Characteristics, survival and neurological outcome in out-of-hospital cardiac arrest in young adults in Sweden: A nationwide study.

Aim: The aim of this study was to present a comprehensive overview of out-of-hospital cardiac arrests (OHCA) in young adults. Methods: The data set analyzed included all cases of OHCA from 1990 to 2020 in the age-range 16-49 years in the Swedish Registry of Cardiopulmonary Resuscitation (SRCR). OHCA between 2010 and 2020 were analyzed in more detail. Clinical characteristics, survival, neurological outcomes, and long-time trends in survival were studied. Logistic regression was used to study 30-days survival, neurological outcomes and Utstein determinants of survival. Results: Trends were assessed in 11,180 cases. The annual increase in 30-days survival during 1990-2020 was 5.9% with no decline in neurological function among survivors. Odds ratio (OR) for heart disease as the cause was 0.55 (95% CI 0.44 to 0.67) in 2017-2020 compared to 1990-1993. Corresponding ORs for overdoses and suicide attempts were 1.61 (95% CI 1.23-2.13) and 2.06 (95% CI 1.48-2.94), respectively. Exercise related OHCA was noted in roughly 5%. OR for bystander CPR in 2017-2020 vs 1990-1993 was 3.11 (95% CI 2.57 to 3.78); in 2020 88 % received bystander CPR. EMS response time increased from 6 to 10 minutes. Conclusion: Survival has increased 6% annually, resulting in a three-fold increase over 30 years, with stable neurological outcome. EMS response time increased with 66% but the majority now receive bystander CPR. Cardiac arrest due to overdoses and suicide attempts are increasing.

The impact of the three-level collaboration exercise on collaboration and leadership during scenario-based hospital evacuation exercises using flexible surge capacity concept: a mixed method cross-sectional study.

BACKGROUND: Hospitals play a crucial role in responding to disasters and public health emergencies. However, they are also vulnerable to threats such as fire or flooding and can fail to respond or evacuate adequately due to unpreparedness and lack of evacuation measures. The United Nations Office for Disaster Risk Reduction has emphasised the importance of partnerships and capacity building in disaster response. One effective way to improve and develop disaster response is through exercises that focus on collaboration and leadership. This study aimed to examine the effectiveness of using the 3-level collaboration (3LC) exercise in developing collaboration and leadership in districts in Thailand, using the concept of flexible surge capacity (FSC) and its collaborative tool during a hospital evacuation simulation. METHODS: A mixed-method cross-sectional study was conducted with 40 participants recruited from disaster-response organisations and communities. The data from several scenario-based simulations were collected according to the collaborative elements (Command and control, Safety, Communication, Assessment, Triage, Treatment, Transport), in the disaster response education, "Major Incident Medical Management and Support" using self-evaluation survey pre- and post-exercises, and direct observation. RESULTS: The 3LC exercise effectively facilitated participants to gain a mutual understanding of collaboration, leadership, and individual and organisational flexibility. The exercise also identified gaps in communication and the utilisation of available resources. Additionally, the importance of early community engagement was highlighted to build up a flexible surge capacity during hospital evacuation preparedness. CONCLUSIONS: the 3LC exercise is valuable for improving leadership skills and multiagency collaboration by incorporating the collaborative factors of Flexible Surge Capacity concept in hospital evacuation preparedness.

Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths.

BACKGROUND: Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy. But deaths, unforeseen by emergency physicians at time of the emergency department (ED) visit, might have a weaker association with the ED visit. OBJECTIVES: To develop an ML algorithm that predicts unsurprising deaths within 30 days after ED discharge. METHODS: In this retrospective registry study, we included all ED attendances within the Swedish region of Halland in 2015 and 2016. All registered deaths within 30 days after ED discharge were classified as either "surprising" or "unsurprising" by an adjudicating committee with three senior specialists in emergency medicine. ML algorithms were developed for the death subclasses by using Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM). RESULTS: Of all 30-day deaths (n = 148), 76% (n = 113) were not surprising to the adjudicating committee. The most common diseases were advanced stage cancer, multidisease/frailty, and dementia. By using LR, RF, and SVM, mean area under the receiver operating characteristic curve (ROC-AUC) of unsurprising deaths in the test set were 0.950 (SD 0.008), 0.944 (SD 0.007), and 0.949 (SD 0.007), respectively. For all mortality, the ROC-AUCs for LR, RF, and SVM were 0.924 (SD 0.012), 0.922 (SD 0.009), and 0.931 (SD 0.008). The difference in prediction performance between all and unsurprising death was statistically significant (P < .001) for all three models. CONCLUSION: In patients discharged to home from the ED, three-quarters of all 30-day deaths did not surprise an adjudicating committee with emergency medicine specialists. When only unsurprising deaths were included, ML mortality prediction improved significantly.