Machine Learning-Based Risk Model for Predicting Early Mortality After Surgery for Infective Endocarditis.

ABSTRACT: Background The early mortality after surgery for infective endocarditis is high. Although risk models help identify patients at high risk, most current scoring systems are inaccurate or inconvenient. The objective of this study was to construct an accurate and easy-to-use prediction model to identify patients at high risk of early mortality after surgery for infective endocarditis. Methods and Results A total of 476 consecutive patients with infective endocarditis who underwent surgery at 2 centers were included. The development cohort consisted of 276 patients. Eight variables were selected from 89 potential predictors as input of the XGBoost model to train the prediction model, including platelet count, serum albumin, current heart failure, urine occult blood ≥(++), diastolic dysfunction

SUBMITTER: Luo L 

PROVIDER: S-EPMC9238722 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature