Key result
Novel nomogram improves in-hospital MACCE prediction over modified RCRI with an AUC of ~0.76.
Why the study?
Few evidence-based predictive tools are available to evaluate major adverse cardio- and cerebro-vascular events before major noncardiac surgery.
Does a novel nomogram improve the prediction of in-hospital MACCEs compared to the modified RCRI score in patients undergoing major noncardiac surgery?
Case-Control (n=586)
No
Does a novel nomogram improve the prediction of in-hospital MACCEs compared to the modified RCRI score in patients undergoing major noncardiac surgery?
Effect estimate: Increase in AUC by 0.119 (95% CI 0.056-0.180)
A novel nomogram incorporating simple clinical and laboratory variables provides superior prediction of perioperative MACCEs compared to the traditional RCRI score.
Nomogram may enhance perioperative MACCE prediction over RCRI; hypothesis-generating and requires prospective validation before clinical adoption.
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 χ 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.
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Wu et al. (2022) conducted a case-control in Patients undergoing major noncardiac surgery (n=586). New prediction nomogram (CHF, VHD, Cr > 2.0 mg/dL, ASA class, neutrophil count, age) vs. Modified Revised Cardiac Risk Index (RCRI) score was evaluated on In-hospital MACCEs (all-cause death, acute myocardial infarction, cardiac arrest, heart failure, ventricular fibrillation, complete heart block and ischemic stroke) (Increase in AUC by 0.119, 95% CI 0.056-0.180). A novel nomogram incorporating clinical characteristics and laboratory tests predicted in-hospital MACCEs with an AUC of 0.758, showing better discrimination than the modified RCRI score.
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