Maximum emergency department overcrowding is correlated with occurrence of unexpected cardiac arrest

Abstract

Background: Emergency department overcrowding negatively impacts critically ill patients and could lead to the occurrence of cardiac arrest. However, the association between emergency department crowding and the occurrence of in-hospital cardiac arrest has not been thoroughly investigated. This study aimed to evaluate the correlation between emergency department occupancy rates and the incidence of in-hospital cardiac arrest. Methods: A single-center, observational, registry-based cohort study was performed including all consecutive adult, non-traumatic in-hospital cardiac arrest patients between January 2014 and June 2017. We used emergency department occupancy rates as a crowding index at the time of presentation of cardiac arrest and at the time of maximum crowding, and the average crowding rate for the duration of emergency department stay for each patient. To calculate incidence rate, we divided the number of arrest cases for each emergency department occupancy period by accumulated time. The primary outcome is the association between the incidence of inhospital cardiac arrest and emergency department occupancy rates. Results: During the study period, 629 adult, non-traumatic cardiac arrest patients were enrolled in our registry. Among these, 187 patients experienced in-hospital cardiac arrest. Overall survival discharge rate was 24.6%, and 20.3% of patients showed favorable neurologic outcomes at discharge. Emergency department occupancy rates were positively correlated with in-hospital cardiac arrest occurrence. Moreover, maximum emergency department occupancy in the critical zone had the strongest positive correlation with in-hospital cardiac arrest occurrence (Spearman rank correlation ρ = 1.0, P < .01). Meanwhile, occupancy rates were not associated with the ED mortality.

Machine learning is the key to diagnose COVID-19: a proof-of-concept study

“…RT-PCR and chest-CT are expensive, require qualified professionals to perform them and it is a real challenge to be able to get efficiently these two examinations in the context of a pandemic. An increasing number of patients awaiting results of these tests can lead to ED overcrowding and increased mortality rates in an epidemic context 41 , 42 . The logistic regression model presented in this study and trained only with clinico-biological variables had an AUC value of 0.754.…”

Development of a machine-learning algorithm to predict in-hospital cardiac arrest for emergency department patients using a nationwide database

“…In addition, the quality of care in the ED is adversely affected by crowding and overwhelmed medical staff. Previous studies have also reported that ED crowding increases the incidence of IHCA 10 , 15 . Therefore, the patient’s clinical features and hospital conditions should be considered to predict IHCA occurrence.…”

“…However, we expect that our predictive model has high sensitivity and can be used for the purpose of screening patients with the potential to develop IHCA. IHCA in the ED is sudden and unpredictable, but considered to be preventable if early identification of at-risk patients and adequate interventions are possible 15 , 31 . Therefore, a predictive model for screening patients at the emergency scene and transporting them to the optimal hospital will help reduce the incidence of IHCA and increase the survival of patients.…”

Effect of a Boarding Restriction Protocol on Emergency Department Crowding

“… 25 Although there is no universally accepted tool to measure ED crowding, ED occupancy rate is one reliable method that has been used in many previous studies. 26 27 To obtain ED occupancy rate, we reconstructed the dataset of the number of ED occupying patients at 10-minute intervals from the time of arrival and departure of each patient. A total of 9072 ED occupancy rate values were generated for the pre- and post-period, and the difference in crowding between the two periods was examined.…”