A Nomogram for Predicting In-Hospital Major Adverse Cardio- and Cerebro-Vascular Events in Patients Undergoing Major Noncardiac Surgery: A Large-Scale Nested Case-Control Study

Purpose: Few evidence-based predictive tools are available to evaluate major adverse cardio-and cerebro-vascular events (MACCEs) before major noncardiac surgery. We sought to develop a new simple but effective tool for estimating surgical risk. Patients and Methods: Using a nested case-control study design, we recruited 105 patients who experienced MACCEs and 481 patients without MACCEs during hospitalization from 10,507 patients undergoing major noncardiac surgery in Beijing Chaoyang hospital. Least absolute shrinkage and selection operator (LASSO) regression and likelihood ratio were applied to screen 401 potential features for logistic regression. A nomogram was constructed using the selected variables. Results: Chronic heart failure, valvular heart disease, preoperative serum creatinine >2.0 mg/dL, ASA class, neutrophil count and age were most associated with in-hospital MACCEs among all the factors. A new prediction model established based on these showed a good discriminatory ability (AUC, 0.758 [95% confidence interval (CI), 0.708-0.808] and a well-performed calibration curve (Hosmer-Lemeshow chi(2) = 7.549, p = 0.479), which upheld in the 10-fold cross-validation (AUC, 0.742 [95% CI, 0.718-0.767]. This model also demonstrated an improved performance in comparison to the modified Revised Cardiac Risk Index (RCRI) score (increase in AUC by 0.119 [95% CI, 0.056-0.180]; NRI, 0.445 [95% CI, 0.237-0.653]; IDI, 0.133 [95% CI, 0.087-0.178]. The decision curve analysis showed a positive net benefit of our new model. Conclusion: Our nomogram, which relies upon simple clinical characteristics and laboratory tests, is able to predict MACCEs in patients undergoing major noncardiac surgery. This prediction shows better discrimination than the standardized modified RCRI score, laying a promising foundation for further large-scale validation.