Tremendous advances in cardiovascular medicine have resulted in a large and growing elderly population and a burden of comorbidities in elderly patients, which has led to an increase in the number of elderly patients undergoing elective surgeries, and perioperative cardiovascular accidents have become one of the leading causes of postoperative mortality. [10]. In our study, MACEs were defined as the occurrence of myocardial infarction, cardiac arrest, heart failure, or stroke within 30 days after surgery. The incidence of MACEs reached 5%, which is consistent with the incidence of 6.3% in an international prospective cohort study by P J Devereaux et al. [11]. In this study, we developed a new, easy-to-use nomogram to predict MACEs in elderly patients who underwent major abdominal surgery, and the model had good accuracy and discriminative ability.
We chose LASSO regression analysis to screen the predictor variables for model development. LASSO regularization is a statistical method for addressing overfitting and screening characteristic variables. Unlike the traditional stepwise regression STEPWISE forward and backward variable screening method, LASSO regression can utilize a smaller sample size and efficiently screen a greater number of variables. It has been widely used in many predictive models and machine learning algorithms [12].
Rajagopalan et al. applied cardiac biomarkers such as BNP and troponin to predict MACEs, and the findings showed a high degree of correlation [13–19]. It has been reported that patients undergoing noncardiac surgery may have multiple comorbidities and a greater risk of MACEs, suggesting that preoperative comorbidities such as coronary artery disease are predictive of MACEs. [20] Another study showed that the risk of MACEs increases with age [21]. Therefore, the existence of a simple and easy method for clinical preoperative assessment is of particular importance.
The SPPB is a simple and easy-to-apply preoperative tool for assessing physical function. A prospective multicenter cohort study showed that low SPPB could predict postoperative pulmonary complications in elderly patients undergoing pulmonary resection [22]. Ryohei et al. studied the correlation between SPPB and postoperative delirium, and the results showed that low SPPB was a risk factor for postoperative delirium. [23] There are also findings showing that SPPB can be used as a predictor of mid-term prognosis in mitral valve surgery patients [24].
In our study, we performed LASSO regression based on SPPB to screen eight characteristic variables and developed a new nomogram prediction model. We evaluated the performance of the nomogram using the area under the ROC curve (AUC). The area under the ROC curve (AUC) was 0.852, indicating the model's accuracy. Calibration curves for the probability of major postoperative adverse cardiovascular events showed good agreement between the nomogram-predicted and observed values. A DCA curve was applied to assess the net benefit of Nomotu to patients, and the results showed that the model could provide positive net benefits to patients at a threshold probability range of 0.05–0.80. The results suggest that the model based on the SPPB can be used to predict adverse postoperative cardiovascular events, which will help clinicians screen patients at high risk for MACEs and enable clinicians to intervene effectively in patients preoperatively. Prehabilitation has been commonly used in clinical practice, and the model can be used to perform preoperative assessments through multidisciplinary and multidiagnostic means to provide better clinical services for patients and improve the quality of perioperative care.
There are several limitations of our study: 1. This was a single-center study with a sample of only 426 patients, and in the future, we will perform external validation in a multicenter sample. This study only followed up the data for 30 days, and in the future, we need to carry out a long period of follow-up to study the predictive value of SPPB for long-term postoperative survival. This population is composed of elderly patients, and in the future, we hope to carry out this study in a larger population.
In conclusion, SPPB is an essential predictor of MACEs, and we developed a simple and practical nomogram based on SPPB that has good predictive ability for MACEs. In clinical practice, we can assess patients preoperatively based on nomograms with risk stratification and perform preoperative interventions to reduce MACEs, which will help to enhance perioperative management and improve patient-centered outcomes.