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

NCT05779098 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1071

Last updated 2025-05-22

No results posted yet for this study

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.

Conditions

  • Liver Failure After Operative Procedure

Sponsors & Collaborators

  • Shanghai 10th People's Hospital

    collaborator OTHER

  • Jinling Hospital, China

    collaborator OTHER

  • Shen Feng

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-04-01
Primary Completion
2025-04-01
Completion
2025-04-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05779098 on ClinicalTrials.gov