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.
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 Type
OBSERVATIONAL
Enrollment
350
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.
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
Tuen Mun Hospital
Tuenmen, Hong Kong
Peritoneal Equilibration Test (PET) Parameters
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
Time frame: Measured at baseline during study enrollment
Peritoneal Equilibration Test (PET) Parameters
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
Time frame: Measured at baseline during study enrollment
Peritoneal Equilibration Test (PET) Parameters
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
Time frame: Measured at baseline during study enrollment
Peritoneal Equilibration Test (PET) Parameters
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
Time frame: Measured at baseline during study enrollment
Peritoneal Equilibration Test (PET) Parameters
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)
Time frame: Measured at baseline during study enrollment
Peritoneal Equilibration Test (PET) Parameters
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)
Time frame: Measured at baseline during study enrollment
Peritoneal Equilibration Test (PET) Parameters
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)
Time frame: Measured at baseline during study enrollment
Peritoneal Equilibration Test (PET) Parameters
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)
Time frame: Measured at baseline during study enrollment
Dialysis Adequacy (Kt/V) parameters
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
Time frame: Measured at baseline during study enrollment
Dialysis Adequacy (Kt/V) parameters
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
Time frame: Measured at baseline during study enrollment
Dialysis Adequacy (Kt/V) parameters
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)
Time frame: Measured at baseline during study enrollment
Dialysis Adequacy (Kt/V) parameters
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)
Time frame: Measured at baseline during study enrollment
Discriminative Ability of AI Model
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)
Time frame: Measured at baseline during study enrollment
Discriminative Ability of AI Model
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)
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Time frame: Measured at baseline during study enrollment
Discriminative Ability of AI Model
Outcome: Classification of peritoneal transporter type (low, low-average, high-average, high) based on PET Performance Metrics: Sensitivity Unit of Measure: Sensitivity (%)
Time frame: Measured at baseline during study enrollment
Discriminative Ability of AI Model
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)
Time frame: Measured at baseline during study enrollment
Calibration Performance of AI Model
Outcome: Model-predicted vs. actual transporter status and dialysis adequacy (Kt/V) Performance Metrics: Calibration Slope Unit of Measure: Calibration slope (ideal value = 1)
Time frame: Measured at baseline during study enrollment
Calibration Performance of AI Model
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)
Time frame: Measured at baseline during study enrollment
Calibration Performance of AI Model
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
Time frame: Measured at baseline during study enrollment
Calibration Performance of AI Model
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)
Time frame: Measured at baseline during study enrollment