IntroductionThe number of vertical artefacts (VAs) in lung ultrasound (LUS) impacts patients’ clinical management. This study aimed to demonstrate the influence of ultrasound settings on the number of VAs in patients under invasive mechanical ventilation (IMV).MethodsPatients under IMV were recruited for LUS, including three breathing cycles with a motionless curvilinear probe on the thoracic region with the most VAs. Three experts in LUS were asked about the number of VAs at random, and blinded after altering the settings for a total of 20 test recordings per patient. The correlation between expert classifications was tested after grading the classifications. The number of VAs across clinicians was compared between baseline recordings and test condition recordings to determine statistical differences.Results29 patients were enrolled with a median Sequential Organ Failure Assessment score of 6 (interquartile range (IQR) 3). IMV was mainly due to stroke (n=10) and pneumonia (n=6). LUS was made between days 1 and 6 (IQR). Baseline recordings showed a median of 2±2 VAs in inspiration and a median of 1±2 in expiration from a total of 3636 expert classifications, with a strong agreement within patients. A probe frequency of 8 MHz, artefact filtering, speckle reduction and frame average reduced the median VA number by one. A power of −20 dB and dynamic range of 32 dB abolished the VAs. A gain above 90% increased the median number of VAs by one.ConclusionIn thisin vivostudy, the LUS settings influenced the VA number in IMV patients, after controlling for physiological and operator confounders.

Quantitative approaches to improve lung ultrasound (LUS) vertical artifacts (VA) interpretation using total signal intensity (ITOT) are not widely available for clinical practice. In this study, we aimed to i) develop a mathematical algorithm to extract ITOT as a post-hoc LUS analysis and ii) confirm ITOT utility by conducting laboratory VA research using an in vitro model with different acoustic channels.The ITOT was extracted from static and conventional LUS imaging recorded from in vitro models after varying the amount of water content or the pores size of the phantom, compared to a control condition.The defined algorithm was able to calculate the ITOT from all phantoms. Mean ITOT showed statistically significantly different values across phantom categories.We demonstrate that ITOT may be able to differentiate the in vitro acoustic channels formed by increased water content from those with small size pores. However, the utility of this semi-quantitative tool in clinical practice or other LUS imaging data sets remains unclear.

Abstract 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; IL1β; 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.

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 arti-facts. 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 parame-ters 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 num -ber 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.

Editor—The portability and cost of ultrasound devices, and operator skills, remain significant barriers to the adoption of point-of-care echocardiography. 1 Vieillard-Baron A. Millington S.J. Sanfilippo F. et al. A decade of progress in critical care echocardiography: a narrative review. Intensive Care Med. 2019; 45: 770-788 Crossref PubMed Scopus (125) Google Scholar , 2 Marbach J.A. Almufleh A. Di Santo P. et al. A shifting paradigm: the role of focused cardiac ultrasound in bedside patient assessment. Chest. 2020; 158: 2107-2118 Abstract Full Text Full Text PDF PubMed Scopus (18) Google Scholar , 3 Mayo P.H. Chew M. Douflé M. et al. Machines that save lives in the intensive care unit: the ultrasonography machine. Intensive Care Med. 2022; 48: 1429-1438 Crossref PubMed Scopus (6) Google Scholar Recent hardware and software ultrasound innovations include crystal-free pocket probes and algorithms designed to facilitate and automate echocardiographic measurements, and connectivity to smartphones. 3 Mayo P.H. Chew M. Douflé M. et al. Machines that save lives in the intensive care unit: the ultrasonography machine. Intensive Care Med. 2022; 48: 1429-1438 Crossref PubMed Scopus (6) Google Scholar ,4 Le M.P.T. Voigt L. Nathanson R. et al. Comparison of four handheld point-of-care ultrasound devices by expert users. Ultrasound J. 2022; 14: 27 Crossref PubMed Scopus (18) Google Scholar We designed the present study to compare automatic measurements of left ventricular ejection fraction (LVEF) taken with a silicon chip ultrasound probe and a smartphone (LVEFSMART) to reference manual measurements (LVEFREF) taken with a high-end ultrasound device.

article: Comment on: lung ultrasound predicts non-invasive ventilation outcome in COVID-19 acute respiratory failure: a pilot study - Minerva Anestesiologica 2022 June;88(6):529-30 - Minerva Medica - Journals

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 (LVEF Nov ) and by experts (LVEF Exp ) were compared with LVEF reference measurements (LVEF Ref ) taken manually by echo experts. Results LVEF Ref ranged from 26 to 80% (mean 54 ± 12%), and the reproducibility of measurements was 9 ± 6%. Thirty patients (32%) had a LVEF Ref < 50% (left ventricular systolic dysfunction). Real-time LVEF Exp and LVEF Nov 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 LVEF Exp (5 ± 4%) and for LVEF Nov (6 ± 5%) and significantly better than for reference measurements ( p < 0.001). We observed a strong relationship between LVEF Ref and both real-time LVEF Exp ( r = 0.86, p < 0.001) and LVEF Nov ( r = 0.81, p < 0.001). The average difference (bias) between real time and reference measurements was 0 ± 6% for LVEF Exp and 0 ± 7% for LVEF Nov . The sensitivity to detect systolic dysfunction was 70% for real-time LVEF Exp and 73% for LVEF Nov . The specificity to detect systolic dysfunction was 98% both for LVEF Exp and LVEF Nov . 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.

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.

Editor—Point-of-care ultrasound techniques are increasingly used for the bedside assessment of cardiac function and haemodynamics in critically ill patients. The sub-aortic or left ventricular outflow tract velocity time integral (VTI) can be measured using pulsed-Doppler ultrasonography from a transthoracic apical 5-chamber view. 1 Blanco P. Aguiar F.M. Blaivas M. Rapid ultrasound in shock (RUSH) velocity time integral. J Ultrasound Med. 2015; 34: 1691-1700 Crossref PubMed Scopus (52) Google Scholar Quantifying VTI is useful to discriminate between vasoplegic states (hypotension with normal/high VTI) and low flow states (low VTI). 1 Blanco P. Aguiar F.M. Blaivas M. Rapid ultrasound in shock (RUSH) velocity time integral. J Ultrasound Med. 2015; 34: 1691-1700 Crossref PubMed Scopus (52) Google Scholar ,2 Mercado P. Maizel J. Beyls C. et al. Transthoracic echocardiography: an accurate and precise method for estimating cardiac output in the critically ill patient. Crit Care. 2017; 21: 136 Crossref PubMed Scopus (88) Google Scholar Measuring VTI is also useful to predict fluid responsiveness, either by quantifying the respiratory swings in VTI when patients are mechanically ventilated, 3 Feissel M. Michard F. Mangin I. et al. Respiratory changes in aortic blood velocity as an indicator of fluid responsiveness in ventilated patients with septic shock. Chest. 2001; 119: 867-873 Abstract Full Text Full Text PDF PubMed Scopus (450) Google Scholar or by quantifying VTI changes during a passive leg raising manoeuvre or a fluid challenge. 4 Muller L. Toumi M. Bousquet P.J. et al. An increase in aortic blood flow after an infusion of 100 ml colloid over 1 minute can predict fluid responsiveness: the mini-fluid challenge study. Anesthesiology. 2011; 115: 541-547 Crossref PubMed Scopus (217) Google Scholar

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.