At a glance
ClinicalIndex Comparison RecordN/ACompleted· 1,071 enrolled
Drug / intervention
Not specified
Likely dose
Not stated in record
Structured eligibility isn't available for this trial yet — see the full criteria in the Eligibility tab below.
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A Machine Learning Architecture to Predict Post-Hepatectomy Liver Failure Using Liver Regeneration Biomarkers and Time-Phased Data
In Brief
An observational study for Liver Failure After Operative Procedure. Completed, enrolled 1,071 participants across 1 site.
Detailed Summary
Post-hepatectomy liver failure (PHLF) is the leading cause of morbidity and mortality following major hepatectomy. Existing prediction models fail to capture the dynamic liver regeneration and perioperative changes, limiting their predictive accuracy. We aimed to develop a machine learning (ML) modelling system (PILOT architecture) integrating liver regeneration biomarkers with time-phased perioperative clinical data to accurately predict PHLF risk.
Study Details
Study Typeobservational
Allocation--
Masking--
Primary Purpose--
ConditionsLiver Failure After Operative Procedure
CountriesChina
CollaboratorsShanghai 10th People's Hospital, Jinling Hospital, China
Timeline
N/ACompletedFinished
202420252026
First PostedMar 2023
Enrollment StartApr 2023
Primary CompletionApr 2025
TodayJul 2026
First PostedMar 22, 2023
Enrollment StartApr 1, 2023
Primary CompletionApr 1, 2025
TodayJul 2, 2026
Enrollment to primary: 2 yearsPosted 3.3 years ago