Objectives To perform a prospective longitudinal analysis of lung ultrasound findings in critically ill patients with coronavirus disease 2019 (COVID‐19). Methods Eighty‐nine intensive care unit (ICU) patients with confirmed COVID‐19 were prospectively enrolled and tracked. Point‐of‐care ultrasound (POCUS) examinations were performed with phased array, convex, and linear transducers using portable machines. The thorax was scanned in 12 lung areas: anterior, lateral, and posterior (superior/inferior) bilaterally. Lower limbs were scanned for deep venous thrombosis and chest computed tomographic angiography was performed to exclude suspected pulmonary embolism (PE). Follow‐up POCUS was performed weekly and before hospital discharge. Results Patients were predominantly male (84.2%), with a median age of 43 years. The median duration of mechanical ventilation was 17 (interquartile range, 10–22) days; the ICU length of stay was 22 (interquartile range, 20.2–25.2) days; and the 28‐day mortality rate was 28.1%. On ICU admission, POCUS detected bilateral irregular pleural lines (78.6%) with accompanying confluent and separate B‐lines (100%), variable consolidations (61.7%), and pleural and cardiac effusions (22.4% and 13.4%, respectively). These findings appeared to signify a late stage of COVID‐19 pneumonia. Deep venous thrombosis was identified in 16.8% of patients, whereas chest computed tomographic angiography confirmed PE in 24.7% of patients. Five to six weeks after ICU admission, follow‐up POCUS examinations detected significantly lower rates ( P < .05) of lung abnormalities in survivors. Conclusions Point‐of‐care ultrasound depicted B‐lines, pleural line irregularities, and variable consolidations. Lung ultrasound findings were significantly decreased by ICU discharge, suggesting persistent but slow resolution of at least some COVID‐19 lung lesions. Although POCUS identified deep venous thrombosis in less than 20% of patients at the bedside, nearly one‐fourth of all patients were found to have computed tomography–proven PE.
Objectives: Difficult intravenous access (DIVA) is common in the emergency department (ED). We investigated the extent to which DIVA is associated with care delay outcomes including time to first laboratory draw, therapies, imaging, and ED disposition. Methods: An observational retrospective cohort analysis of patients with DIVA treated between 2018 and 2020 at 2 urban academic EDs was performed. DIVA was defined as patients requiring ultrasound-guided intravenous access placed by physicians or advanced practice providers (APPs) as opposed to landmark-based intravenous placement by nurses. ED throughput variables and disposition time were compared. We correlated DIVA with time to administration of intravenous pain medications, fluids, imaging contrast, laboratory results, and ED disposition. Results: A total of 108,256 subjects with 161,122 total encounters were included. DIVA occurred in 4961 (3.1%) of ED visits. Patients with DIVA were more likely to be female (3.5% vs 2.6% for males, odds ratio [OR] 1.34, 95% confidence interval [CI]: 1.27-1.42), self-identify as black (OR 1.78, 95% CI: 1.66-1.91), and have higher acuity of illness (P < 0.001). Among pediatric patients, DIVA occurred most often in the first year of life at a rate of 3.25%. In adults, DIVA occurred in 2 age peaks; at 35 years (4.02%), and at 63 years (3.44%). In all workflow metrics, the presence of DIVA was associated with significant delays in median time to completion: 50 minutes for pain
Scarce data exist regarding the natural history of lung lesions detected on ultrasound in those who survive severe COVID-19 pneumonia.Objective-We performed a prospective analysis of point-of-care ultrasound (POCUS) findings in critically ill COVID-19 patients during and after hospitalization.Methods-We enrolled 171 COVID-19 intensive care unit patients. POCUS of the lungs was performed with phased array (2-4 MHz), convex (2-6 MHz) and linear (10-15 MHz) transducers, scanning 12 lung areas. Chest computed tomography angiography was performed to exclude suspected pulmonary embolism. Survivors were clinically and sonographically evaluated during a 4 month period for evidence of residual lung injury. Chest computed tomography angiography and echocardiography were used to exclude pulmonary hypertension (PH) and chest high-resolution-computedtomography to exclude interstitial lung disease (ILD) in symptomatic survivors.Results-Cox regression analysis showed that lymphocytopenia (hazard ratio [HR]: 0.88, 95% confidence intervals [CI]: 0.68-0.96, p = .048), increased lactate (HR: 1.17, 95% CI: 0.94-1.46, p = 0.049), and D-dimers (HR: 1.21, 95% CI: 1.03-1.44, p = .03) were mortality predictors. Non-survivors had increased incidence of pulmonary abnormalities (B-lines, pleural line irregularities, and consolidations) compared to survivors (p < .05). During follow-up, POCUS with clinical and laboratory parameters integrated in the semi-quantitative Riyadh-Residual-Lung-Injury scale had sensitivity of 0.82 (95% CI: 0.76-0.89) and specificity of 0.91 (95% CI: 0.94-0.95) in predicting ILD. The prevalence of PH and ILD (non-specific-interstitial-pneumonia) was 7% and 11.8%, respectively. Conclusion-POCUSshowed ability to monitor the evolution of severe COVID-19 pneumonia after hospital discharge, supporting its integration in clinical predictive models of residual lung injury.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.