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ACR Open Rheumatol ; 4(5): 457-463, 2022 May.
Article in English | MEDLINE | ID: covidwho-1712012


OBJECTIVE: Patients with anti-melanoma-differentiation-associated 5 (anti-MDA5)-positive dermatomyositis (DM) share several striking similarities to patients with SARS-CoV-2. Our objective was to assess the prevalence of anti-angiotensin converting enzyme-2 (ACE2) immunoglobulin M (IgM) antibodies, found in patients with severe SARS-CoV-2, in two independent anti-MDA5-positive DM cohorts. METHODS: Anti-ACE2 IgM antibodies were assayed by enzyme-linked immunosorbent assay (ELISA) in two anti-MDA5-positive DM cohorts: a predominantly outpatient North American cohort (n = 52) and a Japanese cohort enriched for new-onset disease (n = 32). Additionally, 118 patients with SARS-CoV-2 with a spectrum of clinical severity were tested for anti-MDA5 antibodies by ELISA. RESULTS: Five of fifty-two (9.6%) North American patients and five of thirty-two (15%) Japanese patients were positive for anti-ACE2 IgM. In the North American cohort, all five patients with anti-ACE2 IgM antibodies had proximal muscle weakness, had interstitial lung disease, were significantly more likely to receive pulse dose methylprednisolone (80% vs 30%, P = 0.043), and had worse forced vital capacity (median 59% predicted vs 78%, P = 0.056) compared with the anti-ACE2-IgM-negative group. In the Japanese cohort, all five anti-ACE2-IgM-positive patients also required pulse dose methylprednisolone, and three of five (60%) patients died. Japanese patients with anti-ACE2 IgM had significantly worse oxygenation, as defined by a lower partial pressure of oxygen (PaO2)/fraction of inspired oxygen (FiO2) ratio (233 vs 390, P = 0.021), and a higher alveolar-arterial oxygenation gradient (91 vs 23 mm Hg, P = 0.024) than the IgM-negative group. CONCLUSION: We describe anti-ACE2 IgM autoantibodies in two independent cohorts with anti-MDA5-positive DM. These autoantibodies may be biomarkers for severe disease and provide insight into disease pathogenesis.

Eur Radiol ; 32(7): 4446-4456, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1707890


OBJECTIVES: We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU). METHODS: Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models. RESULTS: A higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 (p = 0.039) and an accuracy of 0.732. CONCLUSIONS: The addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU. KEY POINTS: • Deep learning was used to predict mortality in COVID-19 ICU patients. • Serial radiographs and clinical data were used. • The models could inform clinical decision-making and resource allocation.

COVID-19 , Deep Learning , Humans , Intensive Care Units , Radiography , X-Rays
Acad Radiol ; 29 Suppl 5: S76-S81, 2022 05.
Article in English | MEDLINE | ID: covidwho-1620430


RATIONALE AND OBJECTIVES: The coronavirus pandemic upended in-person radiology education and led to a transition to virtual platforms. We needed a new method to monitor lecture attendance, previously relying on a physical badge system. Our goal was to develop and implement a virtual conference attendance system that is user-friendly, automated, useable in any virtual conference environment, and accurate. MATERIALS AND METHODS: We developed a web-based platform to serve as a virtual conference attendance tracking and evaluation platform. Daily, the application synchronizes with our lecture calendar to identify scheduled conferences and generates a unique attendance link for each event. The link is automatically posted in the conference chat and attendees must be logged in by the time it is posted to click the link, prompting single sign-on authentication. We integrated the system with resident schedules to excuse residents when appropriate. Real-time attendance reports are accessible in a user-friendly dashboard with a 5-star lecture review and comment system. We surveyed residents on satisfaction with the application after 1-year of use. RESULTS: Over the 2020-2021 academic year, we registered 376 conferences with 5,040 virtual swipes from 65 users. Once set up, virtual swipes take seconds to perform with minimal disruption to the conference. Average satisfaction for the platform was rated as 4.69 on a scale of 1 to 5. All respondents agreed or strongly agreed that use of the platform should be continued for future years, with 85% strongly agreeing. CONCLUSION: We developed an online platform for radiology conference attendance logging and evaluation, designed for virtual conferences.

COVID-19 , Radiology , Humans , Pandemics , Radiology/education , Surveys and Questionnaires