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.