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

Author:

Kurexi Adilai1,Yan Rui1,Yuan Tingting1,Taati Zhaenhaer1,Mijiti Maimaiti1,Li Dan1

Affiliation:

1. The 3rd Affiliated Teaching Hospital of Xinjiang Medical University[Affiliated cancer Hospital ]

Abstract

Abstract

Objective To investigate the predictive value of a short physical performance test (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 clinical efficacy of the model was assessed by the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA). Results The incidence of postoperative MACE 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 MACE and can guide preoperative interventions and help to improve perioperative management.The area under the curve (AUC) was 0.852 (95% CI, 0.749–0.954), the calibration curve showed good agreement, and the decision curve showed promising clinical efficacy. 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.

Publisher

Research Square Platform LLC

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