Cardiology Commons^™

Aorto-Superior Vena Cava Fistula Secondary To Ascending Aortic Dissection, Talal Al-Assil, Aiden Michael Van Loo, Bakri Kaakeh, Bashar Al Jayyousi, Rania Esteitie

Journal of Shock and Hemodynamics

Aortic fistulas are a rare condition that can be fatal if not detected early. They can occur spontaneously or after trauma or vascular procedures. The most common aortic fistulas occur with the esophagus, intestines, atrium, and bronchi. Aorto-superior vena cava (SVC) fistulas are exceedingly rare finding. We present a patient with a dissected aortic pseudoaneurysm complicated by an aorto-SVC fistula, leading to left-to-right shunting. This caused increased pulmonary blood flow, left ventricular volume overload, and eventual high-output heart failure. This is a unique case of an aorto-SVC fistula as a complication of subacute/chronic ascending aortic dissection in a patient who …

Improving Time To Defibrillation​, Jonathan Eyestone, Dana Gilbreth, Amanda Maloney, Tina Pham

Articles, Abstracts, and Reports

"ABSTRACT TITLE: Improving Time to Defibrillation

Background: Providence St. Vincent Medical Center (PSVMC) Medical Surgical units are not meeting the American Heart Association requirement of less than 3 minutes to defibrillation. Time to defibrillation in codes with shockable rhythms averages 4.2 minutes. In Mock Codes, only one met the time less than 3 minutes; four Mock Codes had times over 5 minutes. Resuscitation Quality Improvement CPR (RQI) training is required quarterly. Defibrillator practice with overall Code Blue competency is annual.

Purpose: Combine the hands-on practice of the defibrillator with the hands-on training for RQI.

Methods: The Medical Surgical unit 6 …

Prediction Of Major Adverse Cardiac Events In The Emergency Department Using An Artificial Neural Network With A Systematic Grid Search, Ahmed Raheem Buksh, Nadeem Ullah Khan, Rida Jawed, Shahan Waheed, Musa Karim

Department of Emergency Medicine

Background: The aim of our research was to design and evaluate an Artificial Neural Network (ANN) model using a systemic grid search for the early prediction of major adverse cardiac events (MACE) among patients presenting to the triage of an emergency department.
Methods: This is a single-center, cross-sectional study using electronic health records from January 2017 to December 2020. The research population consists of adults coming to our emergency department triage at Aga Khan University Hospital. The MACE during hospitalization was the main outcome. To enhance the architecture of an ANN using triage data, we used a systematic grid search …