Lyme disease is the most common tick-borne disease in Canada and the United States, caused by Borrelia burgdorferi, which affects multiple organ systems. Epidemiology, clinical presentation, and neuroimaging findings are reviewed. What Is Lyme Disease?L yme disease, known as Lyme borreliosis, was first described in 1976 by William E. Mast and William M. Burrows in Old Lyme, Connecticut. 1 It is the most commonly reported vectorborne disease in North America. In the United States, it is caused by Borrelia burgdorferi, a spirochete, closely related clinically to Treponema pallidum (syphilis) and transmitted via the bite of an infected Ixodes tick. 2,3 Lyme disease is predominantly seen in the mid-Atlantic (primarily New England) and upper Midwest regions (Wisconsin, Minnesota, and Great Lakes region) but is also prevalent in parts of the Pacific coast (Oregon and Washington). 4 Transmission of this tick-borne zoonosis requires both infected reservoirs in the small mammalian hosts and vector blacklegged ticks. Large mammals like humans are seldom hosts and are mainly affected by nymphal ticks. 5 The incidence of Lyme disease is approximately 30,000 or 0.5 per 1000 individuals in endemic areas per the Centers for Disease Control and Prevention. 2,6 However, under-reporting and misclassification are the common drawbacks of any surveillance system. The Centers for Disease Control and Prevention report that highly Lyme endemic states may have considerably higher prevalence than that recorded. 7What Are the Clinical Manifestations of Lyme Disease?Lyme disease can be classified in 3 stages: early localized (stage 1), early disseminated (stage 2), and late disseminated (stage 3). In

No abstract

Introduction: Previous studies have demonstrated that obstructive sleep apnea (OSA) and obesity independently increase the risk for development of atrial fibrillation (AF). However, it is unknown whether weight changes in an OSA cohort also increase the risk of AF. Methods: This was a case control study from a single tertiary institution analyzing patients with a confirmed OSA diagnosis from 2013-2020. Patients with missing data on any of the key variables were excluded from these analyses. The covariates included smoking history, hypertension, congestive heart failure, chronic obstructive pulmonary disease, heart failure, and coronary artery disease. Patients’ weight at the time of AF diagnosis by electrocardiogram (ECG) was compared to the weight documented one year earlier. Weight at the time of the ECG closest to the sleep study date was compared to the weight one year prior for the control group. ​​Multivariate logistic regression analysis to examine the association between AF cases (versus controls) and weight percent change greater than 5%. Results: Among the 182 patients included in the analysis, the incidence of AF was 32.4% and the median weight change was -1.32± 11.69 lb (Table 1). About 36% of those diagnosed with AF had weight changes (gain or loss) above 5% compared to 23% in the control group (p = 0.07). The average weight change for those with diagnosed AF compared to individuals without AF was -2.5 ±11.8 lb vs. -0.76 ± 11.6 lb (p=0.19). The change in the adjusted odds of AF diagnosis among those with more than 5% weight gain or loss was 2.27 (95% CI =1.01, 5.09) compared to those with less or no weight change. Conclusions: Among individuals with OSA, those who exhibited weight changes greater than 5% over a one year period have increased odds for developing AF. Further large-scale studies need to be undertaken to understand the link between intentional versus unintentional weight loss.

Introduction: High-quality care of patients with Cardiac Implantable Electronic Devices, involves early identification of device manufacturer, which leads to timely interrogation using specific programmers. Pacemaker ID is a phone application that aims to identify the manufacturer of pacemakers and defibrillators from a chest X-Ray image. The app utilizes machine learning, including autonomous learning software leading the algorithm to improve over time. Our study aims to investigate the accuracy of the Pacemaker ID app at detecting the manufacturer of cardiac implantable electronic devices Methods: A total of 200 consecutive x-rays were collected from a two year period at The George Washington University in Washington DC and Centro Medico Hospital in Honduras. Chest X-Rays with implanted devices were scanned using the app. The device manufacturer was recorded from the initial operative report Results: The dataset included 146 Medtronic, 23 Boston Scientific, 24 St. Jude and 7 Biotronik devices. The app has a weighted average precision of 85.5%, recall of 73% and F1-score of 77.28%. Dataset accuracy 73%. Medtronic had the most precise prediction at 95.5%, while Biotronik only had 15.2% precision. Boston Scientific had the best recall at 87%. Medtronic, the most abundant in the dataset, showed a high recall of 72% and a very high precision of 95.5%. The F1-score, where Medtronic score the highest with 82%. Biotronik has a low F1-score, at 25% due to the low precision of estimates, particularly because 17% of the Medtronic images were wrongly identified as Biotronik Conclusions: The PacemakerID application was found to have a 73 % overall accuracy in identifying devices. It has the potential to expedite the interrogation process in hospital settings and improve the quality of care among patients with cardiac devices. Taking into account the following limitations: device model, camera type and image quality