Development of a machine learning-based model to predict major adverse events after surgery for type A aortic dissection complicated by malnutrition.

ABSTRACT:

Objective

This study aims to develop a predictive model for the risk of major adverse events (MAEs) in type A aortic dissection (AAAD) patients with malnutrition after surgery, utilizing machine learning (ML) algorithms.

Methods

We retrospectively collected clinical data from AAAD patients with malnutrition who underwent surgical treatment at our center. Through least absolute shrinkage and selection operator (LASSO) regression analysis, we screened for preoperative and intraoperative characteristic variables. Based on the random forest (RF) algorithm, we constructed a ML predictive model, and further evaluated and interpreted this model.

Results

Through LASSO regression analysis and univariate analysis, we ultimately selected seven feature variables for modeling. Aft

SUBMITTER: Xie LF 

PROVIDER: S-EPMC11254848 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature