Sungkyunkwan University · 医学
Professor Tae Gun Shin's research lab focuses on improving emergency care outcomes through systematic, protocol-driven approaches in critical care settings. The lab investigates resuscitation strategies, including extracorporeal cardiopulmonary resuscitation and early sepsis management, with an emphasis on timely implementation of evidence-based bundles. Research also explores the impact of emergency department crowding on clinical guideline adherence and patient outcomes, particularly in high-acuity conditions like cardiac arrest and septic shock. The lab is committed to optimizing emergency department efficiency and quality of care in resource-constrained environments.
Figures are computed from collected data and may differ slightly.
ED crowding was significantly associated with lower compliance with the entire resuscitation bundle and decreased likelihood of the timely implementation of the bundle elements.
The Surviving Sepsis Campaign guidelines recommend implementing a 6-h resuscitation bundle, which has been associated with reduced mortality of patients presenting with severe sepsis or septic shock. However, this resuscitation bundle has not yet become a widely implemented treatment protocol. It is still unclear what factors are associated with the rate of compliance with the resuscitation bundle. In this study, we evaluated the potential factors associated with implementation and compliance of
Early vitamin C and thiamine administration in patients with septic shock did not improve survival; however, administration could benefit conditions that are more severe, such as hypoalbuminemia or severe organ failure.
This preliminary report revealed a mortality of over 20% in patients with septic shock, which suggests that there are areas for improvement in terms of the quality of initial resuscitation and outcomes of septic shock patients in the ED.
Standardized approaches to syncope evaluation reduced hospital admissions, medical costs and length of stay in the overcrowded emergency department of a tertiary teaching hospital in South Korea.
The aim of this study was to describe the cause of the recent improvement in the outcomes of patients who experienced in-hospital cardiac arrest. We retrospectively analyzed the in-hospital arrest registry of a tertiary care university hospital in Korea between 2005 and 2009. Major changes to the in-hospital resuscitation policies occurred during the study period, which included the requirement of extensive education of basic life support and advanced cardiac life support, the reformation of car
We aimed to compare outcomes of sepsis patients according to their hemodynamic presentation: cryptic shock (CS), cryptic to overt shock (COS), and overt shock (OS). We analyzed the sepsis registry for adult patients who presented to the emergency department (ED) of a tertiary hospital and met the criteria for severe sepsis or septic shock between August 2008 and March 2012. We classified the patients as having CS, COS, or OS. "Cryptic shock" was defined as severe sepsis with a lactate level of 4
The hourly delay of antibiotic therapy was associated with decreased platelet count and increased serum bilirubin concentration in critically ill septic patients during the first three days of ED admission.
Our study suggests that the C-MAC could be useful for training residents in the direct laryngoscopy while ensuring patient safety in the emergency department.
Objective. We conducted this study to evaluate the clinical outcomes of patients with severe sepsis and septic shock who were treated with ketamine for endotra-cheal intubation.Methods. A single-center, retrospective study was carried out to compare the out-comes of patients with severe sepsis and septic shock who received a ketamine or non-ketamine agent for rapid sequence intubation (RSI). We analyzed the sepsis registry for adult patients who presented to the emergency department (ED), met th
This is the first prospective study designed to identify the efficacy of an AI-based 12-lead ECG analysis algorithm for diagnosing AMI in emergency departments across multiple centers. This study may provide insights into the utility of deep learning in detecting AMI on electrocardiograms in emergency departments. Trial registration ClinicalTrials.gov identifier: NCT05435391. Registered on June 28, 2022.
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