Liver Transplantation
Conditions
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.
Interventions
None listed
Sponsors
West China Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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 |
Countries
China
Contacts
Primary ContactChun ling Jiang, PhD
Outcome results
None listed