for agreement with measured values.

Study Timeline

• Study First Submitted: 2025-02-02
• Study First Submitted QC: 2025-02-19
• Study First Posted: 2025-02-24
• Study Start: 2025-03-03
• Status Verified: 2025-04
• Last Update Submitted: 2025-04-07
• Last Update Posted: 2025-04-09
• Primary Completion: 2026-02-28
• Study Completion: 2026-03-31

Recruitment & Eligibility

• Status: ENROLLING_BY_INVITATION
• Sex: All
• Target Recruitment: 350

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Peritoneal Equilibration Test (PET) Parameters	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 Outcome Measures

Name	Time	Method
Dialysis Adequacy (Kt/V) parameters	Measured at baseline during study enrollment	Predictive Accuracy of AI Model for Dialysis Adequacy (Kt/V) Outcome: AI-predicted vs. actual total weekly Kt/V Performance Metrics: Intraclass Correlation Coefficient (ICC) Unit of Measure: ICC value (range: 0 to 1, higher values indicate better agreement)
Discriminative Ability of AI Model	Measured at baseline during study enrollment	Outcome: Classification of peritoneal transporter type (low, low-average, high-average, high) based on PET Performance Metrics: F1-score Unit of Measure: F1-score (range: 0 to 1, higher values indicate better balance between precision and recall)
Calibration Performance of AI Model	Measured at baseline during study enrollment	Outcome: Model-predicted vs. actual transporter status and dialysis adequacy (Kt/V) Performance Metrics: Brier Score Unit of Measure: Brier Score (range: 0 to 1, lower values indicate better calibration)

Trial Locations

Locations (1)

Tuen Mun Hospital; 🇭🇰 Tuen Mun, Hong Kong