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1.
CMAJ Open ; 10(3): E675-E684, 2022.
Article in English | MEDLINE | ID: covidwho-1954931

ABSTRACT

BACKGROUND: Characterizing the multiorgan manifestations and outcomes of patients hospitalized with COVID-19 will inform resource requirements to address the long-term burden of this disease. We conducted a descriptive analysis using prospectively collected data to describe the clinical characteristics and spectrum of organ dysfunction, and in-hospital and longer-term clinical outcomes of patients hospitalized with COVID-19 during the first wave of the pandemic at a Canadian centre. METHODS: We conducted a prospective case series involving adult patients (aged ≥ 18 yr) with COVID-19 admitted to 1 of 2 hospitals in London, Ontario, from Mar. 17 to June 18, 2020, during the first wave of the pandemic. We recorded patients' baseline characteristics, physiologic parameters, measures of organ function and therapies administered during hospitalization among patients in the intensive care unit (ICU) and in non-ICU settings, and compared the characteristics of hospital survivors and nonsurvivors. Finally, we recorded follow-up thoracic computed tomography (CT) and echocardiographic findings after hospital discharge. RESULTS: We enrolled 100 consecutive patients (47 women) hospitalized with COVID-19, including 32 patients who received ICU care and 68 who received treatment in non-ICU settings. Respiratory sequelae were common: 23.0% received high-flow oxygen by nasal cannula, 9.0% received noninvasive ventilation, 24.0% received invasive mechanical ventilation and 2.0% received venovenous extracorporeal membrane oxygenation. Overall, 9.0% of patients had cerebrovascular events (3.0% ischemic stroke, 6.0% intracranial hemorrhage), and 6.0% had pulmonary embolism. After discharge, 11 of 19 patients had persistent abnormalities on CT thorax, and 6 of 15 had persistent cardiac dysfunction on echocardiography. INTERPRETATION: This study provides further evidence that COVID-19 is a multisystem disease involving neurologic, cardiac and thrombotic dysfunction, without evidence of hepatic dysfunction. Patients have persistent organ dysfunction after hospital discharge, underscoring the need for research on long-term outcomes of COVID-19 survivors.


Subject(s)
COVID-19 , Adult , COVID-19/complications , COVID-19/epidemiology , COVID-19/therapy , Female , Humans , Multiple Organ Failure/epidemiology , Multiple Organ Failure/etiology , Ontario/epidemiology , Pandemics , SARS-CoV-2
2.
Diagnostics (Basel) ; 11(11)2021 Nov 04.
Article in English | MEDLINE | ID: covidwho-1533838

ABSTRACT

Lung ultrasound (LUS) is an accurate thoracic imaging technique distinguished by its handheld size, low-cost, and lack of radiation. User dependence and poor access to training have limited the impact and dissemination of LUS outside of acute care hospital environments. Automated interpretation of LUS using deep learning can overcome these barriers by increasing accuracy while allowing point-of-care use by non-experts. In this multicenter study, we seek to automate the clinically vital distinction between A line (normal parenchyma) and B line (abnormal parenchyma) on LUS by training a customized neural network using 272,891 labelled LUS images. After external validation on 23,393 frames, pragmatic clinical application at the clip level was performed on 1162 videos. The trained classifier demonstrated an area under the receiver operating curve (AUC) of 0.96 (±0.02) through 10-fold cross-validation on local frames and an AUC of 0.93 on the external validation dataset. Clip-level inference yielded sensitivities and specificities of 90% and 92% (local) and 83% and 82% (external), respectively, for detecting the B line pattern. This study demonstrates accurate deep-learning-enabled LUS interpretation between normal and abnormal lung parenchyma on ultrasound frames while rendering diagnostically important sensitivity and specificity at the video clip level.

3.
BMJ Open ; 11(3): e045120, 2021 03 05.
Article in English | MEDLINE | ID: covidwho-1119316

ABSTRACT

OBJECTIVES: Lung ultrasound (LUS) is a portable, low-cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning (DL) techniques to match or exceed human-level, diagnostic specificity among similar appearing, pathological LUS images. DESIGN: A convolutional neural network (CNN) was trained on LUS images with B lines of different aetiologies. CNN diagnostic performance, as validated using a 10% data holdback set, was compared with surveyed LUS-competent physicians. SETTING: Two tertiary Canadian hospitals. PARTICIPANTS: 612 LUS videos (121 381 frames) of B lines from 243 distinct patients with either (1) COVID-19 (COVID), non-COVID acute respiratory distress syndrome (NCOVID) or (3) hydrostatic pulmonary edema (HPE). RESULTS: The trained CNN performance on the independent dataset showed an ability to discriminate between COVID (area under the receiver operating characteristic curve (AUC) 1.0), NCOVID (AUC 0.934) and HPE (AUC 1.0) pathologies. This was significantly better than physician ability (AUCs of 0.697, 0.704, 0.967 for the COVID, NCOVID and HPE classes, respectively), p<0.01. CONCLUSIONS: A DL model can distinguish similar appearing LUS pathology, including COVID-19, that cannot be distinguished by humans. The performance gap between humans and the model suggests that subvisible biomarkers within ultrasound images could exist and multicentre research is merited.


Subject(s)
COVID-19/diagnostic imaging , Deep Learning , Lung/diagnostic imaging , Neural Networks, Computer , Pulmonary Edema/diagnostic imaging , Respiratory Distress Syndrome/diagnostic imaging , Canada , Diagnosis, Differential , Humans
4.
J Am Soc Echocardiogr ; 33(8): 1040-1047, 2020 08.
Article in English | MEDLINE | ID: covidwho-342809

ABSTRACT

BACKGROUND: The COVID-19 pandemic has placed an extraordinary strain on healthcare systems across North America. Defining the optimal approach for managing a critically ill COVID-19 patient is rapidly changing. Goal-directed transesophageal echocardiography (TEE) is frequently used by physicians caring for intubated critically ill patients as a reliable imaging modality that is well suited to answer questions at bedside. METHODS: A multidisciplinary (intensive care, critical care cardiology, and emergency medicine) group of experts in point-of-care echocardiography and TEE from the United States and Canada convened to review the available evidence, share experiences, and produce a consensus statement aiming to provide clinicians with a framework to maximize the safety of patients and healthcare providers when considering focused point-of-care TEE in critically ill patients during the COVID-19 pandemic. RESULTS: Although transthoracic echocardiography can provide the information needed in most patients, there are specific scenarios in which TEE represents the modality of choice. TEE provides acute care clinicians with a goal-directed framework to guide clinical care and represents an ideal modality to evaluate hemodynamic instability during prone ventilation, perform serial evaluations of the lungs, support cardiac arrest resuscitation, and guide veno-venous ECMO cannulation. To aid other clinicians in performing TEE during the COVID-19 pandemic, we describe a set of principles and practical aspects for performing examinations with a focus on the logistics, personnel, and equipment required before, during, and after an examination. CONCLUSIONS: In the right clinical scenario, TEE is a tool that can provide the information needed to deliver the best and safest possible care for the critically ill patients.


Subject(s)
Coronavirus Infections/epidemiology , Critical Care/organization & administration , Cross Infection/prevention & control , Echocardiography, Transesophageal/methods , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Severe Acute Respiratory Syndrome/epidemiology , COVID-19 , Canada/epidemiology , Consensus , Coronavirus Infections/prevention & control , Female , Humans , Infection Control/methods , Male , North America/epidemiology , Pandemics/prevention & control , Patient Positioning , Pneumonia, Viral/prevention & control , Point-of-Care Systems , Risk Assessment , Safety Management
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