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Multi-parametric approach to predict prosthetic valve size using CMR and clinical data: insights from SAVR

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Abstract

The purpose of this investigation was to characterize the CMR and clinical parameters that correlate to prosthetic valve size (PVS) determined at SAVR and develop a multi-parametric model to predict PVS. Sixty-two subjects were included. Linear/area measurements of the aortic annulus were performed on cine CMR images in systole/diastole on long/short axis (SAX) views. Clinical parameters (age, habitus, valve lesion, valve morphology) were recorded. PVS determined intraoperatively was the reference value. Data were analyzed using Spearman correlation. A prediction model combining imaging and clinical parameters was generated. Imaging parameters had moderate to moderately strong correlation to PVS with the highest correlations from systolic SAX mean diameter (r = 0.73, p < 0.0001) and diastolic SAX area (r = 0.73, p < 0.0001). Age was negatively correlated to PVS (r = − 0.47, p = 0.0001). Weight was weakly correlated to PVS (r = 0.27, p = 0.032). AI and bicuspid valve were not predictors of PVS. A model combining clinical and imaging parameters had high accuracy in predicting PVS (R2 = 0.61). Model predicted mean PVS was 23.3 mm (SD 1.1); actual mean PVS was 23.3 mm (SD 1.3). The Spearman r of the model (0.80, 95% CI 0.683–0.874) was significantly higher than systolic SAX area (0.68, 95% CI 0.516–0.795). Clinical parameters like age and habitus impact PVS; valve lesion/morphology do not. A multi-parametric model demonstrated high accuracy in predicting PVS and was superior to a single imaging parameter. A multi-parametric approach to device sizing may have future application in TAVR.

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Data availability

Data is available upon reasonable request.

Abbreviations

3ch:

3 Chamber

AI:

Aortic insufficiency

AS:

Aortic stenosis

CMR:

Cardiac magnetic resonance

PVS:

Prosthetic valve size

SAX:

Short axis

SAVR:

Surgical aortic valve replacement

TAVR:

Transcatheter aortic valve replacement

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Authors and Affiliations

  1. Section of Cardiothoracic Surgery, Heart Center, Veterans Affairs Medical Center, Washington, DC, USA

    Conor F. Hynes, Jeffrey Panting, Dominic A. Emerson & Gregory D. Trachiotis

  2. Section of Cardiology, Heart Center, Veterans Affairs Medical Center, Washington, DC, USA

    Federico E. Mordini, Jason Morrissette, Erin Goheen-Thomas & Michael D. Greenberg

  3. The George Washington University School of Medicine, Washington, DC, USA

    Conor F. Hynes, Richard L. Amdur, Jeffrey Panting, Dominic A. Emerson & Gregory D. Trachiotis

  4. Georgetown University School of Medicine, Washington, DC, USA

    Federico E. Mordini, Conor F. Hynes & Dominic A. Emerson

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  1. Federico E. Mordini
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Correspondence to Federico E. Mordini.

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Mordini, F.E., Hynes, C.F., Amdur, R.L. et al. Multi-parametric approach to predict prosthetic valve size using CMR and clinical data: insights from SAVR. Int J Cardiovasc Imaging 37, 2269–2276 (2021). https://doi.org/10.1007/s10554-021-02203-5

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