Abstract
Since there is no proven treatment to reduce mortality in heart failure with preserved ejection fraction patients, to identify the predictors of decompensation are important in these patients.
In this study, we aimed to evaluate the left atrium (LA) mechanical functions in patients with left ventricular hypertrophy (LVH) presenting with dyspnea and to investigate the predictors of pulmonary edema (PE).
This study was designed as a single-center cross-sectional study. Patients with LVH who presented to the emergency department with dyspnea were divided into two groups as PE (n=30) and non-PE (n=100). Mechanical functions of the LA were evaluated by speckle-tracking echocardiography. As a statistical method, diagnostic modelling was performed to demonstrate the relationship between demographic and echocardiographic features with the diagnosis of the patient (with or without PE). First, a basal model was created consisting of age, gender, body mass index (BMI), left ventricular mass index (LVMI), creatinine (Model 0). Then, different models were created by adding reservoir strain (S) (Model 1), conduit S, pump S, reservoir strain rate (SR), conduit SR and pump SR to the basal model, respectively. It was analyzed how each model made changes in performance criteria compared to the basal model.
When the relationship between left atrial strain parameters and PE were analyzed, LA reservoir S (OR: 1.74 (1.14–2.64); p: 0.003) and LA pump SR (OR: 1.69 (1.07–2.64); p: 0.023) had found to be significantly associated with the development of PE. Another parameter associated with PE was admission creatinine value (OR: 1.52 (1.08–2.15), p-value: 0.016). In our study, LVMI is not a predictor for PE but, when the interaction of LA reservoir S and LVMI was considered, it is observed that decreased LA reservoir S is associated with more PE, especially in individuals with higher LVMI.
In this study, we found that the LA reservoir S is significantly associated with the development of PE in patients with LVH, especially in individuals with higher LVMI.
Model 1
| Variables | Odds ratio, 95% confidence interval | p-value |
|---|---|---|
| Age, years (change from 58 to 67) | 0.88 (0.52–1.48) | 0.635 |
| Gender (female:male) | 0.72 (0.29–1.77) | 0.478 |
| LVMI, g/m2 (change from 61.6 to 81.6) | 1.26 (0.97–1.63) | 0.082 |
| BMI, kg/m2 (change from 28.1 to 34.5) | 1.95 (0.56–1.60) | 0.859 |
| Creatinine, mg/dL (change from 0.84 to 1.25) | 1.74 (1.14–2.64) | 0.009 |
| Reservoir S, % (change from 21.8 to 31.2) | 0.4 2 (0.24–0.75) | 0.003 |
| Variables | Odds ratio, 95% confidence interval | p-value |
|---|---|---|
| Age, years (change from 58 to 67) | 0.88 (0.52–1.48) | 0.635 |
| Gender (female:male) | 0.72 (0.29–1.77) | 0.478 |
| LVMI, g/m2 (change from 61.6 to 81.6) | 1.26 (0.97–1.63) | 0.082 |
| BMI, kg/m2 (change from 28.1 to 34.5) | 1.95 (0.56–1.60) | 0.859 |
| Creatinine, mg/dL (change from 0.84 to 1.25) | 1.74 (1.14–2.64) | 0.009 |
| Reservoir S, % (change from 21.8 to 31.2) | 0.4 2 (0.24–0.75) | 0.003 |
Effect of reservoir S on performance criteria according to the basal model.
Interaction of reservoir S and LVMI
Type of funding source: None
