Development of a predictive model for postoperative major adverse cardiovascular events in elderly patients undergoing major abdominal surgery

Abstract

Objective

To investigate the predictive value of a Short Physical Performance Battery (SPPB) for postoperative major adverse cardiovascular events(MACEs) in elderly patients undergoing major abdominal surgery and to develop a nomogram risk prediction model.

Methods

A total of 427 elderly patients aged ≥ 65 years who underwent major abdominal surgery at our hospital between June 2023 and March 2024 were selected for the study, and 416 patients were ultimately included. The preoperative SPPB score was measured, and the patients were divided into two groups: a high SPPB group (≥ 10) and a low SPPB group (< 10). The subjects’ clinical datasets and postoperative major adverse cardiovascular event (MACEs) occurrence data were recorded. LASSO regression analysis was performed to screen predictor variables and develop a nomogram risk prediction model for predicting MACEs. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the model’s clinical efficacy.

Results

The incidence of postoperative MACEs in elderly patients who underwent major abdominal surgery was 5%. LASSO regression analysis revealed that arrhythmia, creatine kinase, SPPB, anesthesia duration, age, intraoperative minimum heart rate, BMI, and coronary artery disease were significant predictors of MACEs. The nomogram risk prediction model based on SPPB and clinical indicators can better predict the occurrence of MACEs and can guide preoperative interventions and help to improve perioperative management.The decision curve indicated encouraging clinical effectiveness, the calibration curve demonstrated good agreement, and the area under the curve (AUC) was 0.852 (95% CI, 0.749–0.954).

Conclusion

The nomogram risk prediction model based on SPPB and clinical indicators can better predict the occurrence of MACEs and can guide preoperative intervention and help to improve perioperative management.