DETECT-PD -- Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 入组人数
- 350
- 试验地点
- 1
- 主要终点
- Peritoneal Equilibration Test (PET) Parameters
研究概览
简要总结
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.
详细描述
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:
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •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
结局指标
主要结局
Peritoneal Equilibration Test (PET) Parameters
时间窗: 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)
次要结局
- Dialysis Adequacy (Kt/V) parameters(Measured at baseline during study enrollment)
- Discriminative Ability of AI Model(Measured at baseline during study enrollment)
- Calibration Performance of AI Model(Measured at baseline during study enrollment)
研究者
Ka Chun Leung
Resident Specialist
Tuen Mun Hospital