A LASSO-derived risk model for long-term mortality in Chinese patients with acute coronary syndrome.

ABSTRACT:

Background

The formal risk assessment is essential in the management of acute coronary syndrome (ACS). In this study, we develop a risk model for the prediction of 3-year mortality for Chinese ACS patients with machine learning algorithms.

Methods

A total of 2174 consecutive patients who underwent angiography with ACS were enrolled. The missing data among baseline characteristics were imputed using the MissForest algorithm based on random forest method. In model development, a least absolute shrinkage and selection operator (LASSO) derived Cox regression with internal tenfold cross-validation was used to identify the predictors for 3-year mortality. The clinical performance was assessed with decision curve analysis.

Results

The average follow-up period was 27.82 ± 13.

SUBMITTER: Li YM 

PROVIDER: S-EPMC7137217 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature