Background: Intraoperative electrocardiographic monitoring is considered a standard of care. However, there are no evidence-based algorithms for using intraoperative ST segment data to identify patients at high risk for adverse perioperative cardiac events. Therefore, we performed an exploratory study of statistical measures summarizing intraoperative ST segment values determine whether the variability of these measurements was associated with adverse postoperative events. We hypothesized that elevation, depression, and variability of ST segments captured in an anesthesia information management system are associated with postoperative serum troponin elevation.
Methods: We conducted a single-institution, retrospective study of intraoperative automated ST segment measurements from leads I, II, and III, which were recorded in the electronic anesthesia record of adult patients undergoing noncardiac surgery. The maximum, minimum, mean, and SD of ST segment values were entered into logistic regression models to find independent associations with myocardial injury, defined as an elevated serum troponin concentration during the 7 days after surgery. Performance of these models was assessed by measuring the area under the receiver operator characteristic curve. The net reclassification improvement was calculated to quantify the amount of information that the ST segment values analysis added regarding the ability to predict postoperative troponin elevation.
Results: Of 81,011 subjects, 4504 (5.6%) had postoperative myocardial injury. After adjusting for patient characteristics, the ST segment maximal depression (e.g., lead I: odds ratio [OR], 1.66; 95% confidence interval [CI], 1.26-2.19; P = 0.0004), maximal elevation (e.g., lead I: OR, 1.70; 95% CI, 1.34-2.17; P < 0.0001), and SD (e.g., lead I: OR, 0.16; 95% CI, 0.06-0.42; P = 0.0002) were found to have statistically significant associations with myocardial injury. Increased SD was associated with decreased risk when accounting for the maximal amount of ST segment depression and elevation and for patient characteristics. The ST segment summary statistics model had fair discrimination, with an area under the receiver operator characteristic curve of 0.71 (95% CI, 0.68-0.73). Addition of ST segment data produced a net reclassification improvement of 0.0345 (95% CI, 0.00016-0.0591; P = 0.0474).
Conclusions: Analysis of automated ST segment values obtained during anesthesia may be useful for improving the prediction of postoperative troponin elevation.