Machine learning prediction of postoperative major adverse cardiovascular events in geriatric patients: a prospective cohort study.

ABSTRACT:

Background

Postoperative major adverse cardiovascular events (MACEs) account for more than one-third of perioperative deaths. Geriatric patients are more vulnerable to postoperative MACEs than younger patients. Identifying high-risk patients in advance can help with clinical decision making and improve prognosis. This study aimed to develop a machine learning model for the preoperative prediction of postoperative MACEs in geriatric patients.

Methods

We collected patients' clinical data and laboratory tests prospectively. All patients over 65 years who underwent surgeries in West China Hospital of Sichuan University from June 25, 2019 to June 29, 2020 were included. Models based on extreme gradient boosting (XGB), gradient boosting machine, random forest, support vector machi

SUBMITTER: Peng X 

PROVIDER: S-EPMC9463850 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature