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Background: During cardiopulmonary resuscitation (CPR), end-tidal carbon dioxide (EtCO2) is primarily determined by pulmonary blood flow, thereby reflecting the blood flow generated by CPR. We aimed to develop an EtCO2 trajectory-based prediction model for prognostication at specific time points during CPR in patients with out-of-hospital cardiac arrest (OHCA). Methods: We screened patients receiving CPR between 2015–2021 from a prospectively collected database of a tertiary-care medical center. The primary outcome was survival to hospital discharge. We used group-based trajectory modeling to identify the EtCO2 trajectories. Multivariable logistic regression analysis was used for model development and internally validated using bootstrapping. We assessed performance of the model using the area under the receiver operating characteristic curve (AUC). Results: The primary analysis included 542 patients with a median age of 68.0 years. Three distinct EtCO2 trajectories were identified in patients resuscitated for 20 minutes (min): low (average EtCO2 10.0 millimeters of mercury [mm Hg]; intermediate (average EtCO2 26.5 mm Hg); and high (average EtCO2: 51.5 mm Hg). Twenty-min EtCO2 trajectory was fitted as an ordinal variable (low, intermediate, and high) and positively associated with survival (odds ratio 2.25, 95% confidence interval [CI] 1.07–4.74). When the 20-min EtCO2 trajectory was combined with other variables, including arrest location and arrest rhythms, the AUC of the 20-min prediction model for survival was 0.89 (95% CI 0.86–0.92). All predictors in the 20-min model remained statistically significant after bootstrapping. Conclusion: Time-specific EtCO2 trajectory was a significant predictor of OHCA outcomes, which could be combined with other baseline variables for intra-arrest prognostication. For this purpose, the 20-min survival model achieved excellent discriminative performance in predicting survival to hospital discharge.

Background The aim of this study was to validate and compare the performance of statistical (Utstein‐Based Return of Spontaneous Circulation and Shockable Rhythm–Witness–Age–pH) and machine learning–based (Prehospital Return of Spontaneous Circulation and Swedish Cardiac Arrest Risk Score) models in predicting the outcomes following out‐of‐hospital cardiac arrest and to assess the impact of the COVID‐19 pandemic on the models' performance. Methods and Results This retrospective analysis included adult patients with out‐of‐hospital cardiac arrest treated at 3 academic hospitals between 2015 and 2023. The primary outcome was neurological outcomes at hospital discharge. Patients were divided into pre‐ (2015–2019) and post‐2020 (2020–2023) subgroups to examine the effect of the COVID‐19 pandemic on out‐of‐hospital cardiac arrest outcome prediction. The models' performance was evaluated using the area under the receiver operating characteristic curve and compared by the DeLong test. The analysis included 2161 patients, 1241 (57.4%) of whom were resuscitated after 2020. The cohort had a median age of 69.2 years, and 1399 patients (64.7%) were men. Overall, 69 patients (3.2%) had neurologically intact survival. The area under the receiver operating characteristic curves for predicting neurological outcomes were 0.85 (95% CI, 0.83–0.87) for the Utstein‐Based Return of Spontaneous Circulation score, 0.82 (95% CI, 0.81–0.84) for the Shockable Rhythm–Witness–Age–pH score, 0.79 (95% CI, 0.78–0.81) for the Prehospital Return of Spontaneous Circulation score, and 0.79 (95% CI, 0.77–0.81) for the Swedish Cardiac Arrest Risk Score model. The Utstein‐Based Return of Spontaneous Circulation score significantly outperformed both the Prehospital Return of Spontaneous Circulation score (P

Introduction: Timely diagnosis of patients affected by an emerging infectious disease plays a crucial role in treating patients and avoiding disease spread. In prior research, we developed an approach by using machine learning (ML) algorithms to predict serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection based on clinical features of patients visiting an emergency department (ED) during the early coronavirus 2019 (COVID-19) pandemic. In this study, we aimed to externally validate this approach within a distinct ED population. Methods: To create our training/validation cohort (model development) we collected data retrospectively from suspected COVID-19 patients at a US ED from February 23–May 12, 2020. Another dataset was collected as an external validation (testing) cohort from an ED in another country from May 12–June 15, 2021. Clinical features including patient demographics and triage information were used to train and test the models. The primary outcome was the confirmed diagnosis of COVID-19, defined as a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2. We employed three different ML algorithms, including gradient boosting, random forest, and extra trees classifiers, to construct the predictive model. The predictive performances were evaluated with the area under the receiver operating characteristic curve (AUC) in the testing cohort. Results: In total, 580 and 946 ED patients were included in the training and testing cohorts, respectively. Of them, 98 (16.9%) and 180 (19.0%) were diagnosed with COVID-19. All the constructed ML models showed acceptable discrimination, as indicated by the AUC. Among them, random forest (0.785, 95% confidence interval [CI] 0.747–0.822) performed better than gradient boosting (0.774, 95% CI 0.739–0.811) and extra trees classifier (0.72, 95% CI 0.677–0.762). There was no significant difference between the constructed models. Conclusion: Our study validates the use of ML for predicting COVID-19 in the ED and demonstrates its potential for predicting emerging infectious diseases based on models built by clinical features with temporal and spatial heterogeneity. This approach holds promise for scenarios where effective diagnostic tools for an emerging infectious disease may be lacking in the future.

Background: Emergency department cardiac arrest (EDCA) is a global public health challenge associated with high mortality rates and poor neurological outcomes. This study aimed to describe the incidence, risk factors, and causes of EDCA during emergency department (ED) visits in the U.S. Methods: This retrospective cohort study used data from the 2019 Nationwide Emergency Department Sample (NEDS). Adult ED visits with EDCA were identified using the cardiopulmonary resuscitation code. We used descriptive statistics and multivariable logistic regression, considering NEDS’s complex survey design. The primary outcome measure was EDCA incidence. Results: In 2019, there were approximately 232,000 ED visits with cardiac arrest in the U.S. The incidence rate of EDCA was approximately 0.2%. Older age, being male, black race, low median household income, weekend ED visits, having Medicare insurance, and ED visits in non-summer seasons were associated with a higher risk of EDCA. Hispanic race was associated with a lower risk of EDCA. Certain comorbidities (e.g., diabetes and cancer), trauma centers, hospitals with a metropolitan and/or teaching program, and hospitals in the South were associated with a higher risk of EDCA. Depression, dementia, and hypothyroidism were associated with a lower risk of EDCA. Septicemia, acute myocardial infarction, and respiratory failure, followed by drug overdose, were the predominant causes of EDCA. Conclusions: Some patients were disproportionately affected by EDCA. Strategies should be developed to target these modifiable risk factors, specifically factors within ED’s control, to reduce the subsequent disease burden.

Abstract Background Accurate pain assessment is essential in the emergency department (ED) triage process. Overestimation of pain intensity, however, can lead to unnecessary overtriage. The study aimed to investigate the influence of pain on patient outcomes and how pain intensity modulates the triage’s predictive capabilities on these outcomes. Methods A prospective observational cohort study was conducted at a tertiary care hospital, enrolling adult patients in the triage station. The entire triage process was captured on video. Two pain assessment methods were employed: (1) Self-reported pain score in the Taiwan Triage and Acuity Scale, referred to as the system-based method; (2) Five physicians independently assigned triage levels and assessed pain scores from video footage, termed the physician-based method. The primary outcome was hospitalization, and secondary outcomes included ED length of stay (EDLOS) and ED charges. Results Of the 656 patients evaluated, the median self-reported pain score was 4 (interquartile range, 0–7), while the median physician-rated pain score was 1.5 (interquartile range, 0–3). Increased self-reported pain severity was not associated with prolonged EDLOS and increased ED charges, but a positive association was identified with physician-rated pain scores. Using the system-based method, the predictive efficacy of triage scales was lower in the pain groups than in the pain-free group (area under the receiver operating curve, [AUROC]: 0.615 vs. 0.637). However, with the physician-based method, triage scales were more effective in predicting hospitalization among patients with pain than those without (AUROC: 0.650 vs. 0.636). Conclusions Self-reported pain seemed to diminish the predictive accuracy of triage for hospitalization. In contrast, physician-rated pain scores were positively associated with longer EDLOS, increased ED charges, and enhanced triage predictive capability for hospitalization. Pain, therefore, appears to modulate the relationship between triage and patient outcomes, highlighting the need for careful pain evaluation in the ED.

Abstract Objectives Little is known about the recent status of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) in the U.S. emergency department (ED). This study aimed to describe the disease burden (visit and hospitalization rate) of AECOPD in the ED and to investigate factors associated with the disease burden of AECOPD. Methods Data were obtained from the National Hospital Ambulatory Medical Care Survey (NHAMCS), 2010–2018. Adult ED visits (aged 40 years or above) with AECOPD were identified using International Classification of Diseases codes. Analysis used descriptive statistics and multivariable logistic regression accounting for NHAMCS’s complex survey design. Results There were 1,366 adult AECOPD ED visits in the unweighted sample. Over the 9-year study period, there were an estimated 7,508,000 ED visits for AECOPD, and the proportion of AECOPD visits in the entire ED population remained stable at approximately 14 per 1,000 visits. The mean age of these AECOPD visits was 66 years, and 42% were men. Medicare or Medicaid insurance, presentation in non-summer seasons, the Midwest and South regions (vs. Northeast), and arrival by ambulance were independently associated with a higher visit rate of AECOPD, whereas non-Hispanic black or Hispanic race/ethnicity (vs. non-Hispanic white) was associated with a lower visit rate of AECOPD. The proportion of AECOPD visits that were hospitalized decreased from 51% to 2010 to 31% in 2018 (p = 0.002). Arrival by ambulance was independently associated with a higher hospitalization rate, whereas the South and West regions (vs. Northeast) were independently associated with a lower hospitalization rate. The use of antibiotics appeared to be stable over time, but the use of systemic corticosteroids appeared to increase with near statistical significance (p = 0.07). Conclusions The number of ED visits for AECOPD remained high; however, hospitalizations for AECOPD appeared to decrease over time. Some patients were disproportionately affected by AECOPD, and certain patient and ED factors were associated with hospitalizations. The reasons for decreased ED admissions for AECOPD deserve further investigation.

Abstract Little is known about pulmonary embolism (PE) in the United States emergency department (ED). This study aimed to describe the disease burden (visit rate and hospitalization) of PE in the ED and to investigate factors associated with its burden. Data were obtained from the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2010 to 2018. Adult ED visits with PE were identified using the International Classification of Diseases codes. Analyses used descriptive statistics and multivariable logistic regression accounting for the NHAMCS’s complex survey design. Over the 9-year study period, there were an estimated 1,500,000 ED visits for PE, and the proportion of PE visits in the entire ED population increased from 0.1% in 2010–2012 to 0.2% in 2017–2018 (P for trend = 0.002). The mean age was 57 years, and 40% were men. Older age, obesity, history of cancer, and history of venous thromboembolism were independently associated with a higher proportion of PE, whereas the Midwest region was associated with a lower proportion of PE. The utilization of chest computed tomography (CT) scan appeared stable, which was performed in approximately 43% of the visits. About 66% of PE visits were hospitalized, and the trend remained stable. Male sex, arrival during the morning shift, and higher triage levels were independently associated with a higher hospitalization rate, whereas the fall and winter months were independently associated with a lower hospitalization rate. Approximately 8.8% of PE patients were discharged with direct-acting oral anticoagulants. The ED visits for PE continued to increase despite the stable trend in CT use, suggesting a combination of prevalent and incident PE cases in the ED. Hospitalization for PE remains common practice. Some patients are disproportionately affected by PE, and certain patient and hospital factors are associated with hospitalization decisions.

BackgroundPrevious studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods.ObjectiveThis study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19.MethodsData were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity.ResultsThe study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902.ConclusionThis study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.

Abstract Transferring patients between emergency departments (EDs) is a complex but important issue in emergency care regionalization. Social network analysis (SNA) is well-suited to characterize the ED transfer pattern. We aimed to unravel the underlying transfer network structure and to identify key network metrics for monitoring network functions. This was a retrospective cohort study using the National Electronic Referral System (NERS) database in Taiwan. All interhospital ED transfers from 2014 to 2016 were included and transfer characteristics were retrieved. Descriptive statistics and social network analysis were used to analyze the data. There were a total of 218,760 ED transfers during the 3-year study period. In the network analysis, there were a total of 199 EDs with 9516 transfer ties between EDs. The network demonstrated a multiple hub-and-spoke, regionalized pattern, with low global density (0.24), moderate centralization (0.57), and moderately high clustering of EDs (0.63). At the ED level, most transfers were one-way, with low reciprocity (0.21). Sending hospitals had a median of 5 transfer-out partners [interquartile range (IQR) 3–7), while receiving hospitals a median of 2 (IQR 1–6) transfer-in partners. A total of 16 receiving hospitals, all of which were designated base or co-base hospitals, had 15 or more transfer-in partners. Social network analysis of transfer patterns between hospitals confirmed that the network structure largely aligned with the planned regionalized transfer network in Taiwan. Understanding the network metrics helps track the structure and process aspects of regionalized care.

Introduction: Healthcare professionals frequently experience work-related fatigue, which may jeopardize their health and put patient safety at risk. In this study, we applied a machine learning (ML) approach based on data collected from a smartwatch to construct prediction models of work-related fatigue for emergency clinicians. Methods: We conducted this prospective study at the emergency department (ED) of a tertiary teaching hospital from March 10–June 20, 2021, where we recruited physicians, nurses, and nurse practitioners. All participants wore a commercially available smartwatch capable of measuring various physiological data during the experiment. Participants completed the Multidimensional Fatigue Inventory (MFI) web form before and after each of their work shifts. We calculated and labeled the before-and-after-shift score differences between each pair of scores. Using several tree-based algorithms, we constructed the prediction models based on features collected from the smartwatch. Records were split into training/validation and testing sets at a 70:30 ratio, and we evaluated the performances using the area under the curve (AUC) measure of receiver operating characteristic on the test set. Results: In total, 110 participants were included in this study, contributing to a set of 1,542 effective records. Of these records, 85 (5.5%) were labeled as having work-related fatigue when setting the MFI difference between two standard deviations as the threshold. The mean age of the participants was 29.6. Most of the records were collected from nurses (87.7%) and females (77.5%). We selected a union of 31 features to construct the models. For total participants, CatBoost classifier achieved the best performances of AUC (0.838, 95% confidence interval [CI] 0.742–0.918) to identify work-related fatigue. By focusing on a subgroup of nurses

Introduction: The return of spontaneous circulation after cardiac arrest (RACA) score is a well-validated model for estimating the probability of return of spontaneous circulation (ROSC) in patients with out-of-hospital cardiac arrest (OHCA) by incorporating several variables, including gender, age, arrest aetiology, witness status, arrest location, initial cardiac rhythms, bystander cardiopulmonary resuscitation (CPR), and emergency medical services (EMS) arrival time. The RACA score was initially designed for comparisons between different EMS systems by standardising ROSC rates. End-tidal carbon dioxide (EtCO 2) is a quality indicator of CPR. We aimed to improve the performance of the RACA score by adding minimum EtCO 2 measured during CPR to develop the EtCO 2 + RACA score for OHCA patients transported to an emergency department (ED). Methods: This was a retrospective analysis using prospectively collected data for OHCA patients resuscitated at an ED during 2015–2020. Adult patients with advanced airways inserted and available EtCO 2 measurements were included. We used the EtCO 2 values recorded in the ED for analysis. The primary outcome was ROSC. In the derivation cohort, we used multivariable logistic regression to develop the model. In the temporally split validation cohort, we assessed the discriminative performance of the EtCO 2 + RACA score by the area under the receiver operating characteristic curve (AUC) and compared it with the RACA score using the DeLong test. Results: There were 530 and 228 patients in the derivation and validation cohorts, respectively. The median measurements of EtCO 2 were 8.0 times (interquartile range [IQR] 3.0–12.0 times), with the median minimum EtCO 2 of 15.5 millimeters of mercury (mm Hg) (IQR 8.0–26.0 mm Hg). The median RACA score was 36.4% (IQR 28.9–48.0%), and a total of 393 patients (51.8%) achieved ROSC. The EtCO 2 + RACA score was validated with good discriminative performance (AUC, 0.82, 95% CI 0.77–0.88), outperforming the RACA score (AUC, 0.71, 95% CI 0.65–0.78) (DeLong test: P < 0.001). Conclusion: The EtCO 2 + RACA score may facilitate the decision-making process regarding allocations of medical resources in EDs for OHCA resuscitation.

Abstract Early recognition and prevention comprise the first ring of the Chain of Survival for in-hospital cardiac arrest (IHCA). We previously developed and internally validated an emergency department (ED) triage tool, Emergency Department In-hospital Cardiac Arrest Score (EDICAS), for predicting ED-based IHCA. We aimed to externally validate this novel tool in another ED population. This retrospective cohort study used electronic clinical warehouse data from a tertiary medical center with approximately 130,000 ED visits per year. We retrieved data from 268,208 ED visits over a 2-year period. We selected one ED visit per person and excluded out-of-hospital cardiac arrest or children. Patient demographics and computerized triage information were retrieved, and the EDICAS was calculated to predict the ED-based IHCA. A total of 145,557 adult ED patients were included. Of them, 240 (0.16%) developed IHCA. The EDICAS showed excellent discrimination with an area under the receiver operating characteristic (AUROC) of 0.88. The AUROC of the EDICAS outperformed those of other early warning scores (0.80 for Modified Early Warning Score [MEWS] and 0.83 for Rapid Emergency Medicine Score [REMS]) in the same ED population. An EDICAS of 6 or above (i.e., high-risk patients) corresponded to a sensitivity of 33%, a specificity of 97%, and a positive likelihood ratio of 12.2. In conclusion, we externally validated a tool for predicting imminent IHCA in the ED and demonstrated its superior performance over other early warning scores. The real-world impact of the EDICAS warning system with appropriate interventions would require a future prospective study.

Introduction: Although factors related to a return visit to the emergency department (ED) have been reported, only a few studies have examined “high-risk” ED revisits with serious adverse outcomes. In this study we aimed to describe the incidence and trend of high-risk ED revisits in United States EDs and to investigate factors associated with these revisits. Methods: We obtained data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), 2010–2018. Adult ED revisits within 72 hours of a previous discharge were identified using a mark on the patient record form. We defined high-risk revisits as revisits with serious adverse outcomes, including intensive care unit admissions, emergency surgery, cardiac catheterization, or cardiopulmonary resuscitation (CPR) during the return visit. We performed analyses using descriptive statistics and multivariable logistic regression, accounting for NHAMCS’s complex survey design. Results: Over the nine-year study period, there were an estimated 37,700,000 revisits, and the proportion of revisits in the entire ED population decreased slightly from 5.1% in 2010 to 4.5% in 2018 (P for trend = 0.02). By contrast, there were an estimated 827,000 high-risk ED revisits, and the proportion of high-risk revisits in the entire ED population remained stable at approximately 0.1%. The mean age of these high-risk revisit patients was 57 years, and 43% were men. Approximately 6% of the patients were intubated, and 13% received CPR. Most of them were hospitalized, and 2% died in the ED. Multivariable analysis showed that older age (65+ years), Hispanic ethnicity, daytime visits, and arrival by ambulance during the revisit were independent predictors of high-risk revisits. Conclusion: High-risk revisits accounted for a relatively small fraction (0.1%) of ED visits. Over the period of the NHAMCS survey between 2010–2018, this fraction remained stable. We identified factors during the return visit that could be used to label high-risk revisits for timely intervention.

Abstract To investigate whether the effects of sodium bicarbonate (SB) during cardiopulmonary resuscitation (CPR) would be influenced by blood pH and administration timing. Adult patients experiencing in-hospital cardiac arrest (IHCA) from 2006 to 2015 were retrospectively screened. Early intra-arrest blood gas data were obtained within 10 min of CPR. Multivariable logistic regression analysis and generalised additive models were used for effect estimation and data exploration, respectively. A total of 1060 patients were included. Only 59 patients demonstrated favourable neurological status at hospital discharge. Blood pH ≤ 7.18 was inversely associated with favourable neurological outcome (odds ratio [OR], 0.24; 95% confidence interval [CI], 0.11–0.52; p value 7.18; OR 1.56; 95% CI 1.01–2.41; p value = 0.05). SB should not be empirically administered for patients with IHCA since its effects may be influenced by blood pH and administration timing.

Introduction: Research suggests that pain assessment involves a complex interaction between patients and clinicians. We sought to assess the agreement between pain scores reported by the patients themselves and the clinician’s perception of a patient’s pain in the emergency department (ED). In addition, we attempted to identify patient and physician factors that lead to greater discrepancies in pain assessment. Methods: We conducted a prospective observational study in the ED of a tertiary academic medical center. Using a standard protocol, trained research personnel prospectively enrolled adult patients who presented to the ED. The entire triage process was recorded, and triage data were collected. Pain scores were obtained from patients on a numeric rating scale of 0 to 10. Five physician raters provided their perception of pain ratings after reviewing videos. Results: A total of 279 patients were enrolled. The mean age was 53 years. There were 141 (50.5%) female patients. The median self-reported pain score was 4 (interquartile range 0–6). There was a moderately positive correlation between self-reported pain scores and physician ratings of pain (correlation coefficient, 0.46; P

Abstract To investigate whether the optimal time to tracheal intubation (TTI) during cardiopulmonary resuscitation would differ by different blood gas phenotypes. Adult patients experiencing in-hospital cardiac arrest (IHCA) from 2006 to 2015 were retrospectively screened. Early intra-arrest blood gas analysis, performed within 10 min of resuscitation, was used to define different phenotypes. In total, 567 patients were included. Non-severe acidosis (pH≧7.15) was associated with favourable neurological outcome (odds ratio [OR]: 4.60, 95% confidence interval [CI] 1.63–12.95; p value = 0.004) and survival (OR: 3.25, 95% CI 1.72–6.15; p value

Background: After years of setting up public automated external defibrillators (AEDs), the rate of bystander AED use remains low all over the world. This study aimed to assess the public awareness and willingness of bystanders to use AEDs and to investigate the awareness on the Good Samaritan Law (GSL) and the factors associated with the low rate of bystander AED use. Methods: Using stratified random sampling, national telephone interviews were conducted using an author-designed structured questionnaire. The results were weighted to match the census data in Taiwan. The factors associated with public awareness and willingness of bystanders to use AEDs were analysed by logistic regression. Results: Of the 1073 respondents, only 15.2% had the confidence to recognise public AEDs, and 5.3% of them had the confidence to use the AED. Concerns on immature technique and legal issues remain the most common barriers to AED use by bystanders. Moreover, only 30.8% thought that the public should use AEDs at the scene. Few respondents (9.6%) ever heard of the GSL in Taiwan, and less than 3% understood the meaning of GSL. Positive awareness on AEDs was associated with high willingness of bystanders to use AEDs. Respondents who were less likely to use AEDs as bystanders were healthcare personnel and women. Conclusion: The importance of active awareness and the barriers to the use of AEDs among bystanders seemed to have been underestimated in the past years. The relatively low willingness to use AEDs among bystander healthcare providers and women needs further investigation.

Abstract Introduction This study is aimed to investigate the association of intraosseous (IO) versus intravenous (IV) route during cardiopulmonary resuscitation (CPR) with outcomes after out-of-hospital cardiac arrest (OHCA). Methods We systematically searched PubMed, Embase, Cochrane Library and Web of Science from the database inception through April 2020. Our search strings included designed keywords for two concepts, i.e. vascular access and cardiac arrest. There were no limitations implemented in the search strategy. We selected studies comparing IO versus IV access in neurological or survival outcomes after OHCA. Favourable neurological outcome at hospital discharge was pre-specified as the primary outcome. We pooled the effect estimates in random-effects models and quantified the heterogeneity by the I 2 statistics. Time to intervention, defined as time interval from call for emergency medical services to establishing vascular access or administering medications, was hypothesized to be a potential outcome moderator and examined in subgroup analysis with meta-regression. Results Nine retrospective observational studies involving 111,746 adult OHCA patients were included. Most studies were rated as high quality according to Newcastle-Ottawa Scale. The pooled results demonstrated no significant association between types of vascular access and the primary outcome (odds ratio [OR], 0.60; 95% confidence interval [CI], 0.27–1.33; I 2 , 95%). In subgroup analysis, time to intervention was noted to be positively associated with the pooled OR of achieving the primary outcome (OR: 3.95, 95% CI, 1.42–11.02, p: 0.02). That is, when the studies not accounting for the variable of “time to intervention” in the statistical analysis were pooled together, the meta-analytic results between IO access and favourable outcomes would be biased toward inverse association. No obvious publication bias was detected by the funnel plot. Conclusions The meta-analysis revealed no significant association between types of vascular access and neurological outcomes at hospital discharge among OHCA patients. Time to intervention was identified to be an important outcome moderator in this meta-analysis of observation studies. These results call for the need for future clinical trials to investigate the unbiased effect of IO use on OHCA CPR.

BackgroundLittle is known about the in-hospital cardiac arrest (IHCA) in the US emergency department (ED). This study aimed to describe the incidence and mortality of ED-based IHCA visits and to investigate the factors associated with higher incidence and poor outcomes of IHCA.Materials and MethodsData were obtained from the National Hospital Ambulatory Medical Care Survey (NHAMCS) between 2010 and 2018. Adult ED visits with IHCA were identified using the cardiopulmonary resuscitation code, excluding those with out-of-hospital cardiac arrest. We used descriptive statistics and multivariable logistic regression accounting for NHAMCS’s complex survey design. The primary outcome measures were ED-based IHCA incidence rates and ED-based IHCA mortality.ResultsOver the 9-year study period, there were approximately 1,114,000 ED visits with IHCA. The proportion of IHCA visits in the entire ED population (incidence rate, 1.2 per 1,000 ED visits) appeared stable. The mean age of patients who visited the ED with IHCA was 60 years, and 65% were men. Older age, male, arrival by ambulance, and being uninsured independently predicted a higher incidence of ED-based IHCA. Approximately 51% of IHCA died in the ED, and the trend remained stable. Arrival by ambulance, nighttime, or weekend arrival, and being in the non-Northeast were independently associated with a higher mortality rate after IHCA.ConclusionThe high burden of ED visits with IHCA persisted through 2010–2018. Additionally, ED-based IHCA survival to hospital admission remained poor. Some patients were disproportionately affected, and certain contextual factors were associated with a poorer outcome.

Background: Little is known about the trajectories of vital signs prior to in-hospital cardiac arrest (IHCA), which could explain the heterogeneous processes preceding this event. We aimed to identify clinically relevant subphenotypes at high risk of IHCA in the emergency department (ED).Methods: This retrospective cohort study used electronic clinical warehouse data from a tertiary medical center. We retrieved data from 733,398 ED visits over a 7-year period. We selected one ED visit per person and retrieved patient demographics, triage data, vital signs (systolic blood pressure [SBP], heart rate [HR], body temperature, respiratory rate, oxygen saturation), selected laboratory markers, and IHCA status. Group-based trajectory modeling was performed.Results: There were 37,697 adult ED patients with a total of 1,507,121 data points across all vital-sign categories. Three to four trajectory groups per vital-sign category were identified, and the following five trajectory groups were associated with a higher rate of IHCA: low and fluctuating SBP, high and fluctuating HR, persistent hypothermia, recurring tachypnea, and low and fluctuating oxygen saturation. The IHCA-prone trajectory group was associated with a higher triage level and a higher mortality rate, compared to other trajectory groups. Except for the persistent hypothermia group, the other four trajectory groups were more likely to have higher levels of C-reactive protein, lactic acid, cardiac troponin I, and D-dimer. Multivariable analysis revealed that hypothermia (adjusted odds ratio [aOR], 2.20; 95% confidence interval [95%CI], 1.35–3.57) and recurring tachypnea (aOR 2.44; 95%CI, 1.24–4.79) were independently associated with IHCA.Conclusions: We identified five novel vital-sign sub-phenotypes associated with a higher likelihood of IHCA, with distinct patterns in clinical course and laboratory markers. A better understanding of the pre-IHCA vital-sign trajectories may help with the early identification of deteriorating patients.

Introduction: Diverse coronavirus disease 2019 (COVID-19) mortalities have been reported but focused on identifying susceptible patients at risk of more severe disease or death. This study aims to investigate the mortality variations of COVID-19 from different hospital settings during different pandemic phases. Methods: We retrospectively included adult (≥18 years) patients who visited emergency departments (ED) of five hospitals in the state of Texas and who were diagnosed with COVID-19 between March–November 2020. The included hospitals were dichotomized into urban and suburban based on their geographic location. The primary outcome was mortality that occurred either during hospital admission or within 30 days after the index ED visit. We used multivariable logistic regression to investigate the associations between independent variables and outcome. Generalized additive models were employed to explore the mortality variation during different pandemic phases. Results: A total of 1,788 adult patients who tested positive for COVID-19 were included in the study. The median patient age was 54.6 years, and 897 (50%) patients were male. Urban hospitals saw approximately 59.5% of the total patients. A total of 197 patients died after the index ED visit. The analysis indicated visits to the urban hospitals (odds ratio [OR] 2.14, 95% confidence interval [CI], 1.41, 3.23), from March to April (OR 2.04, 95% CI, 1.08, 3.86), and from August to November (OR 2.15, 95% CI, 1.37, 3.38) were positively associated with mortality. Conclusion: Visits to the urban hospitals were associated with a higher risk of mortality in patients with COVID-19 when compared to visits to the suburban hospitals. The mortality risk rebounded and showed significant difference between urban and suburban hospitals since August 2020. Optimal allocation of medical resources may be necessary to bridge this gap in the foreseeable future.

Introduction: Emergency department (ED) revisits are traditionally used to measure potential lapses in emergency care. However, recent studies on in-hospital outcomes following ED revisits have begun to challenge this notion. We aimed to examine inpatient outcomes and resource use among patients who were hospitalized following a return visit to the ED using a national database. Methods: This was a retrospective cohort study using the National Health Insurance Research Database in Taiwan. One-third of ED visits from 2012–2013 were randomly selected and their subsequent hospitalizations included. We analyzed the inpatient outcomes (mortality and intensive care unit [ICU] admission) and resource use (length of stay [LOS] and costs). Comparisons were made between patients who were hospitalized after a return visit to the ED and those who were hospitalized during the index ED visit. Results: Of the 3,019,416 index ED visits, 477,326 patients (16%) were directly admitted to the hospital. Among the 2,504,972 patients who were discharged during the index ED visit, 229,059 (9.1%) returned to the ED within three days. Of them, 37,118 (16%) were hospitalized. In multivariable analyses, the inpatient mortality rates and hospital LOS were similar between the two groups. Compared with the direct-admission group, the return-admission group had a lower ICU admission rate (adjusted odds ratio, 0.78; 95% confidence interval [CI], 0.72–0.84), and lower costs (adjusted difference, −5,198 New Taiwan dollars, 95% CI, −6,224 to −4,172). Conclusion: Patients who were hospitalized after a return visit to the ED had a lower ICU admission rate and lower costs, compared to those who were directly admitted. Our findings suggest that ED revisits do not necessarily translate to poor initial care and that subsequent inpatient outcomes should also be considered for better assessment.

Background: A low bystander cardiopulmonary resuscitation (CPR) rate is one of the factors associated with low cardiac arrest survival. This study aimed to assess knowledge, attitudes, and willingness towards performing CPR and the barriers for implementation of bystander-initiated CPR. Methods: Telephone interviews were conducted using an author-designed and validated structured questionnaire in Taiwan. After obtaining a stratified random sample from the census, the results were weighted to match population data. The factors affecting bystander-initiated CPR were analysed using logistic regression. Results: Of the 1073 respondents, half of them stated that they knew how to perform CPR correctly, although 86.7% indicated a willingness to perform CPR on strangers. The barriers to CPR performance reported by the respondents included fear of legal consequences (44%) and concern about harming patients (36.5%). Most participants expressed a willingness to attend only an hour-long CPR course. Respondents who were less likely to indicate a willingness to perform CPR were female, healthcare providers, those who had no cohabiting family members older than 65 years, those who had a history of a stroke, and those who expressed a negative attitude toward CPR. Conclusion: The expressed willingness to perform bystander CPR was high if the respondents possessed the required skills. Attempts should be made to recruit potential bystanders for CPR courses or education, targeting those respondent subgroups less likely to express willingness to perform CPR. The reason for lower bystander CPR willingness among healthcare providers deserves further investigation. Keywords: Cardiac arrest, Cardiopulmonary resuscitation (CPR), Willingness, Bystander CPR, Survey

Introduction: Within a few months coronavirus disease 2019 (COVID-19) evolved into a pandemic causing millions of cases worldwide, but it remains challenging to diagnose the disease in a timely fashion in the emergency department (ED). In this study we aimed to construct machine-learning (ML) models to predict severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection based on the clinical features of patients visiting an ED during the early COVID-19 pandemic. Methods: We retrospectively collected the data of all patients who received reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 at the ED of Baylor Scott & White All Saints Medical Center, Fort Worth, from February 23–May 12, 2020. The variables collected included patient demographics, ED triage data, clinical symptoms, and past medical history. The primary outcome was the confirmed diagnosis of COVID-19 (or SARS-CoV-2 infection) by a positive RT-PCR test result for SARS-CoV-2, and was used as the label for ML tasks. We used univariate analyses for feature selection, and variables with P

Neuroleptic malignant syndrome (NMS) is a potentially lethal adverse reaction to neuroleptics, which is characterized by hyperthermia, extrapyramidal symptoms, altered consciousness and autonomic dysfunction. Although NMS is most commonly induced by the high-potency neuroleptics, its development has also been associated with the use of non-neuroleptic agents that block central dopamine pathways. A 68-year-old man with generalized anxiety disorder and depressive symptoms presented at the emergency department (ED) with high fever, tremor, muscle rigidity, rhabdomyolysis and altered mental status. NMS was considered to have been caused by the recent addition and subsequent dose increase in his treatment regimen of venlafaxine, a serotonin norepinephrine reuptake inhibitor. He was successfully treated with bromocriptine, lorazepam, and fluid hydration in the ED and intensive care unit.

Tension pneumoperitoneum is a potentially lethal complication of numerous iatrogenic procedures, including upper gastrointestinal (UGI) endoscopy. We report a 69-year-old man with UGI bleeding who developed tension pneumoperitoneum and cardiac arrest after UGI endoscopy. He was successfully resuscitated with needle decompression. Emergency surgery revealed a perforated gastric ulcer, and subtotal gastrectomy with Billroth II anastomosis was performed. Recovery was smooth and he was discharged from the hospital 18 days later. Tension pneumoperitoneum should be suspected in all patients who develop circulatory collapse with acutely distended abdomen after UGI endoscopy. Early identification relies on a high index of suspicion. Prompt treatment with needle decompression should not be delayed for confirmatory radiography once the clinical diagnosis is made.