Long-term outcomes of congenital subaortic stenosis surgery: systematic review and meta-analysis

To estimate recurrence, reoperation, and aortic insufficiency progression after pediatric surgery for subaortic stenosis and explore associated study-level factors.

PubMed, Embase, Web of Science, and the Cochrane Library were searched for eligible studies published from 1 January 2012 to 3 July 2025. We included observational studies of patients <18 years undergoing surgical resection for SAS with a study-level mean or median postoperative follow-up of ≥ 3 years, which was the prespecified operational definition of long-term follow-up. Proportions were pooled using random-effects models; heterogeneity was quantified using I²; meta-regression assessed study-level associations with reoperation and AI progression; and risk of bias was evaluated using the Newcastle-Ottawa Scale.

Seventeen retrospective studies included 1,536 patients; 1,493 contributed to the recurrence analysis. Follow-up ranged from 3.0 to 12.7 years. Overall recurrence was 21% (95% confidence interval, 16-28%; I²=88.1%). Reoperation across 16 studies was 17% (95% confidence interval, 14-20%; I²=41.6%); a prespecified definition-harmonized sensitivity analysis yielded the same estimate (17%; 95% confidence interval, 15-20%; I²=19.1%). In isolated discrete subaortic stenosis, reoperation was 8% and 12% after definition harmonization. Progression to moderate or severe aortic insufficiency was 10% (95% confidence interval, 8-13%). Older study-level age at surgery was inversely associated with reoperation (β = -0.2230; P = 0.0138), whereas higher preoperative peak left ventricular outflow tract gradient was associated with aortic insufficiency progression (odds ratio per 10-mmHg increase, 1.25; P = 0.0072).

Reoperation and clinically important aortic insufficiency remain substantial during follow-up after pediatric subaortic stenosis surgery. Meta-regression findings are exploratory, aggregate associations and should not guide individual operative timing or risk prediction.