Development and Internal Validation of a Nomogram for Predicting Postoperative Cardiac Events in Elderly Hip Fracture Patients

Purpose: Postoperative cardiac events (PCEs) are among the main adverse events after hip fracture surgery in the elderly. Existing cardiac risk assessment tools have some limitations and are not specifically designed for elderly patients undergoing hip fracture surgery. This study aimed to develop and internally validate a nomogram for prediction of PCEs in these patients. Patients and Methods: We performed a retrospective study of 992 patients aged >= 65 years undergoing hip fracture surgery in our hospital from July 2015 to December 2021. Patients' demographics and clinical data were collected. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select predictors, and multivariate logistic regression was employed to construct a nomogram. Internal validation was performed by bootstrapping. The discriminatory ability of the model was determined by the area under the receiver operating characteristic curve (AUC). The calibration and clinical utility of the model were assessed. The predictive power and clinical benefit of the nomogram were compared with the Revised Cardiac Risk Index (RCRI).Results: The nomogram was constructed including seven variables: general anesthesia, the American Society of Anesthesiologists (ASA) classification, history of heart failure, history of severe arrhythmia, history of coronary artery disease, preoperative platelet count, and serum creatinine. The nomogram had an excellent predictive ability (AUC = 0.875, 95% confidence interval [CI]: 0.828- 0.918). Satisfactory calibration was shown by calibration plots and the Hosmer-Lemeshow goodness-of-fit test (P = 0.520). Clinical usefulness was confirmed by decision curve analysis and clinical impact curve. The predictive power and clinical utility of the nomogram were superior to RCRI.Conclusion: We developed an easy-to-use nomogram for prediction of PCEs in elderly hip fracture patients. This prediction model could effectively identify patients at high risk of PCEs and may be useful for perioperative management optimization.