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Congestive Heart Failure

Abstract

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Original ResearchVolume 13, Issue 1 Part 2, January 2020

JOURNAL：JACC: Cardiovascular Imaging Article Link

Association of Reduced Apical Untwisting With Incident HF in Asymptomatic Patients With HF Risk Factors

M Przewlocka-Kosmala, TH Marwick, H Yang et al. Keywords: community screening; echocardiography; heart failure; left ventricular untwisting

ABSTRACT

OBJECTIVES - This study investigated the prognostic utility of left ventricular (LV) untwisting (UT) in the elderly patients at risk of heart failure (HF).

 

BACKGROUND - LV UT mechanics represent a unique combination of LV filling linking ventricular relaxation and suction. The value of this parameter in the prediction of outcomes in patients at risk of HF is unclear.

 

METHODS - A group of 465 asymptomatic subjects ≥65 years of age with ≥1 HF risk factor (hypertension, diabetes, obesity), recruited from the community, underwent clinical evaluation and echocardiography including measurement of LV apical and basal peak UT velocities. Cox regression analysis was used to identify predictors of new-onset HF and cardiovascular death after a mean follow-up of 18.2 ± 7.5 months.

 

RESULTS - A composite of both of the study endpoints occurred in 54 patients (11.6%). Adverse outcome was significantly associated with apical (hazard ratio [HR]: 0.98; 95% confidence interval [CI]: 0.96 to 0.99; p = 0.006) UT but not with basal (p = 0.18) UT. The prognostic value of apical UT was independent of and incremental to clinical data, as expressed by the ARIC (Atherosclerosis Risk In Communities) study risk score, left atrial volume index (LAVI), and LV global longitudinal strain (GLS). The addition of apical UT to the model including ARIC risk score, LAVI, and GLS was associated with a 41% improvement in reclassification (p = 0.006).

 

CONCLUSIONS - Echocardiographic assessment of apical UT provides incremental value in predicting adverse outcome in asymptomatic patients with HF risk factors. The inclusion of apical UT to the diagnostic algorithm may improve the prognostication process in this population.

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