This study was to determine whether prolonged emergency department (ED) length of stay (LOS) is associated with increased risk of in-hospital cardiac arrest (IHCA). A retrospective cohort with a nationwide database of all adult patients who visited the EDs in South Korea between January 2016 and December 2017 was performed. A total of 18,217,034 patients visited an ED during the study period. The median ED LOS was 2.5 h. IHCA occurred in 9,180 patients (0.2%). IHCA was associated with longer ED LOS (4.2 vs. 2.5 h), and higher rates of intensive care unit (ICU) admission (58.6% vs. 4.7%) and in-hospital mortality (35.7% vs. 1.5%). The ED LOS correlated positively with the development of IHCA (Spearman ρ = 0.91; p < 0.01) and was an independent risk factor for IHCA (odds ratio (OR) 1.10; 95% confidence interval (CI), 1.10–1.10). The development of IHCA increased in a stepwise fashion across increasing quartiles of ED LOS, with ORs for the second, third, and fourth relative to the first being 3.35 (95% CI, 3.26–3.44), 3.974 (95% CI, 3.89–4.06), and 4.97 (95% CI, 4.89–5.05), respectively. ED LOS should be reduced to prevent adverse events in patients visiting the ED.
(1) Background: The emergency department provides lifesaving treatment and has become an entry point to hospital admission. The purpose of our study was to describe the characteristics and outcomes of patients who were admitted through the emergency department to the intensive care unit or general ward. (2) Methods: We performed a retrospective, cross-sectional, descriptive analysis using the National Emergency Department Information System, analyzing patient data including disease category, diagnosis, and mortality from 1 January 2016, to 31 December 2018. (3) Results: During the study period, about 13.6% were admitted through the emergency department. Of these, the overall in-hospital mortality was 4.6%. The frequent disease class for the intensive care unit admissions was the cardiovascular system, and the classes for the general ward admissions were as follows: injury and toxicology, digestive system, and respiratory system. Cardiovascular system-related emergencies were the predominant cause of death among patients admitted to the intensive care unit; however, oncologic complications were the leading cause of death in the general ward. (4) Conclusions: Emergency departments are incrementally utilized as the entry point for hospital admission. Health care providers need to understand emergency department admission epidemiology and prepare for managing patients with certain common diagnoses.
Overcrowding of emergency department (ED) has put a strain on national healthcare systems and adversely affected the clinical outcomes of critically ill patients. Early identification of critically ill patients prior to ED visits can help induce optimal patient flow and allocate medical resources effectively. This study aims to develop ML-based models for predicting critical illness in the community, paramedic, and hospital stages using Korean National Emergency Department Information System (NEDIS) data. Random forest and light gradient boosting machine (LightGBM) were applied to develop predictive models. The predictive model performance based on AUROC in community stage, paramedic stage, and hospital stage was estimated to be 0.870 (95% CI: 0.869–0.871), 0.897 (95% CI: 0.896–0.898), and 0.950 (95% CI: 0.949–0.950) in random forest and 0.877 (95% CI: 0.876–0.878), 0.899 (95% CI: 0.898–0.900), and 0.950 (95% CI: 0.950–0.951) in LightGBM, respectively. The ML models showed high performance in predicting critical illness using variables available at each stage, which can be helpful in guiding patients to appropriate hospitals according to their severity of illness. Furthermore, a simulation model can be developed for proper allocation of limited medical resources.
Objective: This study aimed to develop a scoring system for predicting the in-hospital mortality of acute poisoning patients at the emergency department (ED). Methods: This was a retrospective analysis of the Injury Surveillance Cohort generated by the Korea Center for Disease Control and Prevention (KCDC) from 2011–2018. We developed the new-Poisoning Mortality Scoring system (new-PMS) to generate a prediction model using the derivation group (2011–2017 KCDC cohort). Points were computed for each category of each variable. The sum of these points was the new-PMS. The validation group (2018 KCDC cohort) was subjected to external temporal validation. The performance of new-PMS in predicting mortality was evaluated using receiver operating characteristic (ROC) curves for both groups. For simple interpretation in clinical settings, risk groups were categorized as very low, low, intermediate, and high according to the new-PMS; we suggested the mortality curve according to new-PMS. Results: Of 57326 poisoning cases, 42568 were selected. Of these, 34352 (80.7%) and 8216 (19.3%) were enrolled in the derivation and validation groups, respectively. New-PMS was the sum of points for each category of 10 predictors. The range of new-PMS was -20 to 3420 points. The area under the ROC curve of new-PMS was 0.942 (95% CI: 0.934–0.949) and 0.946 (95% CI: 0.930–0.963) for the derivation and validation groups, respectively. The mean predicted mortality and the observed mortalities of the high-risk group (new-PMS ≥1048) were 9.7% (95% CI: 9.3 – 10.0) and 10.0% for the derivation group and 8.4% (95% CI: 7.7 – 9.1) and 7.4% for the validation groups, respectively. Conclusions: New-PMS showed good performance in predicting in-hospital mortality for both groups. As mortality sharply increased with the high risk-group of the new-PMS, early hemodynamic stabilization of acute poisoning patients at the ED may improve their clinical outcomes. New-PMS contributes to clinical decision-making for acute poisoning patients in clinical settings.
Overcrowding in emergency departments (EDs) has long been a problem worldwide and has serious consequences for patient satisfaction and safety. Typically, overcrowding is caused by delays in the boarding time of ED patients waiting for inpatient beds. If the hospitalization of patients is predicted early enough in EDs, inpatient beds can be prepared in advance and the boarding time can be reduced. We design machine learning-based hospitalization predictive models using data on 27,747 patients and compare the experimental results. Five predictive models are designed: 1) logistic regression, 2) XGBoost, 3) NGBoost, 4) support vector machine, and 5) decision tree models. Based on the predictive results, we estimate the quantitative effects of hospitalization predictions on EDs and wards. Using the data from the ED of a general hospital in South Korea, our experiments show that the ED length of stay of a patient can be reduced by 12.3 minutes on average and the ED can reduce the total length of stay by 340,147 minutes for a year.INDEX TERMS Emergency department, machine learning, hospitalization prediction, estimation of quantitative effects.
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