The robustness of the flow-gradient classification of severe aortic stenosis.
JTCVS Open
; 16: 177-188, 2023 Dec.
Article
in En
| MEDLINE
| ID: mdl-38204672
ABSTRACT
Objectives:
A flow-gradient classification is used to determine the indication for intervention for patients with severe aortic stenosis (AS) with discordant echocardiographic parameters. We investigated the agreement in flow-gradient classification by stroke volume (SV) measurement at the left ventricular outflow tract (LVOT) and at the left ventricle.Methods:
Data were used from a prospective cohort study and patients with severe AS (aortic valve area index ≤0.6 cm2/m2) with preserved ejection fraction (>50%) were selected. SV was determined by an echocardiographic core laboratory at the LVOT and by subtracting the 2-dimensional left ventricle end-systolic from the end-diastolic volume (volumetric). Patients were stratified into 4 groups based on SV index (35 mL/m2) and mean gradient (40 mm Hg). The group composition was compared and the agreement between the SV measurements was investigated using regression, correlation, and limits of agreement. In addition, a systematic LVOT diameter overestimation of 1 mm was simulated to study flow-gradient reclassification.Results:
Of 1118 patients, 699 were eligible. The group composition changed considerably as agreement on flow state occurred in only 50% of the measurements. LVOT SV was on average 15.1 mL (95% limits of agreement -24.955.1 mL) greater than volumetric SV. When a systematic 1-mm LVOT diameter overestimation was introduced, the low-flow groups halved.Conclusions:
There was poor agreement in the flow-gradient classification of severe AS as a result of large differences between LVOT and volumetric SV. Furthermore, this classification was sensitive to small measurement errors. These results stress that parameters beyond the flow-gradient classification should be considered to ensure accurate recommendations for intervention.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Observational_studies
Language:
En
Journal:
JTCVS Open
Year:
2023
Document type:
Article
Affiliation country:
Netherlands
Country of publication:
Netherlands