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

NCT ID: NCT05779098

Last Updated: 2025-05-22

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

1071 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-04-01

Study Completion Date

2025-04-01

Brief Summary

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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.

Detailed Description

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Conditions

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Liver Failure After Operative Procedure

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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Extensive hepatectomy

No interventions assigned to this group

Eligibility Criteria

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Inclusion Criteria

Extensive hepatectomy in our hospital(≥ three Hepatic segment)

Exclusion Criteria

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

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Shanghai 10th People's Hospital

OTHER

Sponsor Role collaborator

Jinling Hospital, China

OTHER

Sponsor Role collaborator

Shen Feng

OTHER

Sponsor Role lead

Responsible Party

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Shen Feng

Dean of Clinical Research Institute of Eastern Hepatobiliary Surgery Hospital

Responsibility Role SPONSOR_INVESTIGATOR

Locations

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

Shanghai, , China

Site Status

Countries

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China

Other Identifiers

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1312871874

Identifier Type: -

Identifier Source: org_study_id

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