DETECT-PD -- Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis
DETECT-PD -- 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.
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.
Inclusion Criteria:
Exclusion Criteria: