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Background: Emergency department overcrowding negatively impacts critically ill patients and could lead to the occurrence of cardiac arrest. However, the association between emergency department crowding and the occurrence of in-hospital cardiac arrest has not been thoroughly investigated. This study aimed to evaluate the correlation between emergency department occupancy rates and the incidence of in-hospital cardiac arrest.Methods: A single-center, observational, registry-based cohort study was performed including all consecutive adult, non-traumatic in-hospital cardiac arrest patients between January 2014 and June 2017. We used emergency department occupancy rates as a crowding index at the time of presentation of cardiac arrest and at the time of maximum crowding, and the average crowding rate for the duration of emergency department stay for each patient. To calculate incidence rate, we divided the number of arrest cases for each emergency department occupancy period by accumulated time. The primary outcome is the association between the incidence of in-hospital cardiac arrest and emergency department occupancy rates.Results: During the study period, 629 adult, non-traumatic cardiac arrest patients were enrolled in our registry. Among these, 187 patients experienced in-hospital cardiac arrest. Overall survival discharge rate was 24.6%, and 20.3% of patients showed favorable neurologic outcomes at discharge. Emergency department occupancy rates were positively correlated with in-hospital cardiac arrest occurrence. Moreover, maximum emergency department occupancy in the critical zone had the strongest positive correlation with in-hospital cardiac arrest occurrence (Spearman rank correlation ρ = 1.0, P < .01). Meanwhile, occupancy rates were not associated with the ED mortality.Conclusion: Maximum emergency department occupancy was strongly associated with in-hospital cardiac arrest occurrence. Adequate monitoring and managing the maximum occupancy rate would be important to reduce unexpected cardiac arrest. [ABSTRACT FROM AUTHOR]

Background: The effect of renal replacement therapy (RRT) on the outcomes of severe acute kidney injury (AKI) after out-of-hospital cardiac arrest (OHCA) is uncertain. This study aimed to evaluate the association of RRT with 6-month mortality in patients with severe AKI treated with targeted temperature management (TTM) after OHCA.Methods: This was a retrospective analysis of a prospectively collected multicentre observational cohort study that included adult OHCA patients treated with TTM across 22 hospitals in South Korea between October 2015 and December 2018. AKI was diagnosed using the Kidney Disease: Improving Global Outcomes criteria. The primary outcome was 6-month mortality and the secondary outcome was cerebral performance category (CPC) at 6 months. Multivariate Cox regression analysis was performed to define the role of RRT in stage 3 AKI.Results: Among 10,426 patients with OHCA, 1373 were treated with TTM. After excluding those who died within 48 h of return of spontaneous circulation (ROSC) and those with pre-arrest chronic kidney disease, our study cohort comprised 1063 patients. AKI developed in 590 (55.5%) patients and 223 (21.0%) had stage 3 AKI. Among them, 115 (51.6%) were treated with RRT. The most common treatment modality among RRT patients was continuous renal replacement therapy (111 [96.5%]), followed by intermittent haemodialysis (4 [3.5%]). The distributions of CPC (1-5) at 6 months for the non-RRT vs. the RRT group were 3/108 (2.8%) vs. 12/115 (10.4%) for CPC 1, 0/108 (0.0%) vs. 1/115 (0.9%) for CPC 2, 1/108 (0.9%) vs. 3/115 (2.6%) for CPC 3, 6/108 (5.6%) vs. 6/115 (5.2%) for CPC 4, and 98/108 (90.7%) vs. 93/115 (80.9%) for CPC 5, respectively (P = 0.01). The RRT group had significantly lower 6-month mortality than the non-RRT group (93/115 [81%] vs. 98/108 [91%], P = 0.04). Multivariate Cox regression analyses showed that RRT was independently associated with a lower risk of death in patients with stage 3 AKI (hazard ratio, 0.569 [95% confidence interval, 0.377-0.857, P = 0.01]).Conclusion: Dialysis interventions were independently associated with a lower risk of death in patients with stage 3 AKI treated with TTM after OHCA. [ABSTRACT FROM AUTHOR]

Background: Previous scoring models such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) scoring systems do not adequately predict mortality of patients undergoing continuous renal replacement therapy (CRRT) for severe acute kidney injury. Accordingly, the present study applies machine learning algorithms to improve prediction accuracy for this patient subset.Methods: We randomly divided a total of 1571 adult patients who started CRRT for acute kidney injury into training (70%, n = 1094) and test (30%, n = 477) sets. The primary output consisted of the probability of mortality during admission to the intensive care unit (ICU) or hospital. We compared the area under the receiver operating characteristic curves (AUCs) of several machine learning algorithms with that of the APACHE II, SOFA, and the new abbreviated mortality scoring system for acute kidney injury with CRRT (MOSAIC model) results.Results: For the ICU mortality, the random forest model showed the highest AUC (0.784 [0.744-0.825]), and the artificial neural network and extreme gradient boost models demonstrated the next best results (0.776 [0.735-0.818]). The AUC of the random forest model was higher than 0.611 (0.583-0.640), 0.677 (0.651-0.703), and 0.722 (0.677-0.767), as achieved by APACHE II, SOFA, and MOSAIC, respectively. The machine learning models also predicted in-hospital mortality better than APACHE II, SOFA, and MOSAIC.Conclusion: Machine learning algorithms increase the accuracy of mortality prediction for patients undergoing CRRT for acute kidney injury compared with previous scoring models. [ABSTRACT FROM AUTHOR]