Clinical trial NCT06842927: Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis
Official title: DETECT-PD -- Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis
Study type: OBSERVATIONAL
Overall status: ENROLLING_BY_INVITATION
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
Keywords: Peritoneal dialysis
Artificial intelligence
Dialysis adequacy
Peritoneum transporter status
Renal replacement therapy
Lead sponsor: Tuen Mun Hospital
Sponsor class: OTHER_GOV
First posted: 2025-02-24
Enrollment: 350
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: End-stage renal failure patients requiring peritoneal dialysis as renal replacement therapy

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.

Eligibility criteria: 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

Primary outcomes:
measure: Peritoneal Equilibration Test (PET) Parameters
timeFrame: Measured at baseline during study enrollment
measure: Peritoneal Equilibration Test (PET) Parameters
timeFrame: Measured at baseline during study enrollment
measure: Peritoneal Equilibration Test (PET) Parameters
timeFrame: Measured at baseline during study enrollment
measure: Peritoneal Equilibration Test (PET) Parameters
timeFrame: Measured at baseline during study enrollment
measure: Peritoneal Equilibration Test (PET) Parameters
timeFrame: Measured at baseline during study enrollment
measure: Peritoneal Equilibration Test (PET) Parameters
timeFrame: Measured at baseline during study enrollment
measure: Peritoneal Equilibration Test (PET) Parameters
timeFrame: Measured at baseline during study enrollment
measure: Peritoneal Equilibration Test (PET) Parameters
timeFrame: Measured at baseline during study enrollment
Secondary outcomes:
measure: Dialysis Adequacy (Kt/V) parameters
timeFrame: Measured at baseline during study enrollment
measure: Dialysis Adequacy (Kt/V) parameters
timeFrame: Measured at baseline during study enrollment
measure: Dialysis Adequacy (Kt/V) parameters
timeFrame: Measured at baseline during study enrollment
measure: Dialysis Adequacy (Kt/V) parameters
timeFrame: Measured at baseline during study enrollment
measure: Discriminative Ability of AI Model
timeFrame: Measured at baseline during study enrollment
measure: Discriminative Ability of AI Model
timeFrame: Measured at baseline during study enrollment
measure: Discriminative Ability of AI Model
timeFrame: Measured at baseline during study enrollment
measure: Discriminative Ability of AI Model
timeFrame: Measured at baseline during study enrollment
measure: Calibration Performance of AI Model
timeFrame: Measured at baseline during study enrollment
measure: Calibration Performance of AI Model
timeFrame: Measured at baseline during study enrollment
measure: Calibration Performance of AI Model
timeFrame: Measured at baseline during study enrollment
measure: Calibration Performance of AI Model
timeFrame: Measured at baseline during study enrollment
Other outcomes:
Locations:
city: Tuen Mun
country: Hong Kong
facility: Tuen Mun Hospital
Study officials:
References:
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