Legionella Myocarditis Masquerading as Dual Chamber Cardiac Arrhythmia

Background Legionella pneumonia is a rare cause of myocarditis. Case 64-y.o male with CAD and PCI to LAD, DM and HTN presented to ER with mental status changes. On exam he was febrile and hypoxic.Presenting rhythm was Afib with frequent bouts of sustained and non-sustained stable posteroseptal VT treated with amiodarone and mexilitene. With right lung infiltrate on CXR and elevated WBC count, antibiotics were initiated for pneumonia. SARS COV-2 Ag and Influenza A & B was negative. Urine Ag for legionella was positive and was promptly treated with Levofloxacin. Coronary angiogram prior to discharge showed non-obstructive CAD. Decision-making Legionnaires' disease with myocarditis was suspected. Patient underwent CMR with late gadolinium enhancement (LGE) and Rest 82Rb perfusion and 18F-FDG PET/CT with high-fat dietary preparation scan for evaluation of legionella myocarditis. CMR revealed LVEF of 46%, with LGE and PET findings as described in the Figure. He was initiated on solumedrol for ongoing inflammation after completion of antibiotic therapy for Legionella pneumonia. Conclusion Our case highlights a systematic approach to differential diagnosis and use of multimodality imaging in legionella myocarditis presenting with dual chamber arrhythmia. There was good correlation between LGE inflammation/scar location and origin of VT, as well as active inflammation demonstrated by FDG PET imaging. The patient was successfully treated with antibiotics, steroids and anti-arrhythmic drugs. [Formula presented]Copyright © 2023 American College of Cardiology Foundation

Are Mobile Cardiac Outpatient Monitors Reliable to Monitor Qtc in Comparison to 12 Lead ECG

Introduction: Use of mobile cardiac outpatient monitor (MCOT) increased during the COVID-19 pandemic as a substitute for telemetry and monitoring of arrythmias during loading of antiarrhythmic drugs (AAD). However, data comparing difference of QTc interval between a MCOT, and 12 lead ECG is scare. Hypothesis: To assess the accuracy of mobile cardiac outpatient monitor in comparison to 12 lead ECG for QTc monitoring Methods: We prospectively evaluated 24 patients at our institution who received IV sotalol as single day loading dose for initiation of oral sotalol therapy for atrial fibrillation/atrial flutter (AF/AFL). All patients were discharged 6 hours after the IV loading dose with a MCOT for 3 days. All patients had a 12 lead ECG within 12-18 hours of the baseline line MCOT transmission. Variation in heart rate and QTc was assessed. Result(s): A total of 24 patients were included in the study. The mean age was 65+7.3 years, 80% of patients were men. The mean difference between the QTc interval measured on 12 lead ECG and MCOT was 5.1+ 6 milliseconds [450+33 (EKG) - 445+39 (MCOT)], p=0.92. The mean heart rate difference between the two modalities was also not significant, p=0.726 [ 70.4+19 (EKG) -72+ 11.8 (MCOT), DELTAHR=1.6+7.2 beats per minute]. Conclusion(s): MCOT can be considered as a reliable alternate to 12 lead ECG for monitoring of QTc in patients receiving AAD.

COVID-19 Detection from Chest X-Ray Using Deep Learning and Contrast Enhancement

In the current COVID-19 pandemic, it has become extremely important to detect the affected patients as soon as possible and isolate them in order to break the chain of the spreading virus. Testing in large numbers at laboratories has overwhelmed their resources. Furthermore, the diagnosis report often takes more than a day to be returned. All this adds up to the incapability of our healthcare infrastructure to test all the possibly infected patients. Radiologists across the world have used chest X-rays to detect chest diseases. X-rays being readily available in far less time than RT-PCR reports make them an easy and quick alternative in comparison to current testing methods. However, examining a vast number of X-rays in an already overwhelmed healthcare facility may still lead to delays in determining the presence of the disease. In addition, it would require expertise and profound knowledge about the much recently explored COVID-19 virus in order to make an accurate assessment of the X-rays. In this study, to find solutions to these problems, we have made use of deep learning for the detection of coronavirus. The proposed system uses three different Convolutional Neural Network (CNN) models to detect COVID-19 from pre-processed chest X-ray images with reliable accuracy and hence provide an alternative for people to be aware of being infected rather than wait days for results. © 2021 IEEE.

Monitor Social Distance Using Convolutional Neural Network and Image Transformation

The COVID-19 pandemic has claimed millions of lives worldwide. In these times, the only sure-shot way to stay safe is to avoid social contact and to follow social distancing regulations. These regulations define the minimum distance people should keep from each other so as to avoid the propagation of the Coronavirus. Hence, monitoring the social distance between people becomes a real-world problem so as to ensure safety for everyone. This is especially difficult to do manually in public places. Our proposed system aims to allow an easy and effective way to measure social distance and identify the people at risk using Convolutional Neural Networks and Image Transformation techniques. © 2021 IEEE.