The fate of aortic valve after surgical repair for discrete membranous subaortic stenosis in pediatric patients.

Abstract

This study aimed to evaluate the long-term outcomes of aortic valve function and identify predictors of significant aortic regurgitation and left ventricular outflow tract obstruction recurrence after surgical repair of discrete membranous subaortic stenosis in pediatric patients and to develop a scoring model for long-term management. This single-center, retrospective cohort study analyzed 219 pediatric patients who underwent surgical repair for discrete membranous subaortic stenosis between 2008 and 2022. The primary end point was the composite of significant aortic regurgitation and left ventricular outflow tract obstruction recurrence assessed during a median follow-up of 7.0 years. Multivariable Cox regression analysis was used to develop a predictive model validated using bootstrap resampling and calibration plots. A nomogram and risk-stratification model were constructed based on significant predictors. Significant aortic regurgitation occurred in 10.0% of patients, and left ventricular outflow tract obstruction recurrence was observed in 9.59% of patients. The composite end point was experienced by 16.9%, with freedom rates from the end point at 1, 2, 5, and 10 years of 99.5%, 97.7%, 93.4%, and 74.9%, respectively. The final model included discrete membrane accumulation on the aortic valve, preoperative aortic regurgitation grade 3 or greater, peeling from aortic valve, bypass time more than 75 minutes, and postoperative aortic regurgitation grade 2 or greater. The model predicted the outcome with a C-index of 0.814 on the test set and exhibited a significant ability in stratification of patients into low-risk and high-risk groups (P < .001). This study highlights key risk factors for significant aortic regurgitation and left ventricular outflow tract obstruction recurrence after discrete membranous subaortic stenosis surgery in pediatric patients and provides a robust risk-stratification model for clinical decision-making.