Objective: There is limited data on the outcomes of cardiac arrest occurring in emergency departments (ED). The objective of this study was to identify the factors associated with these outcomes, primarily the survival to hospital discharge and the neurological status at discharge in emergency department cardiac arrest (EDCA) patients. Methods: A retrospective study was conducted in a tertiary hospital. Adult patients aged over 18 years who had suffered an in-hospital cardiac arrest in the ED between July 2018 to June 2021 were included. The primary outcome was the survival to hospital discharge. Descriptive statistics and logistic regression analyses were performed. Results: We identified 157 ED arrests. Among these, 57.9% of the patients died in the emergency room. A total of 24.1% obtained survival discharge. The combined existing illnesses, such as renal insufficiency or malignancy were directly related to the survival of the patients. A cardiac and respiratory cause of arrest increased the probability of survival (P<0.001). The shorter the time spent on cardiopulmonary resuscitation (CPR), the higher the chances of survival (odds ratio of 0.84). The subjects in both the survivor and deceased groups were classified as Korean Triage and Acuity Scale 2 (KTAS 2: emergency) or higher (P=0.719). There was no difference in the ED occupancy, which is an emergency room overcrowding indicator. Conclusion EDCA patients are already in a clinically deteriorated condition. The underlying clinical conditions, the cause of cardiac arrest, the initial rhythm, and the CPR duration time are directly related to the patient’s chances of survival and prognoses. Therefore, it is possible to identify these factors at an early stage and take the appropriate management measures.

Breast cancer is the second leading cancer for Korean women and its incidence rate has been increasing annually. If early diagnosis were implemented with epidemiologic data, the women could easily assess breast cancer risk using internet. National Cancer Institute in the United States has released a Web-based Breast Cancer Risk Assessment Tool based on Gail model. However, it is inapplicable directly to Korean women since breast cancer risk is dependent on race. Also, it shows low accuracy (58%-59%). In this study, breast cancer discrimination models for Korean women are developed using only epidemiological case-control data (n = 4,574). The models are configured by different classification techniques: support vector machine, artificial neural network, and Bayesian network. A 1,000-time repeated random sub-sampling validation is performed for diverse parameter conditions, respectively. The performance is evaluated and compared as an area under the receiver operating characteristic curve (AUC). According to age group and classification techniques, AUC, accuracy, sensitivity, specificity, and calculation time of all models were calculated and compared. Although the support vector machine took the longest calculation time, the highest classification performance has been achieved in the case of women older than 50 yr (AUC = 64%). The proposed model is dependent on demographic characteristics, reproductive factors, and lifestyle habits without using any clinical or genetic test. It is expected that the model could be implemented as a web-based discrimination tool for breast cancer. This tool can encourage potential breast cancer prone women to go the hospital for diagnostic tests.
Adult ; Aged ; Aged, 80 and over ; Breast Neoplasms/*diagnosis/*epidemiology ; Diagnosis, Computer-Assisted/*methods ; Early Detection of Cancer/*methods ; Female ; Humans ; *Machine Learning ; Middle Aged ; Pattern Recognition, Automated/methods ; Prevalence ; Reproducibility of Results ; Republic of Korea/epidemiology ; Risk Assessment/methods ; Risk Factors ; Sensitivity and Specificity ; Women's Health/*statistics & numerical data