Ignite Creation Date:
2025-12-25 @ 12:36 AM
Ignite Modification Date:
2025-12-25 @ 10:45 PM
Study NCT ID:
NCT06775067
Status:
COMPLETED
Last Update Posted:
2025-01-14
First Post:
2025-01-05
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Incremental Dialysis Decision Model Based on Expert-Guided Machine Learning
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D007676', 'term': 'Kidney Failure, Chronic'}], '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'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 175}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2010-04-12', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-01', 'completionDateStruct': {'date': '2024-06-28', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-01-09', 'studyFirstSubmitDate': '2025-01-05', 'studyFirstSubmitQcDate': '2025-01-09', 'lastUpdatePostDateStruct': {'date': '2025-01-14', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-01-14', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-06-28', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Number (Proportion) of Participants Who Experience an Incremental Dialysis Event, Assessed Monthly', 'timeFrame': 'Baseline and monthly visits from enrollment until incremental dialysis event, death, transfer, or up to 5 years (whichever occurs first)', 'description': "An incremental dialysis event is defined as an increase in a patient's dialysis frequency (e.g., from 1 session per week to 2 sessions per week, or from 2 to 3 sessions per week, etc.) due to clinical considerations such as decreased residual renal function, fluid overload, or other physician-determined criteria. At each monthly visit (up to 5 years from enrollment), investigators will record whether each participant experiences an incremental event. We will quantify the primary outcome as the number and proportion of participants who transition to a higher dialysis frequency per month, as well as the cumulative incidence over time."}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Machine Learning', 'Expert Knowledge', 'Interpretability', 'Incremental Hemodialysis', 'Decision Model'], 'conditions': ['End-stage Renal Disease']}, 'descriptionModule': {'briefSummary': 'This observational prospective study combined clinical expert knowledge with machine learning to develop and validate a predictive model for incremental hemodialysis decision-making. The aim of the predictive model is to assist clinicians in developing individualized incremental dialysis treatment plans to optimize patient outcomes.', 'detailedDescription': "By collecting patients' clinical and biochemical parameters and combining them with experts' judgments of dialysis timing and frequency, the model can dynamically assess patients' risk of needing to increase the frequency of dialysis, thus assisting physicians in formulating individualized incremental dialysis regimens to optimize dialysis outcomes and improve patients' prognosis."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Hemodialysis patients with residual renal function admitted to dialysis centers', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. New hemodialysis patients (Apr 2010-Jun 2024), started within 3 months, including transfers.\n2. Age ≥18, stable hemodialysis \\>6 months.\n\nExclusion Criteria:\n\n1. Incomplete/unreliable data.\n2. Twice-weekly palliative dialysis.\n3. No baseline urine output or ≤200 mL/24h.\n4. Liver disease, heart failure, or severe comorbidities.'}, 'identificationModule': {'nctId': 'NCT06775067', 'briefTitle': 'Incremental Dialysis Decision Model Based on Expert-Guided Machine Learning', 'organization': {'class': 'OTHER', 'fullName': 'Huashan Hospital'}, 'officialTitle': 'Machine Learning Based on Expert Knowledge to Build and Validate a Decision Model for Incremental Dialysis', 'orgStudyIdInfo': {'id': 'KY2019-585'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Huashan Hospital Hemodialysis Cohort', 'description': 'This is a single-center prospective cohort study that included 175 patients with end-stage renal disease (ESKD) who received maintenance hemodialysis at the hemodialysis center of Huashan Hospital from April 2010 to June 2024. The ESKD patient population was comprised of 175 cases in total. All patients retained some residual kidney function (RKF), and their dialysis records and regular laboratory test results were integrated as input features for the machine learning model. The primary objective of the model was twofold: first, to integrate expert knowledge with machine learning to predict when a switch from lower frequency incremental dialysis (I-HD) to higher frequency dialysis should be made; and second, to identify key variables affecting the risk of adverse outcomes over a two-year period.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '200040', 'city': 'Shanghai', 'state': 'Shanghai Municipality', 'country': 'China', 'facility': 'Huashan hospital, Fudan university', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}], 'overallOfficials': [{'name': 'Jing Chen', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Huashan Hospital'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Huashan Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Chen Jing', 'investigatorAffiliation': 'Huashan Hospital'}}}}