Clinical and 2D/3D-Echo Cardiography Determinants of Mitral Valve Reoperation in Children With Congenital Mitral Valve Disease.

BACKGROUND

Congenital mitral valve disease (CMVD) presents major challenges in its medical and surgical management.

OBJECTIVES

The purpose of this study was to investigate the value of 3-dimensional echocardiography (3DE) and identify associations with MV reoperation in this setting.

METHODS

All children <18 years of age who underwent MV reconstruction for CMVD in 2002 to 2018 were included. Preoperative and postoperative 2-dimensional echocardiography (2DE) and 3DE data were collected. Competing risks and Cox regression analysis were used to identify independent associations with MV reoperation. Receiver operating characteristic and decision-tree analysis were implemented for comparison of 3DE vs 2DE.

RESULTS

A total of 206 children underwent MV reconstruction for CMVD (mitral stenosis, n = 105, mitral regurgitation [MR], n = 75; mixed disease, n = 26); 64 (31%) required MV reoperation. Variables independently associated with MV reoperation were age <1 year (HR: 2.65; 95% CI: 1.13-6.21), tethered leaflets (HR: 2.00; 95% CI: 1.05-3.82), ≥ moderate 2DE postoperative MR (HR: 4.26; 95% CI: 2.45-7.40), changes in 3D-effective orifice area (3D-EOA) and in 3D-vena contracta regurgitant area (3D-VCRA). Changes in 3D-EOA and 3D-VCRA were more strongly associated with MV reoperation than changes in mean gradients (area under the curve [AUC]: 0.847 vs AUC: 0.676, P = 0.006) and 2D-VCRA (AUC: 0.969 vs AUC: 0.720, P = 0.012), respectively. Decision-tree analysis found that a <30% increase in 3D-EOA had 80% accuracy (HR = 8.50; 95% CI: 2.9-25.1) and a <40% decrease in 3D-VCRA had 93% accuracy (HR: 22.50; 95% CI: 2.9-175) in discriminating MV reoperation for stenotic and regurgitant MV, respectively.

CONCLUSIONS

Age <1 year, tethered leaflets, 2DE postoperative MR, changes in 3D-EOA and 3D-VCRA were all independently associated with MV reoperation. 3DE parameters showed a stronger association than 2DE. 3DE-based decision-tree algorithms may help prognostication and serve as a support tool for clinical decision-making.