A Machine Learning Architecture to Predict Post-Hepatectomy Liver Failure Using Liver Regeneration Biomarkers and Time-Phased Data

Shen Feng1,071 enrolled

Overview

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 Type

OBSERVATIONAL

Enrollment

1,071

Conditions

Liver Failure After Operative Procedure

Eligibility

Sex: ALLMin age: 18 YearsMax age: 80 Years

Medical Language ↔ Plain English

Inclusion Criteria: Extensive hepatectomy in our hospital（≥ three Hepatic segment） Exclusion Criteria: Serious basic diseases Intolerable surgery Refuse to perform ICG test before operation

Locations (1)

Department of Hepatobiliary and Pancreatic Surgery, Tenth People's Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, China

Shanghai, China

Outcomes

Primary Outcomes

Postoperative liver failure

Time frame: 1-5 days after surgery

Data from ClinicalTrials.gov

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