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A Retrospective Cohort Study on Predicting Delayed Graft Function in Liver Transplant Patients with Hepatocellular Carcinoma: a Nomogram and Machine Learning Approaches.

NCT06626724 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 131

Last updated 2024-10-04

No results posted yet for this study

Summary

Background: This study aimed to develop a predictive model for delayed graft function (DGF) in liver transplant patients with hepatocellular carcinoma (HCC) based on preoperative biochemical indicators, using both logistic regression and XGBoost machine learning algorithms.

Methods: A retrospective cohort study was conducted, including 131 liver transplant patients from January 2020 to April 2022. Preoperative biochemical markers and hematological parameters were analyzed. Logistic regression and XGBoost models were constructed to predict DGF, and their performance was evaluated using the area under the ROC curve (AUC). Shapley Additive Explanations (SHAP) analysis was employed to interpret the feature contributions.

Conditions

  • Hepatocellular Carcinoma (HCC)
  • DGF

Interventions

PROCEDURE

Liver transplantation

Liver transplantation for patients with hepatocellular carcinoma.

Sponsors & Collaborators

  • Jian You

    lead OTHER

Eligibility

Min Age
18 Days
Max Age
75 Days
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-01-01
Primary Completion
2024-04-30
Completion
2024-04-30

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 NCT06626724 on ClinicalTrials.gov