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Published on: February 13, 2021
Simulation of Repair on Dynamic Patient-Specific Left Atrioventricular Valve Models
Stephen Ching1, Christopher Zelonis1, Christian Herz1
1Children's Hospital of Philadelphia, Department of Anesthesia and Critical Care Medicine, Philadelphia, PA.
Purpose:
To develop and evaluate a dynamic, image-derived patient-specific physical simulation platform for the assessment of left atrioventricular valve (LAVV) repair strategies in pediatric patients with repaired atrioventricular canal defects.
Methods:
3D transesophageal echocardiographic images of two patients with regurgitant LAVVs were identified from an institutional database. Custom code in SlicerHeart was used to segment leaflets, define the annulus, and generate patient-specific valve molds. Silicone valve models were fabricated and tested in a pulse duplicator under simulated physiological conditions. Five unrepaired valves were analyzed for manufacturing consistency, and multiple surgical repair techniques were compared for two patient-specific models.
Results:
Manufacturing variability was low in annular metrics (CV for annular circumference: 2.1%; commissural distance: 4.1%; annulus height: 14.7%) but higher in leaflet closure metrics (billow height: 11.1%; billow volume: 18.9%; tenting height: 45.9%; tenting volume: 73.5%). In Patient 1, cleft closure and an Alfieri stitch both eliminated the regurgitant orifice area, but the Alfieri stitch resulted in elevated mean pressure gradient (17 mmHg vs. 4-9 mmHg for other repairs) and deteriorated with repeated loading. In Patient 2, no repair eliminated regurgitation entirely; however, combining an 11 mm patch augmentation with commissuroplasty reduced regurgitant area to 0.147 cm2, the smallest observed among tested strategies.
Conclusion:
This study demonstrates the feasibility of a dynamic physical simulation platform for preclinical LAVV repair evaluation. Although challenges remain in accurately modeling leaflet closure and chordal mechanics, this proof-of-concept work highlights the platform's potential for refining repair strategies before clinical application, which may be particularly relevant in small and heterogeneous populations with congenital heart disease.

