Reclassifying Aortic Stenosis Severity: Combined Energy Loss Index and Global Longitudinal Strain Assessment Identifies Subgroups with Differential Myocardial Function and May Improve Risk Stratification in Aortic Stenosis

TL;DR

This study shows that a new method combining energy loss index and heart strain measurements can better identify aortic stenosis severity and heart function than traditional methods.

Contribution

The study introduces a novel reclassification method for aortic stenosis severity using energy loss index and global longitudinal strain to detect subclinical myocardial dysfunction.

Findings

ELI reclassified 29% of severe AS patients to moderate based on better myocardial function.

ELI showed stronger correlation with GLS than AVA in patients with preserved ejection fraction.

ELI remained an independent predictor of GLS when both parameters were analyzed together.

Abstract

Background: Traditional echocardiographic assessment of aortic stenosis (AS) using aortic valve area (AVA) may overestimate severity due to pressure recovery phenomena, while subclinical myocardial dysfunction remains undetected despite preserved ejection fraction. This study evaluated whether energy loss index (ELI)—which accounts for pressure recovery—demonstrates superior correlation with global longitudinal strain (GLS), a marker of subclinical myocardial dysfunction, compared to conventional AVA-based classification in patients with moderate-to-severe AS and preserved left ventricular ejection fraction (LVEF). Methods: This retrospective single-center study analyzed 149 patients with moderate-to-severe AS (AVA < 1.5 cm2) and LVEF > 50% from 2015 to 2019. Among 97 patients with severe AS by AVA (<1.0 cm2), ELI was calculated using the formula ELI = (AVA × Aa)/(Aa − AVA) ÷ BSA, where Aa represents sinotubular junction cross-sectional area. Patients with ELI ≥ 0.6 cm2/m2 were reclassified as moderate AS. Spearman correlation assessed relationships between AVA, ELI, and GLS. Multivariable linear regression models determined independent predictors of myocardial dysfunction, adjusting for age, body surface area, hypertension, LVEF, and mean pressure gradient. Results: ELI reclassified 28 of 97 patients (29%) from severe to moderate AS. Reclassified patients had significantly better myocardial function, with less impaired GLS (−15.0 ± 3.9% vs. −12.1 ± 5.0%, p = 0.013) and higher LVEF (60.1 ± 6.2% vs. 56.5 ± 9.1%, p = 0.017) compared to non-reclassified patients. In the overall cohort, ELI demonstrated stronger correlation with GLS than AVA (r = −0.307, p = 0.0003 vs. r = −0.209, p = 0.0115). Critically, among patients with severe AS by AVA criteria, ELI maintained significant correlation with GLS (r = −0.443, p = 0.0003) while AVA showed no correlation (r = −0.144, p = 0.159). In multivariable analysis, ELI independently predicted GLS (β = 5.847, 95% CI: 2.85–8.84, p = 0.0002; adjusted R2 = 0.289), whereas AVA did not (β = 2.234, 95% CI: −1.08 to 5.55, p = 0.188; adjusted R2 = 0.234). When both parameters were included simultaneously, only ELI remained significant (p = 0.0024). Conclusions: In this retrospective cohort, ELI-based reclassification identified a subgroup of patients with less severe myocardial dysfunction as measured by GLS and LVEF, and ELI demonstrated superior correlation with subclinical myocardial dysfunction compared to AVA. These findings suggest ELI may provide a more physiologically reflective assessment of hemodynamic burden in AS with preserved LVEF. However, the absence of systematic symptom assessment and clinical outcome data represents critical limitations. Prospective studies with standardized symptom evaluation, longitudinal follow-up, and adjudicated clinical endpoints are required to determine whether ELI-based reclassification improves risk stratification and clinical decision-making before this approach can be recommended for routine practice.

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