Department of Hepatobiliary and Pancreatic Surgery, Tenth People's Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, China
Shanghai, 200092, China
Completed
NCT Number: NCT05779098
A Machine Learning Architecture to Predict Post-Hepatectomy Liver Failure Using Liver Regeneration Biomarkers and Time-Phased Data
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.
Completed
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Notify MeKey information
Conditions
Age range
18 year–80 year
Sex eligibility
All sexes
Study type
Observational
Primary location
Who can participate
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Extensive hepatectomy in our hospital（≥ three Hepatic segment）
Exclusion criteria
Serious basic diseases Intolerable surgery Refuse to perform ICG test before operation
Treatment and study plan
Primary outcomes
-
Postoperative liver failure
Time frame: 1-5 days after surgery
Sponsors and collaborators
Lead sponsor
Shen Feng
Other
Collaborators
- Jinling Hospital, China
- Shanghai 10th People's Hospital
Registry information
Acronym: PHLF predictio
Important dates
- Study start
- 2023
- Primary completion
- 2025
- Study completion
- 2025
- First posted
- Mar 22, 2023
- Registry last updated
- May 22, 2025
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.