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

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

RECRUITING

Total Enrollment

400 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-07-15

Study Completion Date

2024-12-15

Brief Summary

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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.

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Detailed Description

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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.

Conditions

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Liver Transplantation

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

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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

Minimum Eligible Age

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No