We attempted to determine the impact of extracorporeal membrane oxygenation (ECMO) on short-term and long-term outcomes and find potential resource utilization differences between the ECMO and non-ECMO groups, using the National Health Insurance Service database. We selected adult patients (≥20 years old) with non-traumatic cardiac arrest from 2007 to 2015. Data on age, sex, insurance status, hospital volume, residential area urbanization, and pre-existing diseases were extracted from the database. A total of 1.5% (n = 3859) of 253,806 patients were categorized into the ECMO group. The ECMO-supported patients were more likely to be younger, men, more covered by national health insurance, and showed, higher usage of tertiary level and large volume hospitals, and a lower rate of pre-existing comorbidities, compared to the non-ECMO group. After propensity score-matching demographic data, hospital factors, and pre-existing diseases, the odds ratio (ORs) of the ECMO group were 0.76 (confidence interval, (CI) 0.68–0.85) for 30-day mortality and 0.66 (CI 0.58–0.79) for 1-year mortality using logistic regression. The index hospitalization was longer, and the 30-day and 1-year hospital costs were greater in the matched ECMO group. Although ECMO support needed longer hospitalization days and higher hospital costs, the ECMO support reduced the risk of 30-day and 1-year mortality compared to the non-ECMO patients.

Background: The early diagnosis and prompt treatment of sepsis can enhance clinical outcomes. This study aimed to assess the relationship between point-of-care testing (POCT) for lactate levels and both adherence to the Surviving Sepsis Campaign (SSC) guidelines and mortality rates among sepsis patients in the emergency department (ED). We hypothesized that bedside lactate POCT would lead to better clinical outcomes. Methods: We conducted a pre–post observational study utilizing data from a prospectively collected sepsis registry. Following the introduction of lactate POCT, lactate levels were determined using both the central laboratory pathway and a POCT device. We then compared the characteristics and clinical outcomes between the periods before and after the introduction of POCT lactate measurement. Results: The analysis included a total of 1191 patients. The introduction of bedside lactate POCT led to a significant reduction in the time taken to obtain lactate results (from 53 to 33 min) and an increase in the rate of subsequent lactate measurements (from 82.1% to 88.2%). Lactate POCT did not significantly affect adherence to the overall SSC guidelines bundle (47.5% vs. 45.0%) or reduce 30-day mortality rates (31.1% vs. 31.4%). However, bedside lactate POCT could decrease extremely delayed lactate measurements. Conclusions: Bedside lactate POCT successfully reduced the time to obtain lactate results. Although lactate POCT did not lead to improved adherence to the overall SSC guidelines bundle or affect short-term mortality rates in sepsis patients, it may have an advantage in a specific situation such as overcrowded ED where there are subsequent or multiple measurements required.

Lee S, et al. J Korean Med Sci. 2021 Jun;36(25):e173. https://doi.org/10.3346/jkms.2021.36.e173

IntroductionThe evaluation of acute poisoning is challenging due to varied toxic substances and clinical presentations. The new-Poisoning Mortality Score was recently developed to assess patients with acute poisoning and showed good performance in predicting in-hospital mortality. The objective of this study is to externally validate the performance of the new-Poisoning Mortality Score and to compare it with the Modified Early Warning Score.MethodsThis retrospective analysis used data from the 2019-2020 Injury Surveillance Cohort, established by the Korea Center for Disease Control and Prevention, to perform external validation of the new-Poisoning Mortality Score. The statistical performances of the new-Poisoning Mortality and Modified Early Warning Scores were assessed and compared in terms of discrimination and calibration. Discrimination analysis involved metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. For calibration analysis, the Hosmer-Lemeshow goodness-of-fit test was utilized and calibration curves for each score were generated to elucidate the relationship between observed and predicted mortalities.ResultsThis study analysed 16,570 patients with acute poisoning. Significant differences were observed between survivors and those who died in-hospital, including age, sex, and vital signs. The new-Poisoning Mortality Score showed better performance over the Modified Early Warning Score in predicting in-hospital mortality, in terms of the area under the receiver operating characteristic curve (0.947 versus 0.800), sensitivity (0.863 versus 0.667), specificity (0.912 versus 0.817), and accuracy (0.911 versus 0.814). When evaluated through calibration curves, the new-Poisoning Mortality Score showed better concordance between predicted and observed mortalities. In subgroup analyses, the score system consistently showed strong performance, excelling particularly in substances with high mortality indices and remaining superior in all substances as a group.ConclusionsOur study has helped to validate the new-Poisoning Mortality Score as an effective tool for predicting in-hospital mortality in patients with acute poisoning in the emergency department. The score system demonstrated superior performance over the Modified Early Warning Score in various metrics. Our findings suggest that the new-Poisoning Mortality Score can contribute to the enhancement of clinical decision-making and patient management.

Background: Despite advancements in emergency medical systems, inter-hospital transfer (IHT) remains a critical component. Several studies have analyzed the impact of IHT on patient outcomes. Some studies have reported positive effects, indicating that transfers can improve patient prognosis. However, other studies have suggested that transfers may worsen outcomes. We investigated whether IHT is associated with in-hospital mortality. Methods: This retrospective observational study utilized data on patient outcomes from the National Emergency Department Information System (NEDIS) from 2016 to 2018, focusing on patients admitted to hospitals after visiting the emergency department (ED). The primary outcome was the in-hospital mortality rate. Results: This study included 2,955,476 adult patients admitted to emergency medical centers, with 832,598 (28.2%) undergoing IHT. The in-hospital mortality rate was significantly higher in the transfer group (6.9%) than in the non-transfer group (4.8%). Multiple logistic regression analysis revealed that IHT was an independent predictor of in-hospital mortality (adjusted odds ratio [aOR] 1.114, 95% confidence interval [CI] 1.101–1.128) after adjusting for variables. Sub-analysis indicated that higher severity scores, shorter symptom onset-to-arrival duration, and diagnoses of infectious or respiratory diseases were significantly associated with increased in-hospital mortality among transferred patients. Conclusions: This study identifies IHT as a significant factor associated with increased in-hospital mortality. Additionally, it suggested the need for policies to mitigate the risks associated with IHT, particularly in critically ill patients, those with the acute phase response, and those with infectious, genitourinary, and respiratory diseases.

Overcrowding of emergency department (ED) has put a strain on national healthcare systems and adversely affected the clinical outcomes of critically ill patients. Early identification of critically ill patients prior to ED visits can help induce optimal patient flow and allocate medical resources effectively. This study aims to develop ML-based models for predicting critical illness in the community, paramedic, and hospital stages using Korean National Emergency Department Information System (NEDIS) data. Random forest and light gradient boosting machine (LightGBM) were applied to develop predictive models. The predictive model performance based on AUROC in community stage, paramedic stage, and hospital stage was estimated to be 0.870 (95% CI: 0.869–0.871), 0.897 (95% CI: 0.896–0.898), and 0.950 (95% CI: 0.949–0.950) in random forest and 0.877 (95% CI: 0.876–0.878), 0.899 (95% CI: 0.898–0.900), and 0.950 (95% CI: 0.950–0.951) in LightGBM, respectively. The ML models showed high performance in predicting critical illness using variables available at each stage, which can be helpful in guiding patients to appropriate hospitals according to their severity of illness. Furthermore, a simulation model can be developed for proper allocation of limited medical resources.