Development and validation of a prediction model for early major adverse cardiovascular events in children undergoing surgical repair of supravalvular aortic stenosis

Surgical therapy of supravalvular aortic stenosis (SVAS) is associated with low overall early mortality but high incidence of postoperative adverse cardiac events. The aims of this study were to develop and validate a predictive model for major adverse cardiovascular events (MACE) in patients undergoing surgical repair of SVAS. This study included 262 patients who underwent surgical repair of SVAS between 2002 and 2019 in Beijing and Yunnan, China. MACE occurred during postoperative hospitalization or within 30 days after SVAS repair. Multivariate logistic regression was used to select prognostic factors for MACE and construct a nomogram. The receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to assess the predictive performance of the nomogram. Age, sex, body surface area, cardiopulmonary bypass time, and aortic valve z score were identified as prognostic factors. These five prognostic factors were used to construct the prediction nomogram. The area under the curve of the model was 0.859 (95% CI: 0.765–0.953). For the bootstrap sampling validation, the corrected C-index was 0.823, and the DCA showed the nomogram was clinically beneficial across a range of thresholds of 2–66%. A novel nomogram was developed to predict major adverse cardiovascular events in patients undergoing surgical repair of SVAS, which may serve as an effective tool to assist clinicians in individualized prognostic assessment. This study was retrospectively registered at www.chictr.org.cn on May 16, 2021 (ChiCTR2100046494).