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老年患者接受腹部大手术术后主要不良心血管事件预测模型的开发。

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

作者信息

Kurexi Adilai, Yan Rui, Yuan Tingting, Taati Zhaenhaer, Mijiti Maimaiti, Li Dan

机构信息

The 3rd Affiliated Teaching Hospital of Xinjiang Medical University (Affiliated Cancer Hospital), Urumqi, China.

出版信息

BMC Surg. 2024 Dec 21;24(1):403. doi: 10.1186/s12893-024-02711-w.

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.

摘要

目的

探讨简易体能状况量表(SPPB)对老年腹部大手术患者术后主要不良心血管事件(MACE)的预测价值,并建立列线图风险预测模型。

方法

选取2023年6月至2024年3月在我院接受腹部大手术的427例年龄≥65岁的老年患者进行研究,最终纳入416例患者。测量术前SPPB评分,将患者分为两组:高SPPB组(≥10分)和低SPPB组(<10分)。记录受试者的临床数据集和术后主要不良心血管事件(MACE)发生数据。进行LASSO回归分析以筛选预测变量,并建立预测MACE的列线图风险预测模型。采用受试者操作特征(ROC)曲线下面积(AUC)、校准曲线和决策曲线分析(DCA)评估模型的临床疗效。

结果

老年腹部大手术患者术后MACE发生率为5%。LASSO回归分析显示,心律失常、肌酸激酶、SPPB、麻醉持续时间、年龄、术中最低心率、BMI和冠状动脉疾病是MACE的显著预测因素。基于SPPB和临床指标的列线图风险预测模型能更好地预测MACE的发生,可指导术前干预并有助于改善围手术期管理。决策曲线显示出令人鼓舞的临床有效性,校准曲线显示出良好的一致性,曲线下面积(AUC)为0.852(95%CI,0.749-至0.954)。

结论

基于SPPB和临床指标的列线图风险预测模型能更好地预测MACE的发生,可指导术前干预并有助于改善围手术期管理。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6b/11662575/7fcdcbefb203/12893_2024_2711_Fig1_HTML.jpg

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