Comparison of Outpatient Department-Referred and Self-Referred Patients in the Emergency Department.

BACKGROUND: Patients present to emergency departments (EDs) from a variety of backgrounds, which may help inform decision making. OBJECTIVE: This study investigated the clinical characteristics and outcomes of outpatient department (OPD)-referred patients and self-referred patients in the ED. METHODS: We selected nontrauma ED adult patients from a tertiary teaching hospital in Taiwan between August 1, 2020, and October 31, 2020. The acuity levels were determined by dichotomizing the triage classification scores. After propensity score matching, we compared the hospitalization, mortality, and length of ED stay of OPD-referred and self-referred patients. We categorized the patients into "emergency" or "urgent" subgroups according to their triage information and then analyzed the effects of different severity levels. Statistical significance was set at p < 0.05. RESULTS: A total of 564 OPD-referred and 11,959 self-referred patients were included. After propensity score matching, the OPD-referred patients (n = 564), compared with self-referred patients (n = 564), had a higher admission rate (49.8% vs. 28.9%; p < 0.001; odds ratio [OR] 2.44). Among the emergency subgroup patients, there was no significant difference between OPD-referred patients (n = 131) and self-referred patients (n = 138) regarding the admission rate (p = 0.257) or the mortality rate (p = 0.253). Among the urgent subgroup patients, OPD-referred patients (n = 433), compared with self-referred patients (n = 426), had a significantly higher admission rate (46.0% vs. 20.2%; p < 0.001; OR 3.36), but not mortality rate (2.1% vs. 0.5%; p = 0.064). Regarding the length of ED stay, OPD-referred and self-referred patients had a significant difference only in the "urgent and discharged" subgroup (5.8 vs. 2.3 h; p < 0.001). CONCLUSIONS: OPD-referred ED patients might have more severe and complex conditions and need comprehensive care management.

Predicting Bacteremia among Septic Patients Based on ED Information by Machine Learning Methods: A Comparative Study.

INTRODUCTION: Bacteremia is a common but life-threatening infectious disease. However, a well-defined rule to assess patient risk of bacteremia and the urgency of blood culture is lacking. The aim of this study is to establish a predictive model for bacteremia in septic patients using available big data in the emergency department (ED) through logistic regression and other machine learning (ML) methods. MATERIAL AND METHODS: We conducted a retrospective cohort study at the ED of National Cheng Kung University Hospital in Taiwan from January 2015 to December 2019. ED adults (≥18 years old) with systemic inflammatory response syndrome and receiving blood cultures during the ED stay were included. Models I and II were established based on logistic regression, both of which were derived from support vector machine (SVM) and random forest (RF). Net reclassification index was used to determine which model was superior. RESULTS: During the study period, 437,969 patients visited the study ED, and 40,395 patients were enrolled. Patients diagnosed with bacteremia accounted for 7.7% of the cohort. The area under the receiver operating curve (AUROC) in models I and II was 0.729 (95% CI, 0.718-0.740) and 0.731 (95% CI, 0.721-0.742), with Akaike information criterion (AIC) of 16,840 and 16,803, respectively. The performance of model II was superior to that of model I. The AUROC values of models III and IV in the validation dataset were 0.730 (95% CI, 0.713-0.747) and 0.705 (0.688-0.722), respectively. There is no statistical evidence to support that the performance of the model created with logistic regression is superior to those created by SVM and RF. DISCUSSION: The advantage of the SVM or RF model is that the prediction model is more elastic and not limited to a linear relationship. The advantage of the LR model is that it is easy to explain the influence of the independent variable on the response variable. These models could help medical staff identify high-risk patients and prevent unnecessary antibiotic use. The performance of SVM and RF was not inferior to that of logistic regression. CONCLUSIONS: We established models that provide discrimination in predicting bacteremia among patients with sepsis. The reported results could inspire researchers to adopt ML in their development of prediction algorithms.

Success in early treatment with lipid emulsion for antihypertension drug overdose patient.

Intravenous lipid emulsion (ILE) is typically applied as a rescue therapy after the use of conventional treatments for beta blocker (BBs) or calcium channel blocker (CCB) overdoses. We describe the case of a 72-year-old man who presented to our ED after attempting suicide by antihypertensive drug overdose. His blood pressure dropped upon arrival at the ED, and we consequently administered multitherapy including relatively early ILE to prevent prolonged hypotension. He regained stable hemodynamic status on the third day and was later discharged without major sequelae.