A Machine Learning Architecture to Predict Post-Hepatectomy Liver Failure Using Liver Regeneration Biomarkers and Time-Phased Data
PHLF predictio
1 other identifier
observational
1,071
1 country
1
Brief 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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Apr 2023
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
March 9, 2023
CompletedFirst Posted
Study publicly available on registry
March 22, 2023
CompletedStudy Start
First participant enrolled
April 1, 2023
CompletedPrimary Completion
Last participant's last visit for primary outcome
April 1, 2025
CompletedStudy Completion
Last participant's last visit for all outcomes
April 1, 2025
CompletedMay 22, 2025
May 1, 2025
2 years
March 9, 2023
May 18, 2025
Conditions
Outcome Measures
Primary Outcomes (1)
Postoperative liver failure
1-5 days after surgery
Study Arms (1)
Extensive hepatectomy
Eligibility Criteria
Patients with Extensive Hepatology in our hospital
You may qualify if:
- Extensive hepatectomy in our hospital（≥ three Hepatic segment）
You may not qualify if:
- Serious basic diseases Intolerable surgery Refuse to perform ICG test before operation
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Shen Fenglead
- Shanghai 10th People's Hospitalcollaborator
- Jinling Hospital, Chinacollaborator
Study Sites (1)
Department of Hepatobiliary and Pancreatic Surgery, Tenth People's Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, China
Shanghai, 200092, China
Related Publications (1)
Shen H, Yuan T, Si A, Shen Y, Liu J, Jin L, Xie Z, Zhang H, Wei W, Dai Y, Jiang T, He C, Zhang S, Hu Y, Huang S, Yang Z, Chen Y, Zhang X, Shen F, Qi X, Li J. Liver regeneration-associated machine learning architecture integrating time-phased predictions for post-hepatectomy liver failure. EClinicalMedicine. 2025 Nov 28;90:103661. doi: 10.1016/j.eclinm.2025.103661. eCollection 2025 Dec.
PMID: 41399475DERIVED
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR INVESTIGATOR
- PI Title
- Dean of Clinical Research Institute of Eastern Hepatobiliary Surgery Hospital
Study Record Dates
First Submitted
March 9, 2023
First Posted
March 22, 2023
Study Start
April 1, 2023
Primary Completion
April 1, 2025
Study Completion
April 1, 2025
Last Updated
May 22, 2025
Record last verified: 2025-05
Data Sharing
- IPD Sharing
- Will not share