Incremental Dialysis Decision Model Based on Expert-Guided Machine Learning
NCT ID: NCT06775067
Last Updated: 2025-01-14
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
175 participants
OBSERVATIONAL
2010-04-12
2024-06-28
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Huashan Hospital Hemodialysis Cohort
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.
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
2. Age ≥18, stable hemodialysis \>6 months.
Exclusion Criteria
2. Twice-weekly palliative dialysis.
3. No baseline urine output or ≤200 mL/24h.
4. Liver disease, heart failure, or severe comorbidities.
18 Years
ALL
No
Sponsors
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Huashan Hospital
OTHER
Responsible Party
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Chen Jing
Professor
Principal Investigators
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Jing Chen
Role: PRINCIPAL_INVESTIGATOR
Huashan Hospital
Locations
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Huashan hospital, Fudan university
Shanghai, Shanghai Municipality, China
Countries
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Other Identifiers
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KY2019-585
Identifier Type: -
Identifier Source: org_study_id
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