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CT-Defined Prosthesis-Patient Mismatch Downgrades Frequency and Severity, and Demonstrates No Association With Adverse Outcomes After Transcatheter Aortic Valve Replacement. | LitMetric

AI Article Synopsis

Article Abstract

Objectives: This study sought to determine if indexed effective orifice area (EOAi), using left ventricular outflow tract measured from computed tomography (EOAi), reclassified prosthesis-patient mismatch (PPM) compared with conventional echocardiogram-defined measurements (EOAi).

Background: PPM does not predict mortality following transcatheter aortic valve replacement (TAVR). However, it is unknown if the EOAi of the left ventricular outflow tract improves risk stratification.

Methods: A total of 765 TAVR patients from the PARTNER II (Placement of Aortic Transcatheter Valves II) trial S3i cohort were evaluated. EOAi was calculated using the continuity equation, and the left ventricular outflow tract area was derived from baseline computed tomography. Traditional echocardiographic categories defined PPM: absent (>0.85 cm/m), moderate (≥0.65 and ≤0.85 cm/m), or severe (≤0.65 cm/m). Correlation of EOAi and EOAi to 1-year outcomes was performed.

Results: The incidence of PPM was 24% with EOA compared with 45% with EOAi. Only 6% of PPM was graded severe by EOAi compared with 9% by EOAi. EOAi, but not EOAi, defined PPM showed association with reduced left ventricular mass regression (p = 0.03 vs. p = 0.52). There was no association between PPM and death or rehospitalization at 1 year with either modality. EOA was associated with minor stroke at 1 year (log-rank p = 0.04), and EOAi with stroke/transient ischemic attack (log-rank p = 0.030). Furthermore, when subjects with mild or greater paravalvular regurgitation were excluded, the presence of PPM did not show association with any outcome.

Conclusions: EOAi downgrades frequency and severity of PPM in patients after TAVR, and was not associated with mortality 1 year after TAVR. EOAi, but not EOAi, was associated with less left ventricular mass regression.

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http://dx.doi.org/10.1016/j.jcin.2017.05.031DOI Listing

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