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

Shen Feng

Status: Completed

Conditions: Liver Failure After Operative Procedure

Treatments: Procedure: Extensive hepatectomy

Study type: Observational

Funder types: Other

Identifiers

NCT05779098

1312871874

Details and patient eligibility

About

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.

Enrollment: 1,071 patients

Sex: All

Ages: 18 to 80 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

Extensive hepatectomy in our hospital（≥ three Hepatic segment）

Exclusion criteria

Serious basic diseases Intolerable surgery Refuse to perform ICG test before operation

Trial design

1,071 participants in 1 patient group

Extensive hepatectomy