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As delayed treatment of septic shock can lead to an irreversible health state, timely identification of septic shock holds immense value. While numerous approaches have been proposed to build early warning systems, these approaches primarily focus on predicting the future risk of septic shock, irrespective of its precise onset timing. Such early prediction systems without consideration of timeliness fall short in assisting clinicians in taking proactive measures. To address this limitation, we establish a timely warning system for septic shock with data-task engineering, a novel technique regarding the control of data samples and prediction targets. Leveraging machine learning techniques and the real-world electronic medical records from the MIMIC-IV (Medical Information Mart for Intensive Care) database, our system, TEW3S (Timely Early Warning System for Septic Shock), successfully predicted 94% of all shock events with one true alarm for every four false alarms and a maximum lead time of 8 hours. This approach emphasizes the often-overlooked importance of prediction timeliness and may provide a practical avenue to develop a timely warning system for acute deterioration in hospital settings, ultimately improving patient outcomes.

Abstract Sex differences in the in-hospital management of sepsis exist. Previous studies either included patients with sepsis that was defined using previous definitions of sepsis or evaluated the 3-h bundle therapy. Therefore, this study sought to assess sex differences in 1-h bundle therapy and in-hospital management among patients with sepsis and septic shock, defined according to the Sepsis-3 definitions. This observational study used data from Korean Shock Society (KoSS) registry, a prospective multicenter sepsis registry. Adult patients with sepsis between June 2018 and December 2021 were included in this study. The primary outcome was adherence to 1-h bundle therapy. Propensity score matching (PSM) and multivariable logistic regression analyses were performed. Among 3264 patients with sepsis, 3129 were analyzed. PSM yielded 2380 matched patients (1190 men and 1190 women). After PSM, 1-h bundle therapy was performed less frequently in women than in men (13.0% vs. 19.2%; p

Objectives Electrocardiography (ECG)-based diagnosis by experts cannot maintain uniform quality because individual differences may occur. Previous public databases can be used for clinical studies, but there is no common standard that would allow databases to be combined. For this reason, it is difficult to conduct research that derives results by combining databases. Recent commercial ECG machines offer diagnoses similar to those of a physician. Therefore, the purpose of this study was to construct a standardized ECG database using computerized diagnoses. Methods The constructed database was standardized using Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Observational Medical Outcomes Partnership– common data model (OMOP-CDM), and data were then categorized into 10 groups based on the Minnesota classification. In addition, to extract high-quality waveforms, poor-quality ECGs were removed, and database bias was minimized by extracting at least 2,000 cases for each group. To check database quality, the difference in baseline displacement according to whether poor ECGs were removed was analyzed, and the usefulness of the database was verified with seven classification models using waveforms. Results The standardized KURIAS-ECG database consists of high-quality ECGs from 13,862 patients, with about 20,000 data points, making it possible to obtain more than 2,000 for each Minnesota classification. An artificial intelligence classification model using the data extracted through SNOMED-CT showed an average accuracy of 88.03%. Conclusions The KURIAS-ECG database contains standardized ECG data extracted from various machines. The proposed protocol should promote cardiovascular disease research using big data and artificial intelligence.

Objective We investigated the effects of a quick Sequential Organ Failure Assessment (qSOFA)–negative result (qSOFA score

Abstract Background Nighttime hospital admission is often associated with increased mortality risk in various diseases. This study investigated compliance rates with the Surviving Sepsis Campaign (SSC) 3-h bundle for daytime and nighttime emergency department (ED) admissions and the clinical impact of compliance on mortality in patients with septic shock. Methods We conducted an observational study using data from a prospective, multicenter registry for septic shock provided by the Korean Shock Society from 11 institutions from November 2015 to December 2017. The outcome was the compliance rate with the SSC 3-h bundle according to the time of arrival in the ED. Results A total of 2049 patients were enrolled. Compared with daytime admission, nighttime admission was associated with higher compliance with the administration of antibiotics within 3 h (adjusted odds ratio (adjOR), 1.326; 95% confidence interval (95% CI), 1.088–1.617, p = 0.005) and with the complete SSC bundle (adjOR, 1.368; 95% CI, 1.115–1.678; p = 0.003), likely to result from the increased volume of all patients and sepsis patients admitted during daytime hours. The hazard ratios of the completion of SSC bundle for 28-day mortality and in-hospital mortality were 0.750 (95% CI 0.590–0.952, p = 0.018) and 0.714 (95% CI 0.564–0.904, p = 0.005), respectively. Conclusion Septic shock patients admitted to the ED during the daytime exhibited lower sepsis bundle compliance than those admitted at night. Both the higher number of admitted patients and the higher patients to medical staff ratio during daytime may be factors that are responsible for lowering the compliance.

Overcrowding in emergency departments (EDs) has long been a problem worldwide and has serious consequences for patient satisfaction and safety. Typically, overcrowding is caused by delays in the boarding time of ED patients waiting for inpatient beds. If the hospitalization of patients is predicted early enough in EDs, inpatient beds can be prepared in advance and the boarding time can be reduced. We design machine learning-based hospitalization predictive models using data on 27,747 patients and compare the experimental results. Five predictive models are designed: 1) logistic regression, 2) XGBoost, 3) NGBoost, 4) support vector machine, and 5) decision tree models. Based on the predictive results, we estimate the quantitative effects of hospitalization predictions on EDs and wards. Using the data from the ED of a general hospital in South Korea, our experiments show that the ED length of stay of a patient can be reduced by 12.3 minutes on average and the ED can reduce the total length of stay by 340,147 minutes for a year.

Abstract Background A favorable neurological outcome is closely related to patient characteristics and total cardiopulmonary resuscitation (CPR) duration. The total CPR duration consists of pre-hospital and in-hospital durations. To date, consensus is lacking on the optimal total CPR duration. Therefore, this study aimed to determine the upper limit of total CPR duration, the optimal cut-off time at the pre-hospital level, and the time to switch from conventional CPR to alternative CPR such as extracorporeal CPR. Methods We conducted a retrospective observational study using prospective, multi-center registry of out-of-hospital cardiac arrest (OHCA) patients between October 2015 and June 2019. Emergency medical service–assessed adult patients (aged ≥ 18 years) with non-traumatic OHCA were included. The primary endpoint was a favorable neurological outcome at hospital discharge. Results Among 7914 patients with OHCA, 577 had favorable neurological outcomes. The optimal cut-off for pre-hospital CPR duration in patients with OHCA was 12 min regardless of the initial rhythm. The optimal cut-offs for total CPR duration that transitioned from conventional CPR to an alternative CPR method were 25 and 21 min in patients with initial shockable and non-shockable rhythms, respectively. In the two groups, the upper limits of total CPR duration for achieving a probability of favorable neurological outcomes 99% were 43–53 and 45–71 min, respectively. Conclusions Herein, we identified the optimal cut-off time for transitioning from pre-hospital to in-hospital settings and from conventional CPR to alternative resuscitation. Although there is an upper limit of CPR duration, favorable neurological outcomes can be expected according to each patient’s resuscitation-related factors, despite prolonged CPR duration.

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.

Abstract Background In-hospital mortality and short-term mortality are indicators that are commonly used to evaluate the outcome of emergency department (ED) treatment. Although several scoring systems and machine learning-based approaches have been suggested to grade the severity of the condition of ED patients, methods for comparing severity-adjusted mortality in general ED patients between different systems have yet to be developed. The aim of the present study was to develop a scoring system to predict mortality in ED patients using data collected at the initial evaluation and to validate the usefulness of the scoring system for comparing severity-adjusted mortality between institutions with different severity distributions. Methods The study was based on the registry of the National Emergency Department Information System, which is maintained by the National Emergency Medical Center of the Republic of Korea. Data from 2016 were used to construct the prediction model, and data from 2017 were used for validation. Logistic regression was used to build the mortality prediction model. Receiver operating characteristic curves were used to evaluate the performance of the prediction model. We calculated the standardized W statistic and its 95% confidence intervals using the newly developed mortality prediction model. Results The area under the receiver operating characteristic curve of the developed scoring system for the prediction of mortality was 0.883 (95% confidence interval [CI]: 0.882–0.884). The Ws score calculated from the 2016 dataset was 0.000 (95% CI: − 0.021 – 0.021). The Ws score calculated from the 2017 dataset was 0.049 (95% CI: 0.030–0.069). Conclusions The scoring system developed in the present study utilizing the parameters gathered in initial ED evaluations has acceptable performance for the prediction of in-hospital mortality. Standardized W statistics based on this scoring system can be used to compare the performance of an ED with the reference data or with the performance of other institutions.

Objective With trends in population aging an increasing number of older patients are visiting the emergency department (ED). This study aimed to identify the characteristics of ED utilization and risk factors for in-hospital mortality in older patients who visited EDs. Methods This nationwide observational study used National Emergency Department Information System data collected during a 2-year period from January 2016 to December 2017. The characteristics of older patients aged 70 years or older were compared with those of younger patients aged 20 to 69 years. Risk factors associated with in-hospital mortality were analyzed by multivariable logistic regression. Results A total of 6,596,423 younger patients and 1,737,799 older patients were included. In the medical and nonmedical older patient groups, significantly higher proportions of patients were transferred from another hospital, utilized emergency medical services, had Korean Triage and Acuity Scale scores of 1 and 2, required hospitalization, and required intensive care unit admission in the older patient group than in the younger patient group. ED and post-hospitalization mortality rates increased with age; in particular, older medical patients aged 90 or older had an in-hospital mortality rate of 9%. Older age, male sex, transfer from another hospital, emergency medical service utilization, a high Korean Triage and Acuity Scale score, systolic blood pressure 20/min, heart rate >100/min, body temperature

Abstract Background A prediction model of mortality for patients with acute poisoning has to consider both poisoning-related characteristics and patients’ physiological conditions; moreover, it must be applicable to patients of all ages. This study aimed to develop a scoring system for predicting in-hospital mortality of patients with acute poisoning at the emergency department (ED). Methods This was a retrospective analysis of the Injury Surveillance Cohort generated by the Korea Center for Disease Control and Prevention (KCDC) during 2011–2018. We developed the new-Poisoning Mortality Scoring system (new-PMS) to generate a prediction model using the derivation group (2011–2017 KCDC cohort). Points were computed for categories of each variable. The sum of these points was the new-PMS. The validation group (2018 KCDC cohort) was subjected to external temporal validation. The performance of new-PMS in predicting mortality was evaluated using area under the receiver operating characteristic curve (AUROC) for both the groups. Results Of 57,326 poisoning cases, 42,568 were selected. Of these, 34,352 (80.7%) and 8216 (19.3%) were enrolled in the derivation and validation groups, respectively. The new-PMS was the sum of the points for each category of 10 predictors. The possible range of the new-PMS was 0–137 points. Hosmer–Lemeshow goodness-of-fit test showed adequate calibration for the new-PMS with p values of 0.093 and 0.768 in the derivation and validation groups, respectively. AUROCs of the new-PMS were 0.941 (95% CI 0.934–0.949, p

Background. In the Republic of Korea, a trauma care system was not created until 2012, at which point regional trauma centers (RTCs) were established nationwide. In accordance with the national emergency care system and legislation, regional and local emergency medical centers (EMCs) also treat patients presenting with trauma. The aim of the present study was to assess whether treatment in RTCs is truly associated with better patient outcomes than that in EMCs by means of propensity score-matched comparisons and to identify populations that would benefit from treatment in RTCs. Methods. This study analyzed the data of patients with consecutive emergency visits between January 1, 2018, and December 31, 2018, collected in the National Emergency Department Information System registry. Data from RTCs, designated regional EMCs, or local EMCs were included; data from smaller emergency departments were excluded because, in Korea, dedicated RTCs are established only in hospitals with regional or local EMCs. Propensity scores for treatment in RTCs or EMCs were estimated by logistic regression using linear terms. Mortality rates in RTCs and EMCs were compared between the matched samples. Results. The in-hospital mortality rates in the matched cases treated in RTCs and EMCs were 1.4% and 1.6%, respectively. The odds ratio for in-hospital mortality in RTCs over EMCs was 0.984 (95% confidence interval: 0.813–1.191). Among the subgroups evaluated, the subgroup of patients with injuries involving the chest or lower limbs showed a significant difference in the in-hospital mortality rate. Conclusion. There was no significant difference in the overall severity-adjusted mortality rate between patients treated in RTCs and EMCs. Treatment in an RTC might benefit those with injuries involving the chest or lower limbs.

Objective. This study aimed to elucidate whether direct transport of out-of-hospital cardiac arrest (OHCA) patients to higher-level emergency medical centres (EMCs) would result in better survival compared to resuscitation in smaller local emergency departments (EDs) and subsequent transfer. Methods. This study was a retrospective population-based analysis of cases registered in the national database of 2019. This study investigated the immediate results of cardiopulmonary resuscitation for OHCA compared between EMCs and EDs and the results of therapeutic temperature management (TTM) compared between the patients directly transported from the field and those transferred from other hospitals. In-hospital mortality was compared using multivariate logistic regression. Results. From the population dataset, 11,493 OHCA patients were extracted. (8,912 in the EMC group vs. 2,581 in the ED group). Multivariate logistic regression revealed that the odds for ED mortality were lower with treatment in EDs than with treatment in EMCs. (odds ratio 0.712 (95% confidence interval (CI): 0.638–0.796)). From the study dataset, 1,798 patients who received TTM were extracted. (1,164 in the direct visit group vs. 634 in the transferred group). Multivariate regression analysis showed that the odds ratio for overall mortality was 1.411 (95% CI: 0.809–2.446) in the transferred group. (p=0.220). Conclusion. The immediate outcome of OHCA patients who were transported to EDs was not inferior to that of EMCs. Therefore, it would be acceptable to transport OHCA patients to the nearest emergency facilities rather than to the specialized centres in distant areas.

ObjectivesThere do not appear to be many studies which have examined the socio-economic burden and medical factors influencing the mortality and hospital costs incurred by patients with cardiac arrest in South Korea. We analyzed the differences in characteristics, medical factors, mortality, and costs between patients with national health insurance and those on a medical aid program.MethodsWe selected patients (≥20 years old) who experienced their first episode of cardiac arrest from 2004 to 2015 using data from the National Health Insurance Service database. We analyzed demographic characteristics, insurance type, urbanization of residential area, comorbidities, treatments, hospital costs, and mortality within 30 days and one year for each group. A multiple regression analysis was used to identify an association between insurance type and outcomes.ResultsAmong the 487,442 patients with cardiac arrest, the medical aid group (13.3% of the total) had a higher proportion of females, rural residents, and patients treated in low-level hospitals. The patients in the medical aid group also reported a higher rate of non-shockable conditions; a high Charlson Comorbidity Index; and pre-existing comorbidities, such as hypertension, diabetes mellitus, and renal failure with a lower rate of providing a coronary angiography. The national health insurance group reported a lower one-year mortality rate (91.2%), compared to the medical aid group (94%), and a negative association with one-year mortality (Adjusted OR 0.74, 95% CI 0.71-0.76). While there was no significant difference in short-term costs between the two groups, the medical aid group reported lower long-term costs, despite a higher rate of readmission.ConclusionsMedical aid coverage was an associated factor for one-year mortality, and may be the result of an insufficient delivery of long-term services as reflected by the lower long-term costs and higher readmission rates. There were differences of characteristics, comorbidities, medical and hospital factors and treatments in two groups. These differences in medical and hospital factors may display discrepancies by type of insurance in the delivery of services, especially in chronic healthcare services.

Abstract Background Septic shock can be defined both by the presence of hyperlactatemia and need of vasopressors. Lactate levels should be measured after volume resuscitation (as per the Sepsis-3 definition). However, currently, no studies have evaluated patients who have been excluded by the new criteria for septic shock. The aim of this study was to determine the clinical characteristics and prognosis of these patients, based on their lactate levels after initial fluid resuscitation. Methods This observational study was performed using a prospective, multi-center registry of septic shock, with the participation of 10 hospitals in the Korean Shock Society, between October 2015 and February 2017. We compared the 28-day mortality between patients who were excluded from the new definition (defined as lactate level

Objective To evaluate the clinical characteristics, therapeutic interventions, and outcomes of patients with septic shock admitted to the emergency department (ED). Methods This study was a preliminary, descriptive analysis of a prospective, multi-center, observational registry of the EDs of 10 hospitals participating in the Korean Shock Society. Patients aged 19 years or older who had a suspected or confirmed infection and evidence of refractory hypotension or hypoperfusion were included. Results A total of 468 patients were enrolled (median age, 71.3 years; male, 55.1%; refractory hypotension, 82.9%; hyperlactatemia without hypotension, 17.1%). Respiratory infection was the most common source of infection (31.0%). The median Sepsis-related Organ Failure Assessment score was 7.5. The sepsis bundle compliance was 91.2% for lactate measurement, 70.3% for blood culture, 68.4% for antibiotic administration, 80.3% for fluid resuscitation, 97.8% for vasopressor application, 68.0% for central venous pressure measurement, 22.0% for central venous oxygen saturation measurement, and 59.2% for repeated lactate measurement. Among patients who underwent interventions for source control (n=117, 25.1%), 43 (36.8%) received interventions within 12 hours of ED arrival. The in-hospital, 28-day, and 90-day mortality rates were 22.9%, 21.8%, and 27.1%, respectively. The median ED and hospital lengths of stay were 6.8 hours and 12 days, respectively. Conclusion This preliminary report revealed a mortality of over 20% in patients with septic shock, which suggests that there are areas for improvement in terms of the quality of initial resuscitation and outcomes of septic shock patients in the ED.

Abstract Background Extracorporeal cardiopulmonary resuscitation (ECPR) has been considered in selected candidates with potentially reversible causes during a limited period. Candidate selection and the identification of predictable conditions are important factors in determining outcomes during CPR in the emergency department (ED). The objective of this study was to determine the key indicators and develop a prediction model for survival to hospital discharge in patients with sudden cardiac arrest who received ECPR. Methods This retrospective analysis was based on a prospective cohort, which included data on CPR with ECPR-related variables. Patients with sudden cardiac arrest who received ECPR at the ED from May 2006 to June 2016 were included. The primary outcome was survival to discharge. Prognostic indicators and the prediction model were analyzed using logistic regression. Results Out of 111 ECPR patients, there were 18.9% survivors. Survivors showed younger age, shorter CPR duration (p

OBJECTIVE:We analyzed the impact of rapid lactate clearance as an indicator of hemodynamic optimization on the outcome of out-of-hospital cardiac arrest (OHCA) during early post-cardiac arrest care (PCAC). METHODS:This retrospective analysis was based on a prospective cohort. We selected adult patients with OHCA who achieved a survival event between May 2006 and December 2017. Serum lactate levels were measured at 0 and 6 h after a survival event. The lactate clearance rate was calculated as (lactate at 0 h-lactate at 6 h)/lactate at 0 h. The threshold lactate clearance value for predicting survival to discharge was determined by receiver operating characteristic (ROC) curve analysis. Rapid lactate clearance was defined as the lactate clearance above the threshold value or normalization of serum lactate level (