The usefulness of left atrial volume index and left ventricular mass index in determining subclinical cardiac involvement in patients with early-stage sarcoidosis

2015 ◽  
Vol 185 (3) ◽  
pp. 617-621
Author(s):  
H. A. Kasapkara ◽  
A. Şentürk ◽  
E. Bilen ◽  
B. Duran Karaduman ◽  
H. Ayhan ◽  
...  
Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Dharmendrakumar A Patel ◽  
Carl J Lavie ◽  
Richard V Milani ◽  
Hector O Ventura

Background: LV geometry predicts CV events but it is unknown whether left atrial volume index (LAVi) predicts mortality independent of LV geometry in patients with preserved LVEF. Methods: We evaluated 47,865 patients with preserved EF to determine the impact of LAVi and LV geometry on mortality during an average follow-up of 1.7±1.0 years. Results: Deceased patients (n=3,653) had significantly higher LAVi (35.3 ± 15.9 vs. 29.1 ± 11.9, p<0.0001) and abnormal LV geometry (60% vs. 41%, p<0.0001) than survivors (n=44,212). LAVi was an independent predictor of mortality in all four LV geometry groups [Hazard ratio: N= 1.007 (1.002–1.011), p=0.002; concentric remodeling= 1.008 (1.001–1.012), p<0.0001; eccentric hypertrophy= 1.012 (1.006 –1.018), p<0.0001; concentric hypertrophy=1.017 (1.012–1.022), p<0.0001; Figure ]. Comparison of models with and without LAVi for mortality prediction was significant suggesting increased mortality prediction by addition of LAVi to other independent predictors (Table ). Conclusion: LAVi is higher and LV geometric abnormalities are more prevalent in deceased patients with preserved systolic function and are independently associated with increased mortality. LAVi predicts mortality independent of LV geometry and has synergistic influence on all cause mortality prediction in large cohort of patients with preserved ejection fraction.


2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
A Hubert ◽  
V Le Rolle ◽  
E Galli ◽  
A Hernandez ◽  
E Donal

Abstract Aim This work aims to evaluate a novel semi-automatic tool for the assessment of volume-strain loops by transthoracic echocardiography (TTE). The proposed method was evaluated on a typical model of left ventricular (LV) diastolic dysfunction: the cardiac amyloidosis. Method 18 patients with proved cardiac amyloidosis were compared to 19 controls, from a local database. All TTE were performed using Vivid E9 or E95 ultrasound system. The complete method includes several steps: 1) extraction of LV strain full traces from apical 4 and 2 cavities views, 2) estimation of LV volume from these two traces by spline interpolations, 3) resampling of LV strain curves, determined for the same cardiac beat, (in apical 4-, 2- and 3- cavities views) as a function of pre-defined percentage increments of LV-volume and 4) calculation of the LV volume-strain loop area. (Figure 1, panel B) Results (Table 1): LVEF was similar between both groups whereas global longitudinal strain was significantly lower in amyloidosis group (−14.4 vs −20.5%; p<0.001). Amyloidosis group had a worse diastolic function with a greater left atrial volume index (51 vs 22ml/m2), a faster tricuspid regurgitation (2.7 vs 2.0 m/s), a greater E/e' ratio (17.3 vs 5.9) with a p<0.001 for all these indices. Simultaneously, the global area of volume-strain loop was significantly lower in amyloidosis group (36.5 vs 120.0%.mL). This area was better correlated with mean e' with r=0.734 (p<0.001) than all other indices (Figure 1, panel A). Table 1 Amyloidosis (N=18) Controls (N=19) p Global strain-volume loop area (%.mL) 36.5±21.3 120.0±54.2 <0.001 Global longitudinal strain (%) −14.4±3.8 −20.5±1.8 <0.001 Left ventricular ejection fraction (%) 62±7 65±5 0.08 Left atrial volume index (ml/m2) 51±22 22±5 <0.001 E/A 1.72±0.97 2.07±0.45 0.17 Mean e' 5.5±1.3 14.4±2.8 <0.001 Mean E/e' 17.3±5.4 5.9±1.4 <0.001 Tricuspid regurgitation velocity (m/s) 2.7±3.8 2.0±0.3 <0.001 Figure 1 Conclusion LV volume-strain loop area appears a very promising new tool to assess semi-automatically diastolic function. Future applications will concern the integration of LV volume-strain loop area as novel feature in machine-learning approach.


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