Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis

Официальное название

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

Обзор

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.

Подробное описание

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.

Вмешательства

• Другое data collection
  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.
• Другое data report
  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

Первичные конечные точки

• Peritoneal Equilibration Test (PET) Parameters [Срок оценки: Measured at baseline during study enrollment]
• Peritoneal Equilibration Test (PET) Parameters [Срок оценки: Measured at baseline during study enrollment]
• Peritoneal Equilibration Test (PET) Parameters [Срок оценки: Measured at baseline during study enrollment]
• Peritoneal Equilibration Test (PET) Parameters [Срок оценки: Measured at baseline during study enrollment]
• Peritoneal Equilibration Test (PET) Parameters [Срок оценки: Measured at baseline during study enrollment]
• Peritoneal Equilibration Test (PET) Parameters [Срок оценки: Measured at baseline during study enrollment]
• Peritoneal Equilibration Test (PET) Parameters [Срок оценки: Measured at baseline during study enrollment]
• Peritoneal Equilibration Test (PET) Parameters [Срок оценки: Measured at baseline during study enrollment]

Вторичные конечные точки (12)

• Dialysis Adequacy (Kt/V) parameters [Срок оценки: Measured at baseline during study enrollment]
• Dialysis Adequacy (Kt/V) parameters [Срок оценки: Measured at baseline during study enrollment]
• Dialysis Adequacy (Kt/V) parameters [Срок оценки: Measured at baseline during study enrollment]
• Dialysis Adequacy (Kt/V) parameters [Срок оценки: Measured at baseline during study enrollment]
• Discriminative Ability of AI Model [Срок оценки: Measured at baseline during study enrollment]
• Discriminative Ability of AI Model [Срок оценки: Measured at baseline during study enrollment]
• Discriminative Ability of AI Model [Срок оценки: Measured at baseline during study enrollment]
• Discriminative Ability of AI Model [Срок оценки: Measured at baseline during study enrollment]
• Calibration Performance of AI Model [Срок оценки: Measured at baseline during study enrollment]
• Calibration Performance of AI Model [Срок оценки: Measured at baseline during study enrollment]
• Calibration Performance of AI Model [Срок оценки: Measured at baseline during study enrollment]
• Calibration Performance of AI Model [Срок оценки: Measured at baseline during study enrollment]

Критерии участия

Критерии включения

• 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.

Критерии исключения

• 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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Гонконг · 1 центр

• Tuen Mun Hospital — Tuenmen

Публикации

• Riley RD, Ensor J, Snell KIE, Harrell FE Jr, Martin GP, Reitsma JB, Moons KGM, Collins G, van Smeden M. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020 Mar 18;368:m441. doi: 10.1136/bmj.m441. No abstract available. PMID 32188600
• Szeto CC, Wong TY, Chow KM, Leung CB, Li PK. Dialysis adequacy and transport test for characterization of peritoneal transport type in Chinese peritoneal dialysis patients receiving three daily exchanges. Am J Kidney Dis. 2002 Jun;39(6):1287-99. doi: 10.1053/ajkd.2002.33405. PMID 12046043
• SPRINT Research Group; Wright JT Jr, Williamson JD, Whelton PK, Snyder JK, Sink KM, Rocco MV, Reboussin DM, Rahman M, Oparil S, Lewis CE, Kimmel PL, Johnson KC, Goff DC Jr, Fine LJ, Cutler JA, Cushman WC, Cheung AK, Ambrosius WT. A Randomized Trial of Intensive versus Standard Blood-Pressure Control. N Engl J Med. 2015 Nov 26;373(22):2103-16. doi: 10.1056/NEJMoa1511939. Epub 2015 Nov 9. PMID 26551272
• Chen CA, Lin SH, Hsu YJ, Li YC, Wang YF, Chiu JS. Neural network modeling to stratify peritoneal membrane transporter in predialytic patients. Intern Med. 2006;45(9):663-4. doi: 10.2169/internalmedicine.45.1419. Epub 2006 Jun 1. No abstract available. PMID 16755101
• Gu J, Bai E, Ge C, Winograd J, Shah AD. Peritoneal equilibration testing: Your questions answered. Perit Dial Int. 2023 Sep;43(5):361-373. doi: 10.1177/08968608221133629. Epub 2022 Nov 9. PMID 36350033
• Morelle J, Stachowska-Pietka J, Oberg C, Gadola L, La Milia V, Yu Z, Lambie M, Mehrotra R, de Arteaga J, Davies S. ISPD recommendations for the evaluation of peritoneal membrane dysfunction in adults: Classification, measurement, interpretation and rationale for intervention. Perit Dial Int. 2021 Jul;41(4):352-372. doi: 10.1177/0896860820982218. Epub 2021 Feb 10. PMID 33563110
• Blake PG, Bargman JM, Brimble KS, Davison SN, Hirsch D, McCormick BB, Suri RS, Taylor P, Zalunardo N, Tonelli M; Canadian Society of Nephrology Work Group on Adequacy of Peritoneal Dialysis. Clinical Practice Guidelines and Recommendations on Peritoneal Dialysis Adequacy 2011. Perit Dial Int. 2011 Mar-Apr;31(2):218-39. doi: 10.3747/pdi.2011.00026. No abstract available. PMID 21427259
• Chen JB, Lam KK, Su YJ, Lee WC, Cheng BC, Kuo CC, Wu CH, Lin E, Wang YC, Chen TC, Liao SC. Relationship between Kt/V urea-based dialysis adequacy and nutritional status and their effect on the components of the quality of life in incident peritoneal dialysis patients. BMC Nephrol. 2012 Jun 14;13:39. doi: 10.1186/1471-2369-13-39. PMID 22697882

Идентификаторы

NCT: NCT06842927 · CIRB-2024-569-5

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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