Development and validation of a prediction model for in-hospital death in patients with heart failure and atrial fibrillation

Abstract

Background To develop a prediction model for in-hospital mortality of patients with heart failure (HF) and atrial fibrillation (AF). Methods This cohort study extracted the data of 10,236 patients with HF and AF upon intensive care unit (ICU) from the Medical Information Mart for Intensive Care (MIMIC). The subjects from MIMIC-IV were divided into the training set to construct the prediction model, and the testing set to verify the performance of the model. The samples from MIMIC-III database and eICU-CRD were included as the internal and external validation set to further validate the predictive value of the model, respectively. Univariate and multivariable Logistic regression analyses were used to explore predictors for in-hospital death in patients with HF and AF. The receiver operator characteristic (ROC), calibration curves and the decision curve analysis (DCA) curves were plotted to evaluate the predictive values of the model. Results The mean survival time of participants from MIMIC-III was 11.29 ± 10.05 days and the mean survival time of participants from MIMIC-IV was 10.56 ± 9.19 days. Simplified acute physiology score (SAPSII), red blood cell distribution width (RDW), beta-blocker, race, respiratory rate, urine output, coronary artery bypass grafting (CABG), Charlson comorbidity index, renal replacement therapies (RRT), antiarrhythmic, age, and anticoagulation were predictors finally included in the prediction model. The AUC of our prediction model was 0.810 (95%CI: 0.791–0.828) in the training set, 0.757 (95%CI: 0.729–0.786) in the testing set, 0.792 (95%CI: 0.774–0.810) in the internal validation set, and 0.724 (95%CI: 0.687–0.762) in the external validation set. The calibration curves of revealed that the predictive probabilities of our model for the in-hospital death in patients with HF and AF deviated slightly from the ideal model. The DCA curves revealed that the use of our prediction model increased the net benefit than use no model. Conclusion The prediction model had good discriminative ability, and might provide a tool to timely identify patients with HF complicated with AF who were at high risk of in-hospital mortality.

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

“…There is considerable evidence affirming the significance of BUN as a mortality predictor in patients with CVD [31–33]. Moreover, research indicates that BUN levels at admission serve as a predictive marker for mortality in patients with heart failure [32].…”

“…Yan et al focused on the development and validation of a prediction model for in-hospital death in patients with heart failure and AF [33]. Although that study did not directly align with the task of developing a score for risk of death in AF, it is relevant because it addresses the intersection of heart failure and AF, which are often comorbid conditions.…”

“…The prediction model developed by Yan et al may offer insights into prognostic factors and risk assessment strategies that may be applicable to the development of AF risk scores. The present study [33] developed a scoring model using 5998 patients admitted to the ICU from the MIMIC-IV database and used logistic regression to screen approximately 38 prespecified variables, ultimately including 16 variables to construct the score. However, the complexity of the scoring system prevented an evaluation of the importance of variables in variable screening based on traditional logistic regression.…”