Objective To present a large registry data assessing the association between myocarditis and mortality in patients with SARS-CoV-2 infection. Patients and Methods The researchers identified adult patients aged 18-90 years with COVID-19 diagnosis in the TriNetX (COVID-19 research network) database between January 20, 2020, and December 9, 2020. These patients were then divided into those who had a positive myocarditis diagnosis and those who did not. The researchers compared all-cause mortality between propensity-matched (PSM) pairs of patients in both groups. Results Total of 259,352 patients with COVID-19 diagnosis were included in the study. Of those patients, 383 (0.01%) had myocarditis diagnosis, while 258,969 (99.9%) did not have myocarditis diagnosis during their hospital stay. Patients were more male in the myocarditis group (58.75% vs. 44.73%, P < 0.001). As to the PSM cohorts, 383/383 were matched, and the all-cause mortality was 13.4 % vs. 4.2% ( P < 0.001) at 30 days. A Kaplan-Meier survival analysis was also statistically significant ( P < 0.001) at 30 days. Conclusion In a large multinational database of COVID-19 patients, we observed an association between myocarditis diagnosis and increased mortality. Further prospective studies are recommended to further assess myocarditis outcomes in COVID-19 patients and treatment options.

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Abiotrophia defectiva, a nutritionally deficient streptococcus, is a rare cause of infective endocarditis. It has been associated with hemophagocytic syndrome. We present the first case of A. defectiva infective endocarditis that led to antineutrophil cytoplasmic antibody-associated glomerulonephritis. The patient was a 55-year-old man whose endocarditis affected the mitral and aortic valves. His course was complicated by atrial fibrillation, stroke, and glomerulonephritis. He was successfully treated with antibiotics and dual valve replacement.

Background Diagnosis of shockable rhythms leading to defibrillation remains integral to improving out‐of‐hospital cardiac arrest outcomes. New machine learning techniques have emerged to diagnose arrhythmias on ECGs. In out‐of‐hospital cardiac arrest, an algorithm within an automated external defibrillator is the major determinant to deliver defibrillation. This study developed and validated the performance of a convolution neural network (CNN) to diagnose shockable arrhythmias within a novel, miniaturized automated external defibrillator. Methods and Results There were 26 464 single‐lead ECGs that comprised the study data set. ECGs of 7‐s duration were retrospectively adjudicated by 3 physician readers (N=18 total readers). After exclusions (N=1582), ECGs were divided into training (N=23 156), validation (N=721), and test data sets (N=1005). CNN performance to diagnose shockable and nonshockable rhythms was reported with area under the receiver operating characteristic curve analysis, F1, and sensitivity and specificity calculations. The duration for the CNN to output was reported with the algorithm running within the automated external defibrillator. Internal and external validation analyses included CNN performance among arrhythmias, often mistaken for shockable rhythms, and performance among ECGs modified with noise to mimic artifacts. The CNN algorithm achieved an area under the receiver operating characteristic curve of 0.995 (95% CI, 0.990–1.0), sensitivity of 98%, and specificity of 100% to diagnose shockable rhythms. The F1 scores were 0.990 and 0.995 for shockable and nonshockable rhythms, respectively. After input of a 7‐s ECG, the CNN generated an output in 383±29 ms (total time of 7.383 s). The CNN outperformed adjudicators in classifying atrial arrhythmias as nonshockable (specificity of 99.3%–98.1%) and was robust against noise artifacts (area under the receiver operating characteristic curve range, 0.871–0.999). Conclusions We demonstrate high diagnostic performance of a CNN algorithm for shockable and nonshockable rhythm arrhythmia classifications within a digitally connected automated external defibrillator. Registration URL: https://clinicaltrials.gov/ct2/show/NCT03662802 ; Unique identifier: NCT03662802

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