Automation of sub-aortic velocity time integral measurements by transthoracic echocardiography: clinical evaluation of an artificial intelligence-enabled tool in critically ill patients

© 2022 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.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. Quantifying VTI is useful to discriminate between vasoplegic states (hypotension with normal/high VTI) and low flow states (low VTI). Measuring VTI is also useful to predict fluid responsiveness, either by quantifying the respiratory swings in VTI when patients are mechanically ventilated, or by quantifying VTI changes during a passive leg raising manoeuvre or a fluid challenge.info:eu-repo/semantics/publishedVersio

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

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.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.info:eu-repo/semantics/publishedVersio

Influence of ultrasound settings on laboratory vertical artifacts

The work was partially funded by RESEARCH 4 COVID-19 (no. 101) from Fundação para a Ciência e Tecnologia.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.info:eu-repo/semantics/publishedVersio

Characteristic Immune Dynamics in COVID-19 Patients with Cardiac Dysfunction

Funding Information: Type of funding sources: Foundation—015_595935779—Foundation for Science and Technology (FCT), in collaboration with the Agency for Clinical Research and Biomedical Innovation (AICIB) opened special funding, “RESEARCH 4 COVID-19”, to R&D projects and initiatives that respond to the needs of the National Health Service (SNS) as a response to this and future pandemics in a very short time Horizon. Project: “Early recognition of cardiac injury associated with COVID-19 and clinical outcomes”.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.publishersversionpublishe