Evaluation of Pulmonary Complications in Liver Transplantation Patients Based on Machine Learning

Recruiting

• Conditions: Liver Transplantation

• Registration Number: NCT06534840
• Lead Sponsor: West China Hospital

Brief Summary

The main objective of this study is to develop a machine learning model that predicts moderate-severe prediction model of pulmonary complications in liver transplantation patients within 14 postoperative day using a real-world, local preoperative and intraoperative electronic health records, not administrative codes.

Detailed Description

Postoperative pulmonary complications can increase the length of hospital stay and medical costs. In particular, moderate to severe pulmonary complications, which often require clinical intervention, once occur, will lead to significantly prolonged postoperative hospitalization or even cause permanent damage or death in severe cases. A number of risk-stratified cation models have been developed to identify patients at increased risk of postoperative pulmonary complications. However, these models were built by using the traditional regression analysis. However, the traditional prediction methods have the disadvantages of limited processing power of nonlinear models and outlier, and relatively single selection variables. The obtained models have poor accuracy, and the quantification degree is not enough, so it is difficult to popularize clinical application. Artificial machine learning can use it by analyzing a large number of specific features in the rich data set to identify and learn to accurately predict the diagnosis and prognosis of diseases, and surpass traditional prediction models in dealing with classification problems. The algorithms are flexible, and it is more and more widely used in clinical practice research. However, there are few reports on machine learning models predicting prognostic models related to postoperative pulmonary complications in liver transplantation patients. Therefore, we aimed to build predictive models using artificial machine learning methods to screen for their risk factors in order to provide early intervention and individualized treatment for high-risk patients.

Study Timeline

• Status Verified: 2024-07
• Study Start: 2024-07-15
• Study First Submitted: 2024-07-30
• Study First Submitted QC: 2024-07-30
• Last Update Submitted: 2024-07-30
• Study First Posted: 2024-08-02
• Last Update Posted: 2024-08-02
• Primary Completion: 2024-12-15
• Study Completion: 2024-12-15

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 400

Inclusion Criteria

• Adult patients (age ≥ 18 years)
• Undergoing liver transplantation

Exclusion Criteria

• Re-transplantation
• Multi-organ transplants
• Intra-operative deaths
• severe encephalopathy (West Haven criteria III or IV)
• Incomplete clinical data

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Prediction of postoperative moderate-to-severe pulmonary complications	August 2024-December 2024	IA total of 72 variables are expected to be included, using 6 types of machine learning methods, including decision tree (DT), logistic regression (LR), random forest (, RF), support vector machine (SVM), extreme gradient lift (XGBoost), and gradient lift decision tree (GBDT) to build a moderate postoperative prediction model

Trial Locations

Locations (1)

West China Hospital, Sichuan University; 🇨🇳 Chengdu, Sichuan, China