Development and validation of a nomogram for predicting in-hospital mortality in older adult hip fracture patients with atrial fibrillation: a retrospective study - Li, Zhenli; He, Jing; Yao, Tiezhu; Liu, Guang; Liu, Jing; Guo, Ling; Li, Mengjia; Guan, Zhengkun; Gao, Ruolian; Ma, Jingtao (Frontiers in Medicine, 2025)
BackgroundHip fracture is prevalent among older adult patients, which often results in intensive care unit (ICU) admission. When complicated with atrial fibrillation (AF), older adult patients with hip fractures were observed to have a high short-term mortality. However, few studies have focused specifically on such a cohort. This study aimed to develop and validate a nomogram to evaluate the in-hospital mortality risk of such a group in the ICU.MethodsWe enrolled older adult patients with hip fractures complicated by AF in the Medical Information Mart for Intensive Care Database (MIMIC). Logistic regression (LR) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms were employed to screen features. We further used Extreme Gradient Boosting (XGBoost) based on features selected by LR and LASSO algorithms to assist in identifying the final model-established features. An Electronic Intensive Care Unit Collaborative Research Database (eICU-CRD) was utilized for external validation. The area under curves (AUC), calibration curves, Delong test, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were performed to evaluate the prediction performance. Ultimately, a visualized nomogram was constructed to provide convenient access for clinicians to evaluate mortality risk.ResultsA total of 308 patients were enrolled in this study. We employed LR and LASSO algorithms to initially screen out 15 and 20 features, respectively. Next, 10 features, which were the intersection of features selected by the above methods, were further utilized to develop an XGBoost model to obtain the rank of feature importance. Finally, eight features were ultimately selected to develop a nomogram by comparing the AUCs of LR models originating from a “feature-adding by the feature rank” strategy. The nomogram exhibited superior predictive performance (AUC:0.834) than conventional scoring systems in the training set, with an AUC of 0.715 in external validation.ConclusionOur study constructed a predictive model based on features selected by machine learning approaches to evaluate the in-hospital mortality risk of critically ill patients with hip fractures combined with AF. An accessible nomogram was offered to facilitate clinical decision-making.