Influence of Ultrasound Settings on Laboratory Vertical Artifacts.

OBJECTIVE: The aim of the work described here was to analyze the relationship between the change in ultrasound (US) settings and the vertical artifacts' number, visual rating and signal intensity METHODS: An in vitro phantom consisting of a damp sponge and gelatin mix was created to simulate vertical artifacts. Furthermore, several US parameters were changed sequentially (i.e., frequency, dynamic range, line density, gain, power and image enhancement) and after image acquisition. Five US experts rated the artifacts for number and quality. In addition, a vertical artifact visual score was created to determine the higher artifact rating ("optimal") and the lower artifact rating ("suboptimal"). Comparisons were made between the tested US parameters and baseline recordings. RESULTS: The expert intraclass correlation coefficient for the number of vertical artifacts was 0.694. The parameters had little effect on the "optimal" vertical artifacts but changed their number. Dynamic range increased the number of discernible vertical artifacts to 3 from 36 to 102 dB. CONCLUSION: The intensity did not correlate with the visual rating score. Most of the available US parameters did not influence vertical artifacts.

Left atrial strain is associated with distinct inflammatory and immune profile in patients with COVID-19 pneumonia.

INTRODUCTION: SARS-CoV-2 infection is associated with multiple cardiac manifestations. Left atrial strain (LA-S) by speckle tracking echocardiography (STE) is a novel transthoracic echocardiography (TTE) measure of LA myocardial deformation and diastolic dysfunction, which could lead to early recognition of cardiac injury in severe COVID-19 patients with possible implications on clinical management, organ dysfunction, and mortality. Cardiac injury may occur by direct viral cytopathic effects or virus-driven immune activation, resulting in heart infiltration by inflammatory cells, despite limited and conflicting data are available on myocardial histology. PURPOSE: We aimed to explore LA-S and immune profiles in COVID-19 patients admitted to the intensive care unit (ICU) to identify distinctive features in patients with cardiac injury. METHODS: We enrolled 30 patients > 18 years with positive SARS-CoV-2 RT-PCR, admitted to ICU. Acute myocardial infarction and pulmonary embolism were exclusion criteria. On days D1, D3, and D7 after ICU admission, patients performed TTE, hemogram, cardiac (pro-BNP; troponin) and inflammatory biomarkers (ESR; ferritin; IL1ß; IL6; CRP; d-dimer; fibrinogen; PCT; adrenomedullin, ADM), and immunophenotyping by flow cytometry. RESULTS: Patient's mean age was 60.7 y, with 63% males. Hypertension was the most common risk factor (73%; with 50% of patients under ACEi or ARA), followed by obesity (40%, mean BMI = 31 kg/m2). Cardiac dysfunction was detected by STE in 73% of patients: 40% left ventricle (LV) systolic dysfunction, 60% LV diastolic dysfunction, 37% right ventricle systolic dysfunction. Mortality, hospitalization days, remdesivir use, organ dysfunction, cardiac and serum biomarkers were not different between patients with (DYS) and without cardiac dysfunction (nDYS), except for ADM (increased in nDYS group at D7). From the 77 TTE, there was a striking difference between diastolic dysfunction evaluation by classic criteria compared to STE (28.6% vs. 57.1%, p = 0.0006). Lower reservoir (Ɛ) and contraction (ƐCT) LA-S correlated with IL-6 (Ɛ, p = 0.009, r = - 0.47; ƐCT, p = 0.0002, r = - 0.63) and central memory CD4 T-cells (ƐCT, p = 0.049, r = - 0.24). Along all timepoints, DYS patients showed persistent low lymphocyte counts that recovered at D7 in nDYS patients. DYS patients had lower platelets at D3 and showed a slower recovery in platelet counts and CRP levels; the latter significantly decreased at D7 in nDYS patients (p = 0.009). Overall, patients recovered with an increasing P/F ratio, though to a lesser extent in DYS patients. DISCUSSION: Our study shows that LA-S may be a more sensitive marker for diastolic dysfunction in severe COVID-19, which could identify patients at risk for a protracted inflammatory state. A differential immune trait in DYS patients at ICU admission, with persistent lymphopenia, enriched CM T-cells, and higher IL-6 may suggest distinct inflammatory states or migration patterns in patients that develop cardiac injury.

Machine learning for the real-time assessment of left ventricular ejection fraction in critically ill patients: a bedside evaluation by novices and experts in echocardiography.

BACKGROUND: Machine learning algorithms have recently been developed to enable the automatic and real-time echocardiographic assessment of left ventricular ejection fraction (LVEF) and have not been evaluated in critically ill patients. METHODS: Real-time LVEF was prospectively measured in 95 ICU patients with a machine learning algorithm installed on a cart-based ultrasound system. Real-time measurements taken by novices (LVEFNov) and by experts (LVEFExp) were compared with LVEF reference measurements (LVEFRef) taken manually by echo experts. RESULTS: LVEFRef ranged from 26 to 80% (mean 54 ± 12%), and the reproducibility of measurements was 9 ± 6%. Thirty patients (32%) had a LVEFRef < 50% (left ventricular systolic dysfunction). Real-time LVEFExp and LVEFNov measurements ranged from 31 to 68% (mean 54 ± 10%) and from 28 to 70% (mean 54 ± 9%), respectively. The reproducibility of measurements was comparable for LVEFExp (5 ± 4%) and for LVEFNov (6 ± 5%) and significantly better than for reference measurements (p < 0.001). We observed a strong relationship between LVEFRef and both real-time LVEFExp (r = 0.86, p < 0.001) and LVEFNov (r = 0.81, p < 0.001). The average difference (bias) between real time and reference measurements was 0 ± 6% for LVEFExp and 0 ± 7% for LVEFNov. The sensitivity to detect systolic dysfunction was 70% for real-time LVEFExp and 73% for LVEFNov. The specificity to detect systolic dysfunction was 98% both for LVEFExp and LVEFNov. CONCLUSION: Machine learning-enabled real-time measurements of LVEF were strongly correlated with manual measurements obtained by experts. The accuracy of real-time LVEF measurements was excellent, and the precision was fair. The reproducibility of LVEF measurements was better with the machine learning system. The specificity to detect left ventricular dysfunction was excellent both for experts and for novices, whereas the sensitivity could be improved. TRIAL REGISTRATION: NCT05336448. Retrospectively registered on April 19, 2022.

Characteristic Immune Dynamics in COVID-19 Patients with Cardiac Dysfunction.

Background: We aimed to explore immune parameters in COVID-19 patients admitted to the intensive care unit (ICU) to identify distinctive features in patients with cardiac injury. Methods: A total of 30 COVID-19 patients >18 years admitted to the ICU were studied on days D1, D3 and D7 after admission. Cardiac function was assessed using speckle-tracking echocardiography (STE). Peripheral blood immunophenotyping, cardiac (pro-BNP; troponin) and inflammatory biomarkers were simultaneously evaluated. Results: Cardiac dysfunction (DYS) was detected by STE in 73% of patients: 40% left ventricle (LV) systolic dysfunction, 60% LV diastolic dysfunction, 37% right ventricle systolic dysfunction. High-sensitivity cardiac troponin (hs-cTn) was detectable in 43.3% of the patients with a median value of 13.00 ng/L. There were no significant differences between DYS and nDYS patients regarding mortality, organ dysfunction, cardiac (including hs-cTn) or inflammatory biomarkers. Patients with DYS showed persistently lower lymphocyte counts (median 896 [661−1837] cells/µL vs. 2141 [924−3306] cells/µL, p = 0.058), activated CD3 (median 85 [66−170] cells/µL vs. 186 [142−259] cells/µL, p = 0.047) and CD4 T cells (median 33 [28−40] cells/µL vs. 63 [48−79] cells/µL, p = 0.005), and higher effector memory T cells (TEM) at baseline (CD4%: 10.9 [6.4−19.2] vs. 5.9 [4.2−12.8], p = 0.025; CD8%: 15.7 [7.9−22.8] vs. 8.1 [7.7−13.7], p = 0.035; CD8 counts: 40 cells/µL [17−61] vs. 10 cells/µL [7−17], p = 0.011) than patients without cardiac dysfunction. Conclusion: Our study suggests an association between the immunological trait and cardiac dysfunction in severe COVID-19 patients.

Could strain echocardiography help to assess systolic function in critically ill COVID-19 patients?

Strain echocardiography enables the automatic quantification of the global longitudinal strain (GLS), which is a direct measure of ventricular shortening during systole. In the current context of overwhelmed ICUs and clinician shortage, GLS has the advantage to be quick and easy to measure by non-experts. However, little is known regarding its value to assess bi-ventricular systolic function in critically ill COVID-19 patients. Therefore, we designed a study to compare right and left ventricular GLS with classic echo-Doppler indices of systolic function, namely the ejection fraction for the left ventricle (LVEF) and the fractional area change (FAC), the tricuspid annular plane systolic excursion (TAPSE), and the tissue Doppler velocity of the basal free lateral wall (S') for the right ventricle. Eighty transthoracic echocardiographic evaluations done in 30 ICU patients with COVID-19 were analyzed. We observed a fair relationship (r = 0.73, p < 0.01) between LVEF and left ventricular GLS. The GLS cut-off value of - 22% identified a LVEF < 50% with a sensitivity of 63% and a specificity of 80%. All patients with a GLS > - 17% had a LVEF < 50%. Although statistically significant, relationships between FAC (r = 0.41, p < 0.01), TAPSE (r = 0.26, p < 0.05) and right ventricular GLS were weak. S' was not correlated with right ventricular GLS. In conclusion, left ventricular GLS was useful to assess left ventricular systolic function. However, right ventricular GLS was poorly correlated with FAC, TAPSE and S'. Further studies are needed to clarify what is the best method to assess right ventricular systolic function in ICU patients with COVID-19.