Development of a Novel Risk Score for Predicting One-Year Mortality Risk in Patients with Atrial Fibrillation using XGBoost-Assisted Feature Selection

BackgroundThere is a lack of tools specifically designed to assess mortality risk in patients with atrial fibrillation (AF). The aim of this study was to utilize machine learning methods for identifying pertinent variables and developing an easily applicable prognostic score to predict 1-year mortality in AF patients. MethodsThis single-center retrospective cohort study based on the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database focused on patients aged 18 years and older with AF. The study thoroughly scrutinized patient data to identify and analyze variables, encompassing demographic variables, comorbidities, scores, vital signs, laboratory test results, and medication usage. The variable importance from XGBoost guided the development of a logistic model, forming the basis for an AF scoring model. Decision curve analysis was used to compare the AF score with other scores. Python and R software were used for data analysis. ResultsA cohort of 59,595 AF patients was obtained from the MIMIC-IV database; these patients were predominantly elderly (median age 77.3 years) and male (55.6%). The XGBoost model effectively predicted 1-year mortality (Area under the curve (AUC): 0.833; 95% confidence intervals: 0.826-0.839), underscoring the significance of the Charlson Comorbidity Index (CCI) and the presence of metastatic solid tumors. The CRAMB score (Charlson comorbidity index, readmission, age, metastatic solid tumor, and blood urea nitrogen maximum) outperformed the CCI and CHA2DS2-VASc scores, demonstrating superior predictive value for 1-year mortality. In the test set, the area under the ROC curve (AUC) for the CRAMB score was 0.756 (95% confidence intervals: 0.748-0.764), surpassing the CCI score of 0.720 (95% confidence intervals: 0.712-0.728) and the CHA2DS2-VASc score of 0.609 (95% confidence intervals: 0.600-0.618). Decision curve analysis revealed that the CRAMB score had a consistently positive effect and greater net benefit across the entire threshold range than did the default strategies and other scoring systems. The calibration plot for the test set indicated that the CRAMB score was well calibrated. ConclusionsThis studys primary contribution is the establishment of a benchmark for utilizing machine learning models in construction of a score for mortality prediction in AF. The CRAMB score was developed by leveraging a large-sample population dataset and employing XGBoost models for predictor screening. The simplicity of the CRAMB score makes it user friendly, allowing for coverage of a broader and more heterogeneous AF population.