Development of a risk score for predicting one-year mortality in patients with atrial fibrillation using XGBoost-assisted feature selection.

BACKGROUND

There are no tools specifically designed to assess mortality risk in patients with atrial fibrillation (AF).

AIMS

This study aimed to utilize machine learning methods to identify pertinent variables and develop an easily applicable prognostic score to predict 1-year mortality in AF patients.

METHODS

This study, based on the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database, focused on patients aged 18 years and older with AF. A critical care database from China was the external validation set. The importance of variables from XGBoost guided the development of a logistic model, forming the basis for an AF scoring model.

RESULTS

Records of of 26 365 AF patients were obtained from the MIMIC-IV database. The external validation dataset included 231 AF patients. The CRAMB score (Charlson comorbidity index, readmission, age, metastatic solid tumor, and maximum blood urea nitrogen concentration) outperformed the CCI and CHA2DS2-VASc scores, demonstrating superior predictive value for 1-year mortality. In the test set, the area under the receiver operating characteristic (AUC) for the CRAMB score was 0.765 (95% confidence interval [CI], 0.753-0.776), while in the external validation set, it was 0.582 (95% CI, 0.502-0.657).

CONCLUSIONS

The simplicity of the CRAMB score makes it user-friendly, allowing for coverage of a broader and more heterogeneous AF population.