Intraoperative Variables Enhance the Predictive Performance of Myocardial Injury in Patients with High Cardiovascular Risk After Thoracic Surgery When Added to Baseline Prediction Model

Abstract

Purpose Myocardial injury after non-cardiac surgery is closely related to major adverse cardiac and cerebrovascular event and is difficult to identify. This study aims to investigate how to predict the myocardial injury of thoracic surgery and whether intraoperative variables contribute to the prediction of myocardial injury. Methods The prospective study included adult patients with high cardiovascular risk who underwent elective thoracic surgery from May 2022 to October 2022. Multivariate logistic regression was used to establish a model with baseline variables and a model with baseline and intraoperative variables. We compare the predictive performance of two models for postoperative myocardial injury. Results In general, 31.5% (94 of 298) occurred myocardial injury. Age ≥65 years old, obesity, smoking, preoperative hsTnT, and one-lung ventilation time were independent predictors of myocardial injury. Compared with baseline model, the intraoperative variables improved model fit, modestly improved the reclassification (continuous net reclassification improvement 0.409, 95% CI, 0.169 to 0.648, P <0.001, improved integrated discrimination 0.036, 95% CI, 0.011 to 0.062, P <0.01) of myocardial injury cases, and achieved higher net benefit in decision curve analysis. Conclusion The risk stratification and anesthesia management of high-risk patients are essential. The addition of intraoperative variables to the baseline predictive model improved the performance of the overall model of myocardial injury and helped anesthesiologists screen out the patients at the greatest risk for myocardial injury and adjust anesthesia strategies.