Clinical trial NCT06534840: Evaluation of Pulmonary Complications in Liver Transplantation Patients Based on Machine Learning
Official title: Establishment and Evaluation of Moderate-severe Prediction Model of Pulmonary Complications in Liver Transplantation Patients Based on Machine Learning Algorithm
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Liver Transplantation
Lead sponsor: West China Hospital
Sponsor class: OTHER
First posted: 2024-08-02
Enrollment: 400
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients who underwent liver transplantation from January 1,2016to December 31，2019 were admitted to the study.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Prediction of postoperative moderate-to-severe pulmonary complications
timeFrame: August 2024-December 2024
Secondary outcomes:
Other outcomes:
Locations:
city: Chengdu
country: China
facility: West China Hospital, Sichuan University
state: Sichuan
Study officials:
References: