ABSTRACT
Background: The initial insights from the studies on COVID-19 had been disappointing, indicating the necessity of an aggravated search for alternative strategies. In this regard, the adjunct potential of yoga has been proposed for enhancing the effectiveness of the standard of care with respect to COVID-19 management. We tested whether a telemodel of yoga intervention could aid in better clinical management for hospitalized patients with mild-to-moderate COVID-19 when complemented with the standard of care. Methods: This was a randomized controlled trial conducted at the Narayana Hrudyalaya, Bengaluru, India, on hospitalized patients with mild-to-moderate COVID-19 infection enrolled between 31 May and 22 July 2021. The patients (n = 225) were randomized in a 1:1 ratio [adjunct tele-yoga (n = 113) or standard of care]. The adjunct yoga group received intervention in tele-mode within 4-h post-randomization until 14 days along with the standard of care. The primary outcome was the clinical status on day 14 post-randomization, assessed with a seven-category ordinal scale. The secondary outcome set included scores on the COVID Outcomes Scale on day 7, follow-up for clinical status and all-cause mortality on day 28, post-randomization, duration of days at the hospital, 5th-day changes post-randomization for viral load expressed as cyclic threshold (Ct), and inflammatory markers and perceived stress scores on day 14. Results: As compared with the standard of care alone, the proportional odds of having a higher score on the 7-point ordinal scale on day 14 were ~1.8 for the adjunct tele-yoga group (OR = 1.83, 95% CI, 1.11-3.03). On day 5, there were significant reductions in CRP (P = 0.001) and LDH levels (P = 0.029) in the adjunct yoga group compared to the standard of care alone. CRP reduction was also observed as a potential mediator for the yoga-induced improvement of clinical outcomes. The Kaplan-Meier estimate of all-cause mortality on day 28 was the adjusted hazard ratio (HR) of 0.26 (95% CI, 0.05-1.30). Conclusion: The observed 1.8-fold improvement in the clinical status on day 14 of patients of COVID-19 with adjunct use of tele-yoga contests its use as a complementary treatment in hospital settings.
Subject(s)
COVID-19 , Yoga , Humans , COVID-19/therapy , SARS-CoV-2 , IndiaABSTRACT
Background and Aims: The incorporation of artificial intelligence (AI) in point-of-care ultrasound (POCUS) has become a very useful tool to quickly assess cardiorespiratory function in coronavirus disease (COVID)-19 patients. The objective of this study was to test the agreement between manual and automated B-lines counting, left ventricular outflow tract velocity time integral (LVOT-VTI) and inferior vena cava collapsibility index (IVC-CI) in suspected or confirmed COVID-19 patients using AI integrated POCUS. In addition, we investigated the inter-observer, intra-observer variability and reliability of assessment of echocardiographic parameters using AI by a novice. Methods: Two experienced sonographers in POCUS and one novice learner independently and consecutively performed ultrasound assessment of B-lines counting, LVOT-VTI and IVC-CI in 83 suspected and confirmed COVID-19 cases which included both manual and AI methods. Results: Agreement between automated and manual assessment of LVOT-VTI, and IVC-CI were excellent [intraclass correlation coefficient (ICC) 0.98, P < 0.001]. Intra-observer reliability and inter-observer reliability of these parameters were excellent [ICC 0.96-0.99, P < 0.001]. Moreover, agreement between novice and experts using AI for LVOT-VTI and IVC-CI assessment was also excellent [ICC 0.95-0.97, P < 0.001]. However, correlation and intra-observer reliability between automated and manual B-lines counting was moderate [(ICC) 0.52-0.53, P < 0.001] and [ICC 0.56-0.69, P < 0.001], respectively. Inter-observer reliability was good [ICC 0.79-0.87, P < 0.001]. Agreement of B-lines counting between novice and experts using AI was weak [ICC 0.18, P < 0.001]. Conclusion: AI-guided assessment of LVOT-VTI, IVC-CI and B-lines counting is reliable and consistent with manual assessment in COVID-19 patients. Novices can reliably estimate LVOT-VTI and IVC-CI using AI software in COVID-19 patients.
ABSTRACT
BACKGROUND: Coronavirus disease-2019 (COVID-19) causes various cardiopulmonary manifestations. Bedside ultrasound helps in the rapid diagnosis of these manifestations. Vscan Extend™ (GE, Wauwatosa, WI, USA) is a handheld ultrasound device with a dual probe and an artificial intelligence application to detect ejection fraction. It can help in reducing the time for diagnosis, duration, and the number of healthcare workers exposed to COVID-19. This is a prospective observational study comparing the cardiorespiratory parameters and time duration for assessment between Vscan Extend™ and the conventional ultrasound machine. MATERIALS AND METHODS: Paired observations were made in 96 COVID-19 patients admitted to the intensive care unit by two intensivists. Intensivist A used the Vscan Extend™ device to assess the cardiac function, lung fields, diaphragm, deep veins, and abdomen. Intensivist B used clinical examination, X-ray chest, ECG, and conventional echocardiogram for assessment. The agreement between the findings and the time duration required in both the methods was compared. RESULTS: The use of handheld ultrasound has significantly decreased the duration of bedside examination of patients than the conventional method. The median duration of examination using handheld ultrasound was 9 (8.0-11.0) minutes, compared to 20 (17-22) minutes with the conventional method (P < 0.001). The Cohen's kappa coefficient was 1.0 for left ventricular systolic function, most of the lung fields, and diaphragmatic movement. CONCLUSION: Vscan Extend™ helps in the rapid identification and diagnosis of cardiopulmonary manifestations in COVID-19 patients. The agreement between the handheld device and the conventional method proves its efficacy and safety. CTRI NUMBER: CTRI/2020/07/026701. HOW TO CITE THIS ARTICLE: Maheshwarappa HM, Mishra S, Kulkarni AV, Gunaseelan V, Kanchi M. Use of Handheld Ultrasound Device with Artificial Intelligence for Evaluation of Cardiorespiratory System in COVID-19. J Crit Care Med 2021;25(5):524-527.
ABSTRACT
The coronavirus disease 2019 (COVID-19) is a pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2). Although 85% of infected patients remain asymptomatic, 5% show severe symptoms such as hypoxaemic respiratory failure and multiple end organ dysfunction (MODS) requiring intensive care unit (ICU) admission with a mortality rate of about 2.8%. Since a definitive treatment is yet to be identified, preventive and supportive strategies remain the mainstay of management. Supportive measures such as oxygen therapy with nasal cannula, face mask, noninvasive ventilation, mechanical ventilation and even extreme measures such as extracorporeal membrane oxygenation (ECMO) fail to improve oxygenation in some patients. Hence, hyperbaric oxygen therapy (HBOT) has been proposed as a supportive strategy to improve oxygenation in COVID-19 patients. HBOT is known to increase tissue oxygenation by increasing the amount of dissolved oxygen in plasma. HBOT also mitigates tissue inflammation thus reducing the ill effects of cytokine storm in COVID-19 patients. Though there is limited literature available on HBOT in COVID-19 patients, considering the present need for additional supportive therapy to improve oxygenation, HBOT has been proposed as a novel supportive treatment in COVID-19 patients.