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

Early identification of intensive care unit-acquired infections with daily monitoring of C-reactive protein: a prospective observational study

INTRODUCTION: Manifestations of sepsis are sensitive but are poorly specific of infection. Our aim was to assess the value of daily measurements of C-reactive protein (CRP), temperature and white cell count (WCC) in the early identification of intensive care unit (ICU)-acquired infections. METHODS: We undertook a prospective observational cohort study (14 month). All patients admitted for ≥72 hours (n = 181) were divided into an infected (n = 35) and a noninfected group (n = 28). Infected patients had a documented ICU-acquired infection and were not receiving antibiotics for at least 5 days before diagnosis. Noninfected patients never received antibiotics and were discharged alive. The progression of CRP, temperature and WCC from day -5 to day 0 (day of infection diagnosis or of ICU discharge) was analyzed. Patients were divided into four patterns of CRP course according to a cutoff value for infection diagnosis of 8.7 mg/dl: pattern A, day 0 CRP >8.7 mg/dl and, in the previous days, at least once below the cutoff; pattern B, CRP always >8.7 mg/dl; pattern C, day 0 CRP ≤8.7 mg/dl and, in the previous days, at least once above the cutoff; and pattern D, CRP always ≤8.7 mg/dl. RESULTS: CRP and the temperature time-course showed a significant increase in infected patients, whereas in noninfected it remained almost unchanged (P < 0.001 and P < 0.001, respectively). The area under the curve for the maximum daily CRP variation in infection prediction was 0.86 (95% confidence interval: 0.752–0.933). A maximum daily CRP variation >4.1 mg/dl was a good marker of infection prediction (sensitivity 92.1%, specificity 71.4%), and in combination with a CRP concentration >8.7 mg/dl the discriminative power increased even further (sensitivity 92.1%, specificity 82.1%). Infection was diagnosed in 92% and 90% of patients with patterns A and B, respectively, and in only two patients with patterns C and D (P < 0.001). CONCLUSION: Daily CRP monitoring and the recognition of the CRP pattern could be useful in the prediction of ICU-acquired infections. Patients presenting maximum daily CRP variation >4.1 mg/dl plus a CRP level >8.7 mg/dl had an 88% risk of infection

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

Cooperative Behaviour of specific tasks in multi-agent systems and robot control using dynamic approach

In order to foster research and development in a multi-agent robotic environment three fundamental improvements on the robots need to be carried out: a) a very reliable and robot control which works at high speeds and a dynamic approach is described in this work; b) Cooperative behaviour is very important since without it there is no ball pass, and that is becoming more and more necessary; c) Upwards kick, since traditional horizontal kickers are already very common. Other improvements were carried out in the robots but due to lack of space in this paper are not described. This paper describes how these three issues were tackled by the MINHO team and shows their next directions

High accuracy navigation in unknown environment using adaptive control

Aiming to reduce cycle time and improving the accuracy on tracking, a modified adaptive control was developed, which adapts autonomously to changing dynamic parameters. The platform used is based on a robot with a vision based sensory system. Goal and obstacles angles are calculated relatively to robot orientation from image processing software. Autonomous robots are programmed to navigate in unknown and unstructured environments where there are multiple obstacles which can readily change their position. This approach underlies in dynamic attractor and repulsive forces. This theory uses differential equations that produce vector fields to control speed and direction of the robot. This new strategy was compared with existing PID method experimentally and it proved to be more effective in terms of behaviour and time-response. Calibration parameters used in PID control are in this case unnecessary. The experiments were carried out in robot Middle Size League football players built for RoboCup. Target pursuit, namely, ball, goal or any absolute position, was tested. Results showed high tracking accuracy and rapid response to moving targets. This dynamic control system enables a good balance between fast movements and smooth behaviour

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

Development of a preoperative risk score on admission in surgical intermediate care unit in gastrointestinal cancer surgery

DSAIPA/DS/0042/2018 UID/DTP/00617/2019Background: Gastrointestinal cancer surgery continues to be a significant cause of postoperative complications and mortality in high-risk patients. It is crucial to identify these patients. Our study aimed to evaluate the accuracy of specific perioperative risk assessment tools to predict postoperative complications, identifying the most informative variables and combining them to test their prediction ability as a new score. Methods: A prospective cohort study of digestive cancer surgical patients admitted to the surgical intermediate care unit of the Portuguese Oncology Institute of Porto, Portugal was conducted during the period January 2016 to April 2018. Demographic and medical information including sex, age, date from hospital admission, diagnosis, emergency or elective admission, and type of surgery, were collected. We analyzed and compared a set of measurements of surgical risk using the risk assessment instruments P-POSSUM Scoring, ACS NSQIP Surgical Risk Calculator, and ARISCAT Risk Score according to the outcomes classified by the Clavien-Dindo score. According to each risk score system, we studied the expected and observed post-operative complications. We performed a multivariable regression model retaining only the significant variables of these tools (age, gender, physiological P-Possum, and ACS NSQIP serious complication rate) and created a new score (MyIPOrisk-score). The predictive ability of each continuous score and the final panel obtained was evaluated using ROC curves and estimating the area under the curve (AUC). Results: We studied 341 patients. Our results showed that the predictive accuracy and agreement of P-POSSUM Scoring, ACS NSQIP Surgical Risk Calculator, and ARISCAT Risk Score were limited. The MyIPOrisk-score, shows to have greater discrimination ability than the one obtained with the other risk tools when evaluated individually (AUC = 0.808; 95% CI: 0.755-0.862). The expected and observed complication rates were similar to the new risk tool as opposed to the other risk calculators. Conclusions: The feasibility and usefulness of the MyIPOrisk-score have been demonstrated for the evaluation of patients undergoing digestive oncologic surgery. However, it requires further testing through a multicenter prospective study to validate the predictive accuracy of the proposed risk score.publishersversionpublishe