Benefit and Preference of Propranolol Over Metoprolol in Thyrotoxicosis-Induced Atrial Fibrillation: A Case Report and Review of Literature.

Atrial fibrillation is a common manifestation seen in patients with hyperthyroidism and thyroid storm. The presence of excess thyroid hormone (TH) alters adrenergic receptors in the heart and blood vessels, thereby causing an increase in sympathetic function and atrial fibrillation as a sequela of this excess circulating hormone. Excess thyroid hormone (T3) shortens the action potential of cardiomyocytes in the pulmonary vein, which facilitates the generation of reentrant circuits causing atrial fibrillation. Thyroid hormone can regulate cardiac beta-adrenergic receptor expression leading to enhanced catecholamine sensitivity of beta-adrenergic coupled cardiac response. We present a case of a 64-year-old female with a history of hypertension (HTN), nonobstructive coronary artery disease (CAD), congestive heart failure (CHF) [ejection fraction (EF) 35-40%], chronic obstructive pulmonary disease (COPD) on long-term oxygen therapy (LTO2), obstructive sleep apnea (OSA)/hypoventilation syndrome, atrial flutter/atrial fibrillation with a loop recorder on rivaroxaban, and obesity who presented to the emergency department (ED) with gastroenteritis symptoms precipitating difficulty breathing and atrial fibrillation with a rapid ventricular response (HR 140-150) requiring ICU admission for rate and rhythm control. During the course of hospitalization, she was treated with an amiodarone infusion, which induced thyrotoxicosis and increased the ectopic electrical activity in the atrium, worsening atrial fibrillation. On day 3, amiodarone was stopped, and IV esmolol and metoprolol tartrate PO were continued with no resolution of atrial fibrillation. The patient was transitioned to propranolol, which achieved adequate heart rate control prior to discharge. The aim of our review is to highlight that propranolol should be used over metoprolol in patients with hyperthyroidism-induced atrial fibrillation due to the effect of propranolol on blocking the activity of T4 conversion to active T3 and, as such, blocking its effect on cardiac myocytes, terminating reentrant atrial excitation.

Role of Deep Learning in Computed Tomography.

Computed tomography has played an instrumental role in the understanding of the pathophysiology of atherosclerosis in coronary artery disease. It enables visualization of the plaque obstruction and vessel stenosis in a comprehensive manner. As technology for computed tomography is constantly evolving, coronary applications and possibilities are constantly expanding. This influx of information can overwhelm a physician's ability to interpret information in this era of big data. Machine learning is a revolutionary approach that can help provide limitless pathways in patient management. Within these machine algorithms, deep learning has tremendous potential and can revolutionize computed tomography and cardiovascular imaging. In this review article, we highlight the role of deep learning in various aspects of computed tomography.

A Case Report on Uremic Toxins and Their Effects on Cardiac Rhythm: Understanding Junctional Ventricular Escape Rhythm in Renal Failure.

Understanding the reasons behind junctional ventricular escape rhythm is crucial for guiding the clinical management of patients. Various factors such as acidosis, hyperkalemia, metabolic toxins, digoxin toxicity, and BRASH syndrome (comprising bradycardia, renal failure, atrioventricular (AV) nodal blockade, shock, and hyperkalemia) should be considered when dealing with a symptomatic unstable patient in a hospital. In this case, we present an example where metabolic toxins, specifically uremia, in combination with other factors, lead the patient to enter a ventricular escape rhythm, ultimately resulting in cardiogenic shock. The main objective of this case study is to illustrate how uremic metabolic acidosis contributes to AV nodal blockade, leading to a junctional ventricular escape rhythm within the context of BRASH.

Cardiac arrest in the setting of probable catastrophic antiphospholipid syndrome in young patient with a history of COVID infection and polyglandular disorder-Case report.

Antiphospholipid syndrome is an autoimmune disorder characterized by arterial and venous thrombosis and recurrent spontaneous abortions due to the persistent presence of antiphospholipid antibodies. Probable Catastrophic antiphospholipid (Catastrophic antiphospholipid-like syndrome) is a life-threatening presentation of antiphospholipid syndrome which manifests as intravascular thrombosis, leading to rapid onset of symptoms and involvement of multiple organ systems. We present a case of a 28-year-old woman with a history of polyglandular autoimmune syndrome, systemic lupus erythematosus, provoked bilateral deep vein thrombosis in the setting of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection 2 years prior, and hypothyroidism who presents with a cardiac arrest in the setting of an acute ST-elevation myocardial infarction with thromboembolic occlusion of two coronary arteries simultaneously in the setting of noncompliance with anticoagulation for the past 1 week. Her presentation was further complicated by acute hypoxic respiratory failure due to diffuse alveolar hemorrhage during the hospital course with progressive multiorgan failure and eventual death. Catastrophic antiphospholipid is associated with high morbidity and mortality, thus a timely diagnosis and multidisciplinary approach to management is needed for evaluation and management.

eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19.

We present an interpretable machine learning algorithm called 'eARDS' for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the Berlin clinical criteria. The analysis was conducted on data collected from the Intensive care units (ICU) at Emory Healthcare, Atlanta, GA and University of Tennessee Health Science Center, Memphis, TN and the Cerner® Health Facts Deidentified Database, a multi-site COVID-19 EMR database. The participants in the analysis consisted of adults over 18 years of age. Clinical data from 35,804 patients who developed ARDS and controls were used to generate predictive models that identify risk for ARDS onset up to 12-hours before satisfying the Berlin criteria. We identified salient features from the electronic medical record that predicted respiratory failure among this population. The machine learning algorithm which provided the best performance exhibited AUROC of 0.89 (95% CI = 0.88-0.90), sensitivity of 0.77 (95% CI = 0.75-0.78), specificity 0.85 (95% CI = 085-0.86). Validation performance across two separate health systems (comprising 899 COVID-19 patients) exhibited AUROC of 0.82 (0.81-0.83) and 0.89 (0.87, 0.90). Important features for prediction of ARDS included minimum oxygen saturation (SpO2), standard deviation of the systolic blood pressure (SBP), O2 flow, and maximum respiratory rate over an observational window of 16-hours. Analyzing the performance of the model across various cohorts indicates that the model performed best among a younger age group (18-40) (AUROC = 0.93 [0.92-0.94]), compared to an older age group (80+) (AUROC = 0.81 [0.81-0.82]). The model performance was comparable on both male and female groups, but performed significantly better on the severe ARDS group compared to the mild and moderate groups. The eARDS system demonstrated robust performance for predicting COVID19 patients who developed ARDS at least 12-hours before the Berlin clinical criteria, across two independent health systems.