Ignite Creation Date:
2025-12-25 @ 5:08 AM
Ignite Modification Date:
2025-12-26 @ 4:12 AM
Study NCT ID:
NCT06842927
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2025-04-09
First Post:
2025-02-02
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D007676', 'term': 'Kidney Failure, Chronic'}, {'id': 'D004194', 'term': 'Disease'}], 'ancestors': [{'id': 'D051436', 'term': 'Renal Insufficiency, Chronic'}, {'id': 'D051437', 'term': 'Renal Insufficiency'}, {'id': 'D007674', 'term': 'Kidney Diseases'}, {'id': 'D014570', 'term': 'Urologic Diseases'}, {'id': 'D052776', 'term': 'Female Urogenital Diseases'}, {'id': 'D005261', 'term': 'Female Urogenital Diseases and Pregnancy Complications'}, {'id': 'D000091642', 'term': 'Urogenital Diseases'}, {'id': 'D052801', 'term': 'Male Urogenital Diseases'}, {'id': 'D002908', 'term': 'Chronic Disease'}, {'id': 'D020969', 'term': 'Disease Attributes'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D003625', 'term': 'Data Collection'}, {'id': 'D012107', 'term': 'Research Design'}], 'ancestors': [{'id': 'D004812', 'term': 'Epidemiologic Methods'}, {'id': 'D008919', 'term': 'Investigative Techniques'}, {'id': 'D017531', 'term': 'Health Care Evaluation Mechanisms'}, {'id': 'D011787', 'term': 'Quality of Health Care'}, {'id': 'D017530', 'term': 'Health Care Quality, Access, and Evaluation'}, {'id': 'D011634', 'term': 'Public Health'}, {'id': 'D004778', 'term': 'Environment and Public Health'}, {'id': 'D008722', 'term': 'Methods'}]}}, 'documentSection': {'largeDocumentModule': {'largeDocs': [{'date': '2025-01-08', 'size': 238964, 'label': 'Study Protocol and Statistical Analysis Plan', 'hasIcf': False, 'hasSap': True, 'filename': 'Prot_SAP_000.pdf', 'typeAbbrev': 'Prot_SAP', 'uploadDate': '2025-02-13T13:02', 'hasProtocol': True}, {'date': '2025-01-08', 'size': 370440, 'label': 'Informed Consent Form', 'hasIcf': True, 'hasSap': False, 'filename': 'ICF_001.pdf', 'typeAbbrev': 'ICF', 'uploadDate': '2025-02-13T13:04', 'hasProtocol': False}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 350}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ENROLLING_BY_INVITATION', 'startDateStruct': {'date': '2025-03-03', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-04', 'completionDateStruct': {'date': '2026-03-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-04-07', 'studyFirstSubmitDate': '2025-02-02', 'studyFirstSubmitQcDate': '2025-02-19', 'lastUpdatePostDateStruct': {'date': '2025-04-09', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-02-24', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-02-28', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Peritoneal Equilibration Test (PET) Parameters', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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'}, {'measure': 'Peritoneal Equilibration Test (PET) Parameters', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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'}, {'measure': 'Peritoneal Equilibration Test (PET) Parameters', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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'}, {'measure': 'Peritoneal Equilibration Test (PET) Parameters', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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'}, {'measure': 'Peritoneal Equilibration Test (PET) Parameters', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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)'}, {'measure': 'Peritoneal Equilibration Test (PET) Parameters', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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)'}, {'measure': 'Peritoneal Equilibration Test (PET) Parameters', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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)'}, {'measure': 'Peritoneal Equilibration Test (PET) Parameters', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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)'}], 'secondaryOutcomes': [{'measure': 'Dialysis Adequacy (Kt/V) parameters', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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'}, {'measure': 'Dialysis Adequacy (Kt/V) parameters', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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'}, {'measure': 'Dialysis Adequacy (Kt/V) parameters', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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)'}, {'measure': 'Dialysis Adequacy (Kt/V) parameters', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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)'}, {'measure': 'Discriminative Ability of AI Model', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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)'}, {'measure': 'Discriminative Ability of AI Model', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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)'}, {'measure': 'Discriminative Ability of AI Model', 'timeFrame': 'Measured at baseline during study enrollment', 'description': 'Outcome: Classification of peritoneal transporter type (low, low-average, high-average, high) based on PET Performance Metrics: Sensitivity Unit of Measure: Sensitivity (%)'}, {'measure': 'Discriminative Ability of AI Model', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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)'}, {'measure': 'Calibration Performance of AI Model', 'timeFrame': 'Measured at baseline during study enrollment', 'description': 'Outcome: Model-predicted vs. actual transporter status and dialysis adequacy (Kt/V) Performance Metrics: Calibration Slope Unit of Measure: Calibration slope (ideal value = 1)'}, {'measure': 'Calibration Performance of AI Model', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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)'}, {'measure': 'Calibration Performance of AI Model', 'timeFrame': 'Measured at baseline during study enrollment', 'description': 'Outcome: Model-predicted vs. actual transporter status and dialysis adequacy (Kt/V)\n\nPerformance Metrics:\n\nCalibration Slope Calibration-in-the-large (Mean Calibration Error) Brier Score'}, {'measure': 'Calibration Performance of AI Model', 'timeFrame': 'Measured at baseline during study enrollment', 'description': '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)'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Peritoneal dialysis', 'Artificial intelligence', 'Dialysis adequacy', 'Peritoneum transporter status', 'Renal replacement therapy'], 'conditions': ['End-Stage Kidney Disease', 'End Stage Renal Disease (ESRD)', 'End Stage Renal Disease on Dialysis (Diagnosis)', 'End Stage Renal Failure on Dialysis', 'Peritoneal Dialysis', 'Peritoneal Dialysis Patients']}, 'referencesModule': {'references': [{'pmid': '32188600', 'type': 'BACKGROUND', 'citation': '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': '12046043', 'type': 'BACKGROUND', 'citation': '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': '26551272', 'type': 'BACKGROUND', 'citation': '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': '16755101', 'type': 'BACKGROUND', 'citation': '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': '36350033', 'type': 'BACKGROUND', 'citation': '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': '33563110', 'type': 'BACKGROUND', 'citation': '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': '21427259', 'type': 'BACKGROUND', 'citation': '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': '22697882', 'type': 'BACKGROUND', 'citation': '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': '38490516', 'type': 'BACKGROUND', 'citation': 'Lin YL, Lee YC, Lee CC, Wu MH. Role of Peritoneal Equilibration Test in Assessing Folate Transport During Peritoneal Dialysis. J Ren Nutr. 2024 Sep;34(5):463-468. doi: 10.1053/j.jrn.2024.02.003. Epub 2024 Mar 13.'}, {'pmid': '19776045', 'type': 'BACKGROUND', 'citation': 'Cnossen TT, Smit W, Konings CJ, Kooman JP, Leunissen KM, Krediet RT. Quantification of free water transport during the peritoneal equilibration test. Perit Dial Int. 2009 Sep-Oct;29(5):523-7.'}, {'pmid': '2663040', 'type': 'BACKGROUND', 'citation': 'Twardowski ZJ. Clinical value of standardized equilibration tests in CAPD patients. Blood Purif. 1989;7(2-3):95-108. doi: 10.1159/000169582.'}, {'pmid': '36114414', 'type': 'BACKGROUND', 'citation': 'Bello AK, Okpechi IG, Osman MA, Cho Y, Cullis B, Htay H, Jha V, Makusidi MA, McCulloch M, Shah N, Wainstein M, Johnson DW. Epidemiology of peritoneal dialysis outcomes. Nat Rev Nephrol. 2022 Dec;18(12):779-793. doi: 10.1038/s41581-022-00623-7. Epub 2022 Sep 16.'}]}, 'descriptionModule': {'briefSummary': '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).\n\nThe main questions it aims to answer are:\n\nCan artificial intelligence predict peritoneal transporter status based on simple clinical and biochemical measurements? Can artificial intelligence predict dialysis adequacy (Kt/V) using these features?\n\nResearchers 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.\n\nParticipants will:\n\nProvide 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.\n\nThe 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.', 'detailedDescription': '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.\n\nPatient 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:\n\nDemographics \\& Medical History Peritoneal Dialysis Data Biochemical Data\n\nThe AI model will be developed using Python 3.11 and PyTorch 2.41 for deep learning and predictive analytics.\n\nThe key methodological steps include:\n\nData Preprocessing: Handling missing values, feature scaling, and one-hot encoding for categorical variables.\n\nFeature Selection: Identifying the most predictive clinical and biochemical markers.\n\nModel Training: Using deep learning regression models to predict PET and Kt/V outcomes.\n\nPerformance Evaluation: Evaluating model accuracy using:\n\nMean Absolute Error (MAE) Mean Squared Error (MSE) R² score (coefficient of determination) Bland-Altman plots and correlation coefficients for agreement with measured values.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'End-stage renal failure patients requiring peritoneal dialysis as renal replacement therapy', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Age 18 years or older\n* Diagnosis of end-stage renal failure requiring peritoneal dialysis as renal replacement therapy\n* Ability to give informed consent and comply with study procedures.\n\nExclusion Criteria:\n\n* History of hernia or peritoneal leak, including pleuroperitoneal fistula (PPF), patent processus vaginalis (PPV) and retroperitoneal leak\n* Ongoing PD peritonitis with or without antibiotic therapy\n* Just finished PD peritonitis antibiotic treatment within recent 4 weeks\n* Pregnancy\n* Patient refusal'}, 'identificationModule': {'nctId': 'NCT06842927', 'acronym': 'DETECT-PD', 'briefTitle': 'Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis', 'organization': {'class': 'OTHER_GOV', 'fullName': 'Tuen Mun Hospital'}, 'officialTitle': 'DETECT-PD -- Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis', 'orgStudyIdInfo': {'id': 'CIRB-2024-569-5'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Training/Validation', 'description': 'Participants in training/validation arm will receive the same standard investigations and care as part of their routine PD management, including clinical evaluations, biochemical testing, and measurements of peritoneal transporter status via the Peritoneal Equilibrium Test (PET) and dialysis adequacy (Kt/V).', 'interventionNames': ['Other: data collection']}, {'label': 'Test', 'description': 'Participants in training/validation arm will receive the same standard investigations and care as part of their routine PD management, including clinical evaluations, biochemical testing, and measurements of peritoneal transporter status via the Peritoneal Equilibrium Test (PET) and dialysis adequacy (Kt/V).', 'interventionNames': ['Other: data report']}], 'interventions': [{'name': 'data collection', 'type': 'OTHER', 'otherNames': ['model training'], 'description': 'An additional collection of peritoneal dialysate and spot urine samples will be collected.\n\nParticipants randomized to the training/validation arm will have their data used for model development, including the training and validation phases.', 'armGroupLabels': ['Training/Validation']}, {'name': 'data report', 'type': 'OTHER', 'otherNames': ['model testing'], 'description': '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', 'armGroupLabels': ['Test']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Tuenmen', 'country': 'Hong Kong', 'facility': 'Tuen Mun Hospital', 'geoPoint': {'lat': 22.39175, 'lon': 113.97157}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Tuen Mun Hospital', 'class': 'OTHER_GOV'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Resident Specialist', 'investigatorFullName': 'Ka Chun Leung', 'investigatorAffiliation': 'Tuen Mun Hospital'}}}}