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.

Objective: This study was undertaken to assess the appropriateness of transfer of patients from a long-term care hospital to the emergency department (ED). Methods: We conducted a retrospective study in a Wide Regional Emergency Center in Gyeongsangnam-do between January 2019 and December 2019. The patients were divided into groups (direct visit, transferred from other hospitals, and transferred from long-term care hospitals [LTCHs]). The baseline characteristics, Korean Triage and Acuity Scale (KTAS), vital signs, length of stay, ED disposition, cost, clinical outcome, and instances of application of the “Act on decisions on life-sustaining treatment” were collected. Results: A total of 30,142 patients were enrolled during the study period. Twenty-one thousand, nine hundred and sixty-five patients were in the direct visit group, 7,057 patients were transferred from other hospitals, and 1,120 patients were transferred from LTCHs. Hospital admission was higher in cases of transfer from other hospitals and LTCHs (LTCHs, 63.8%; transferred from other hospitals, 64.1%, direct visit, 30.1%; P<0.001). Re-transfer and mortality in the ED were much higher (re-transfer: LTCHs, 11.0%; transferred from other hospitals 3.8%, direct visit 1.9%; P<0.001 and mortality in ED: 2.9%, 0.8%, 1.4%; respectively P<0.001). In the LCTH group after admission, mortality was higher (mortality: 16.2%, 5.4%, 7.1% for LTCH transfers and direct respectively; P<0.001). The implementation rate of the “Act on decisions on life-sustaining treatment”, the well-dying law, was higher in the LTCHs (26.6%, 12.5%, and 11.4% LTCH transfers, and direct respectively; P<0.001). Conclusion In the LTCH group, re-transfer, mortality, and the implementation rate of the “Act on decisions on life-sustaining treatment” were higher than in the other groups.

Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

OBJECTIVE: The current guidelines for the treatment of ST-segment elevation myocardial infarction (STEMI) recommends early reperfusion with a door to balloon (DTB) time of 90 minutes or less in patients undergoing primary percutaneous coronary intervention (PPCI). Therefore, the focus of most studies has been the DTB time. On the other hand, the ischemic time is related to the symptom to balloon (STB) time rather than the DTB time. This study examined the clinical effects of the STB time as well as the social and clinical factors affecting the STB time in STEMI patients. METHODS: This study analyzed 286 patients diagnosed with STEMI from December 2008 to December 2016. The STB time (≤4 hours and>4 hours, ≤12 hours, and >12 hours) in the groups was compared. The mortality and ejection fraction were investigated. In addition, the characteristics of patients and socioeconomic factors affecting STB were analyzed. RESULTS: The SBT time is inversely associated with the ejection fraction (R=−0.126, P=0.033), and the ejection fraction of the ≤12 hours group was higher than that of the >12 hours group (54% vs. 50%, P=0.047). On the other hand, there was no significant difference in mortality between the two groups (3.26% vs. 4.84%, P=0.506). In multivariate analysis, the variable related to SBT was only typical chest pain (adjusted odd ratio, 1.931; 95% confidential interval, 1.014-3.792; P=0.045). CONCLUSION The results of the study support the prognostic value of SBT in STEMI undergoing PPCI. Therefore, efforts should be made to shorten the STB time.

PURPOSE: Inadequate treatment of pain, which has been termed as “oligoanalgesia”, appears to be common phenomenon the emergency department (ED). In order to improve pain recognition and management, a study concerning physician characteristics on pain and pain management is needed. METHODS: This study was based on a survey that targeted emergency medicine doctors from September to November 2015 (the response rate was 7%). Firstly, the survey showed that physicians preferred medicating on five diseases abdominal pain, cancer, simple musculoskeletal disease, trauma, headache in the ED. Secondly, it demonstrated the criteria used to choose the analgesic treatment in accordance with each disease and the level of pain, which is determined using a numerical rating scale (NRS). RESULTS: In the cases of abdominal pain that requires surgery, cancer pain, and multiple trauma, physicians preferred using an opioid as the first medication, while non steroidal anti inflammatory drugs (NSAIDs) are prescribed in most of the other cases. Meperidine was the preferred choice as the opioid. For almost diseases, the NSAIDs are selected in the lower NRS cases over the opioid. Physicians deal with pain of patients who are already diagnosed with specific diseases, such as cancer, while they avoid managing pain from those patients who have not been definitively diagnosed with a specific disease. CONCLUSION: Physicians in the ED prefer the use of NSAIDs as the analgesic treatment, in particular, prescribing meperidine as the preferred opioid. However, it seems that they are hesitant to manage pain without a clear diagnosis.
Abdominal Pain ; Analgesics ; Anti-Inflammatory Agents, Non-Steroidal ; Diagnosis ; Emergencies* ; Emergency Medicine* ; Emergency Service, Hospital ; Headache ; Humans ; Meperidine ; Multiple Trauma ; Musculoskeletal Diseases ; Pain Management