The effect of body weight on left atrial function determined by longitudinal strain analysis in young adults.

PURPOSE: Obesity is a risk factor for various cardiovascular disorders. Left atrial (LA) function is vital for predicting adverse outcomes in many diseases. LA strain was recently proposed as a noninvasive and valuable parameter for LA functional evaluation. We investigated the effect of body mass index (BMI) values on left atrial functions determined by longitudinal strain analysis in young adults without concomitant disease. METHODS: We prospectively included 134 subjects in our study. Participants were categorized into three subgroups, obese, overweight, and control, according to their BMI. Conventional echocardiographic measurements and strain analysis were performed on all patients. RESULTS: There were 41 patients (30.5%) in the obesity group, 46 patients (34.3%) in the overweight group, and 47 patients (35.0%) in the control group. Obese patients had significantly larger LA volume (46.9 ± 12.1 ml; p < 0.001) compared to overweight and control subjects; however, LA volume index (21.4 ± 6.1 ml/m2 vs. 22.4 ± 6.1 ml/m2 vs. 22.4 ± 5.0 ml/m2; p = 0.652) were similar between groups. In the LA strain analysis, obese patients were found to have lower left atrial reservoir longitudinal strain (LASr) compared to both the overweight and control group (44.2 ± 5.8% vs. 39.1 ± 3.7% vs. 36.5 ± 4.9%; p < 0.001); moreover obese patients had significantly worse left atrial contraction phase longitudinal strain (LASct) (-15.1 ± 3.1% vs. -13.1 ± 2.5%; p = 0.007) and left atrial conduit phase longitudinal strain (LAScd) (-29.0 ± 7.1% vs. -23.3 ± 5.4%; p < 0.001) values compared to the control group. However, LASct and LAScd values did not differ between overweight and obese patients. CONCLUSION: LA function determined by LA strain analysis was impaired in obese and overweight individuals compared to the control group, even in the early stages of life. The prognostic significance of this finding should be investigated in prospective studies.

Predictive Value of Supraventricular Short Runs for New-Onset Atrial Fibrillation in Patients with Ischemic Stroke.

Background: The clinical importance of supraventricular run (SVR) is uncertain in the management of patients with previous cerebrovascular events. We aim to evaluate the role of SVRs in the development of future atrial fibrillation (AF) in patients diagnosed with ischemic stroke. Methods: We retrospectively evaluated patients who underwent 24-h Holter monitoring for the evaluation of possible AF after ischemic cerebrovascular events. The presence and duration of SVR were noted. Subsequent diagnosis of AF was searched in patients with sinus rhythm. Results: A total of 694 patients were included in the analysis. SVR was detected in 104 (14.9%) patients in the study group. Seventy-one (10.2%) patients were diagnosed with AF in the follow-up. SVRs were more prevalent among patients with AF (P < 0.001). The median atrial run duration was 5.96 (2.02-17.84) s in the AF absent group vs. 8.76 (3.78-17.62) s in the AF present group (P < 0.001). The best predictive cut-off duration of an atrial run was 8 s (sensitivity = 61.5% and specificity = 74.4%, Area Under Curve (AUC) = 0.708). Cox regression analysis showed that age (odds ratio [OR]: 1.03, 95% confidence interval [CI]: 1.00-1.060, P = 0.020), presence of short supraventricular run (OR: 2.53, 95% CI 1.40-4.57, P = 0.002), and left atrial diameter (OR: 1.13 95% CI: 1.07-1.19, P < 0.001) were the independent predictors of AF development in the follow-up. Conclusion: Age, left atrial diameter, and the presence of SVRs are associated with an increased risk of future AF after ischemic stroke. SVR duration may be an important parameter in risk stratification.

Can a modified-simplified pulmonary embolism severity index (m-sPESI) be used to predict the need for intensive care in hospitalized COVID-19 patients?

Severe acute respiratory syndrome-associated coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), which has been considered a pandemic by the World Health Organization (WHO). Clinical manifestations of COVID-19 disease may differ, most cases are mild, but a significant minority of patients may develop moderate to severe respiratory symptoms, with the most severe cases requiring intensive care and/or mechanical ventilatory support. In this study, we aimed to identify validity of our modified scoring system for foreseeing the approach to the COVID-19 patient and the disease, the treatment plan, the severity of morbidity and even the risk of mortality from the clinician's point of view. In this single center study, we examined the patients hospitalized with the diagnosis of COVID-19 between 01/04/2020 and 01/06/2020, of the 228 patients who were between 20 and 90 years of age, and whose polymerase chain reaction (PCR) tests of nasal and pharyngeal swab samples were positive. We evaulated 228 (92 male and 136 female) PCR (+) patients. Univariate analysis showed that advanced age (p < 0.001), hemoglobin (p < 0.001), troponin-I (p < 0.001), C-reactive protein (CRP) (p < 0.001), fibrinogen (p < 0.001), HT (p = 0.01), CAD (p = 0.001), DM (p < 0.001), history of malignancy (p = 0.008), along with m-sPESI scores (p < 0.001) were significantly higher in patients that needed intensive care due to COVID-19 infection. In the multivariable logistic regression analysis, only the m-sPESI score higher than ≥ 2 was found to be highly significant in terms of indicating the need for ICU admission (AUC 0.948; 84.6% sensitivity and 94.6% specificity) (p < 0.001). With an increasing number of hospitalized patients, healthcare providers are confronting a deluge of lab results in the process of caring for COVID-19 patients. It is imperative to identify risk factors for mortality and morbidity development. The modified sPESI scoring system, which we put forward, is successful in predicting the course of the disease at the presentation of the patient with COVID-19 disease and predicting the need for intensive care with high specificity and sensitivity, can detect the need for intensive care with high specificity and sensitivity.