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Enrolling by Invitation

NCT Number: NCT06842927

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.

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Key information

Conditions

End-stage Kidney Disease Chronic Disease Disease Disease Attributes End Stage Renal Disease (ESRD) End Stage Renal Disease on Dialysis (Diagnosis) End Stage Renal Failure on Dialysis Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Kidney Diseases Kidney Failure, Chronic Male Urogenital Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms Peritoneal Dialysis Peritoneal Dialysis Patients Renal Insufficiency Renal Insufficiency, Chronic Urogenital Diseases Urologic Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Tuen Mun Hospital

Tuenmen, Hong Kong

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Data Collection

Other

An additional collection of peritoneal dialysate and spot urine samples will be collected.

Participants randomized to the training/validation arm will have their data used for model development, including the training and validation phases.

Other names: model training

data report

Other

An additional collection of peritoneal dialysate and spot urine samples will be collected. Participants randomized to the test arm will have their data isolated and reserved exclusively for evaluating the performance of the final AI model

Other names: model testing

Primary outcomes

  1. 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 2-hour and 4-hour dialysate-to-plasma creatinine ratio (D/P Cr) Performance Metrics: Mean Absolute Error (MAE) Unit of Measure: Absolute error

  2. 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: Mean Absolute Error (MAE) Unit of Measure: Absolute error

  3. 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 2-hour and 4-hour dialysate-to-plasma creatinine ratio (D/P Cr) Performance Metrics: Mean Squared Error (MSE) Unit of Measure: Squared Error

  4. 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: Mean Squared Error (MSE) Unit of Measure: Squared Error

  5. 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 2-hour and 4-hour dialysate-to-plasma creatinine ratio (D/P Cr) Performance Metrics: Coefficient of Determination (R²) Unit of Measure: R² value (range: 0 to 1, higher values indicate better model performance)

  6. 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: Coefficient of Determination (R²) Unit of Measure: R² value (range: 0 to 1, higher values indicate better model performance)

  7. 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 2-hour and 4-hour dialysate-to-plasma creatinine ratio (D/P Cr) Performance Metrics: Intraclass Correlation Coefficient (ICC) Unit of Measure: ICC value (range: 0 to 1, higher values indicate better agreement)

  8. 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

  1. Dialysis Adequacy (Kt/V) parameters

    Time frame: 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: Mean Absolute Error (MAE) Unit of Measure: Absolute error

  2. Dialysis Adequacy (Kt/V) parameters

    Time frame: 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: Mean Squared Error (MSE) Unit of Measure: Squared Error

  3. Dialysis Adequacy (Kt/V) parameters

    Time frame: 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: Coefficient of Determination (R²) Unit of Measure: R² value (range: 0 to 1, higher values indicate better model performance)

  4. Dialysis Adequacy (Kt/V) parameters

    Time frame: 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)

  5. Discriminative Ability of AI Model

    Time frame: Measured at baseline during study enrollment

    Outcome: Classification of peritoneal transporter type (low, low-average, high-average, high) based on PET Performance Metrics: Area Under the Receiver Operating Characteristic Curve (AUC-ROC) Unit of Measure: AUC-ROC value (range: 0 to 1, higher values indicate better discriminative ability)

  6. Discriminative Ability of AI Model

    Time frame: Measured at baseline during study enrollment

    Outcome: Classification of peritoneal transporter type (low, low-average, high-average, high) based on PET Performance Metrics: Area Under the Precision-Recall Curve (AUC-PR) Unit of Measure: AUC-PR value (range: 0 to 1, higher values indicate better model performance)

  7. Discriminative Ability of AI Model

    Time frame: Measured at baseline during study enrollment

    Outcome: Classification of peritoneal transporter type (low, low-average, high-average, high) based on PET Performance Metrics: Sensitivity Unit of Measure: Sensitivity (%)

  8. Discriminative Ability of AI Model

    Time frame: 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)

  9. Calibration Performance of AI Model

    Time frame: Measured at baseline during study enrollment

    Outcome: Model-predicted vs. actual transporter status and dialysis adequacy (Kt/V) Performance Metrics: Calibration Slope Unit of Measure: Calibration slope (ideal value = 1)

  10. Calibration Performance of AI Model

    Time frame: Measured at baseline during study enrollment

    Outcome: Model-predicted vs. actual transporter status and dialysis adequacy (Kt/V) Performance Metrics: Calibration-in-the-large (Mean Calibration Error) Unit of Measure: Mean error (lower values indicate better calibration)

  11. Calibration Performance of AI Model

    Time frame: Measured at baseline during study enrollment

    Outcome: Model-predicted vs. actual transporter status and dialysis adequacy (Kt/V)

    Performance Metrics:

    Calibration Slope Calibration-in-the-large (Mean Calibration Error) Brier Score

  12. Calibration Performance of AI Model

    Time frame: 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)

Sponsors and collaborators

Lead sponsor

Tuen Mun Hospital

Other Gov

Registry information

Official study title

DETECT-PD -- Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis

Acronym: DETECT-PD

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Feb 24, 2025
Registry last updated
Apr 9, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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