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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.

Abstract Background Rapid recognition of frailty in older patients in the ED is an important first step toward better geriatric care in the ED. We aimed to develop and validate a novel frailty assessment scale at ED triage, the Emergency Department Frailty Scale (ED-FraS). Methods We conducted a prospective cohort study enrolling adult patients aged 65 years or older who visited the ED at an academic medical center. The entire triage process was recorded, and triage data were collected, including the Taiwan Triage and Acuity Scale (TTAS). Five physician raters provided ED-FraS levels after reviewing videos. A modified TTAS (mTTAS) incorporating ED-FraS was also created. The primary outcome was hospital admission following the ED visit, and secondary outcomes included the ED length of stay (EDLOS) and total ED visit charges. Results A total of 256 patients were included. Twenty-seven percent of the patients were frail according to the ED-FraS. The majority of ED-FraS was level 2 (57%), while the majority of TTAS was level 3 (81%). There was a weak agreement between the ED-FraS and TTAS (kappa coefficient of 0.02). The hospital admission rate and charge were highest at ED-FraS level 5 (severely frail), whereas the EDLOS was longest at level 4 (moderately frail). The area under the Receiver Operating Characteristic curve (AUROC) in predicting hospital admission for the TTAS, ED-FraS, and mTTAS were 0.57, 0.62, and 0.63, respectively. The ED-FraS explained more variation in EDLOS (R2 = 0.096) compared with the other two methods. Conclusions The ED-Fras tool is a simple and valid screening tool for identifying frail older adults in the ED. It also can complement and enhance ED triage systems. Further research is needed to test its real-time use at ED triage internationally.

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.

BackgroundTriage is the process of accurately assessing patients’ symptoms and providing them with proper clinical treatment in the emergency department (ED). While many countries have developed their triage process to stratify patients’ clinical severity and thus distribute medical resources, there are still some limitations of the current triage process. Since the triage level is mainly identified by experienced nurses based on a mix of subjective and objective criteria, mis-triage often occurs in the ED. It can not only cause adverse effects on patients, but also impose an undue burden on the health care delivery system. ObjectiveOur study aimed to design a prediction system based on triage information, including demographics, vital signs, and chief complaints. The proposed system can not only handle heterogeneous data, including tabular data and free-text data, but also provide interpretability for better acceptance by the ED staff in the hospital. MethodsIn this study, we proposed a system comprising 3 subsystems, with each of them handling a single task, including triage level prediction, hospitalization prediction, and length of stay prediction. We used a large amount of retrospective data to pretrain the model, and then, we fine-tuned the model on a prospective data set with a golden label. The proposed deep learning framework was built with TabNet and MacBERT (Chinese version of bidirectional encoder representations from transformers [BERT]). ResultsThe performance of our proposed model was evaluated on data collected from the National Taiwan University Hospital (901 patients were included). The model achieved promising results on the collected data set, with accuracy values of 63%, 82%, and 71% for triage level prediction, hospitalization prediction, and length of stay prediction, respectively. ConclusionsOur system improved the prediction of 3 different medical outcomes when compared with other machine learning methods. With the pretrained vital sign encoder and repretrained mask language modeling MacBERT encoder, our multimodality model can provide a deeper insight into the characteristics of electronic health records. Additionally, by providing interpretability, we believe that the proposed system can assist nursing staff and physicians in taking appropriate medical decisions.

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.

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.

Background Emergency department (ED) crowding is a universal issue. In Taiwan, patients with common medical problems prefer to visit ED of medical centers, resulting in overcrowding. Thus, a bed- to-bed transfer program has been implemented since 2014. However, there was few studies that compared clinical outcomes among patients who choose to stay in medical centers to those being transferred to regional hospitals. The aim of this study was to explore the transfer rate, delineate the factors related to patient transfer, and clarify the influence upon the program outcomes. Methods A retrospective cohort study was conducted using demographic and clinical disease factors from the patient electronic referral system, electronic medical records (EMRs) of a medical center in Taipei, and response to referrals from regional hospitals. The study included adult patients who were assessed as appropriate for transfer in 2016. We analyzed the outcomes (length of stay and mortality rate) between the referrals were accepted and refused using propensity score matching. Results Of the 1759 patients eligible for transfer to regional hospitals, 420 patients (24%) accepted the referral. Medical records were obtained from the regional hospitals for 283 patients (67%). After propensity score matching, the results showed that interhospital transfer resulted in similar median total length of stay (8.7 days in the medical center vs 7.9 days in regional hospitals; P = .245). In-hospital mortality was low for both groups (3.1% in the medical center vs 1.3% in regional hospitals; P = .344). Conclusion Transfer from an overcrowded ED in a medical center to regional hospitals in eligible patients results in non-significant outcome of total length of stay. With the caveat of an underpowered sample, we did not find statistically significant differences in in-hospital mortality. This healthcare delivery model may be used in other cities facing similar problems of ED overcrowding.

Abstract Background Post-resuscitation hemodynamic level is associated with outcomes. This study was conducted to investigate if post-resuscitation diastolic blood pressure (DBP) is a favorable prognostic factor. Methods Using TaIwan Network of Targeted Temperature ManagEment for CARDiac Arrest (TIMECARD) registry, we recruited adult patients who received targeted temperature management in nine medical centers between January 2014 and September 2019. After excluding patients with extracorporeal circulation support, 448 patients were analyzed. The first measured, single-point blood pressure after resuscitation was used for analysis. Study endpoints were survival to discharge and discharge with favorable neurologic outcomes (CPC 1–2). Multivariate analysis, area under the receiver operating characteristic curve (AUC), and generalized additive model (GAM) were used for analysis. Results Among the 448 patients, 182 (40.7%) patients survived, and 89 (19.9%) patients had CPC 1–2. In the multivariate analysis, DBP > 70 mmHg was an independent factor for survival (adjusted odds ratio [aOR] 2.16, 95% confidence interval [CI, 1.41–3.31]) and > 80 mmHg was an independent factor for CPC 1–2 (aOR 2.04, 95% CI [1.14–3.66]). GAM confirmed that DBP > 80 mmHg was associated with a higher likelihood of CPC 1–2. In the exploratory analysis, patients with DBP > 80 mmHg had a significantly higher prevalence of cardiogenic cardiac arrest (p = 0.015) and initial shockable rhythm (p = 0.045). Conclusion We found that DBP after resuscitation can predict outcomes, as a higher DBP level correlated with cardiogenic cardiac arrest.

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.

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

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.

BackgroundEmergency department (ED) crowding has resulted in delayed patient treatment and has become a universal health care problem. Although a triage system, such as the 5-level emergency severity index, somewhat improves the process of ED treatment, it still heavily relies on the nurse’s subjective judgment and triages too many patients to emergency severity index level 3 in current practice. Hence, a system that can help clinicians accurately triage a patient’s condition is imperative. ObjectiveThis study aims to develop a deep learning–based triage system using patients’ ED electronic medical records to predict clinical outcomes after ED treatments. MethodsWe conducted a retrospective study using data from an open data set from the National Hospital Ambulatory Medical Care Survey from 2012 to 2016 and data from a local data set from the National Taiwan University Hospital from 2009 to 2015. In this study, we transformed structured data into text form and used convolutional neural networks combined with recurrent neural networks and attention mechanisms to accomplish the classification task. We evaluated our performance using area under the receiver operating characteristic curve (AUROC). ResultsA total of 118,602 patients from the National Hospital Ambulatory Medical Care Survey were included in this study for predicting hospitalization, and the accuracy and AUROC were 0.83 and 0.87, respectively. On the other hand, an external experiment was to use our own data set from the National Taiwan University Hospital that included 745,441 patients, where the accuracy and AUROC were similar, that is, 0.83 and 0.88, respectively. Moreover, to effectively evaluate the prediction quality of our proposed system, we also applied the model to other clinical outcomes, including mortality and admission to the intensive care unit, and the results showed that our proposed method was approximately 3% to 5% higher in accuracy than other conventional methods. ConclusionsOur proposed method achieved better performance than the traditional method, and its implementation is relatively easy, it includes commonly used variables, and it is better suited for real-world clinical settings. It is our future work to validate our novel deep learning–based triage algorithm with prospective clinical trials, and we hope to use it to guide resource allocation in a busy ED once the validation succeeds.

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.

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

Objectives: Early recognition and prevention of in-hospital cardiac arrest (IHCA) play an increasingly important role in the Chain of Survival. However, clinical tools for predicting IHCA in the emergency department (ED) are scanty. We sought to evaluate the role of serum lactate in predicting ED-based IHCA. Methods: Data were retrieved from 733,398 ED visits over a 7-year period in a tertiary medical centre. We selected one ED visit per person and excluded out-of-hospital cardiac arrest, children, or those without lactate measurements. Patient demographics, computerised triage information, and serum lactate levels were extracted. The initial serum lactate levels were grouped into normal (≤2 mmol/L), moderately elevated (2 < lactate ≤ 4), and highly elevated (>4 mmol/L) categories. The primary outcome was ED-based IHCA. Results: A total of 17,392 adult patients were included. Of them, 342 (2%) developed IHCA. About 50% of the lactate levels were normal, 30% were moderately elevated, and 20% were highly elevated. In multivariable analysis, the group with highly elevated lactate had an 18-fold increased risk of IHCA (adjusted odds ratio [OR], 18.0; 95% confidence interval [CI], 11.5–28.2), compared with the normal lactate group. In subgroup analysis, the poor lactate-clearance group (Conclusions: Elevated lactate levels and poor lactate clearance were strongly associated with a higher risk of ED-based IHCA. Clinicians may consider a more liberal sampling of lactate in patients at higher risk of IHCA with follow-up of abnormal levels.

Background/purposeDo-not-resuscitate (DNR) is a legal order that demonstrates a patient's will to avoid further suffering from advanced treatment at the end of life. The concept of palliative care is increasingly accepted, but the impacts of different major illnesses, geographic regions, and health expenses on DNR rates remain unclear.MethodsThis study utilized the two-million National Health Insurance (NHI) Research Database to examine the percentage of DNR rates among all deaths in hospitals from 2001 to 2011. DNR in the study was defined as no resuscitation before death in hospitals. Death records were extracted from the database and correlated with healthcare information. Descriptive statistics were compiled to examine the relationships between DNR rates and variables including major illnesses, geographic regions, and NHI spending.ResultsA total of 126,390 death records were extracted from the database for analysis. Among cancer-related deaths, pancreatic cancer patients had the highest DNR rate (86.99%) and esophageal cancer patients had the lowest DNR rate (71.62%). The higher DNR rate among cancer-only patients (79.53%) decreased with concomitant dialysis (66.07%) or ventilator use (57.85%). The lower DNR rates in patients with either chronic dialysis (51.27%) or ventilator use (59.10%) increased when patients experienced these two conditions concomitantly (61.31%). Although DNR rates have consistently increased over time across all regions of Taiwan, a persistent disparity was noted between the East and the South (76.89% vs. 70.78% in 2011, p < 0.01). After adjusting for potential confounders, DNR patients had significantly lower NHI spending one year prior to death ($67,553), compared with non-DNR patients.ConclusionOur study found that DNR rates varied across cancer types and decreased in cancer patients with concomitant chronic dialysis or ventilator use. Disparities in DNR rates were evident across geographic regions in Taiwan. A wider adoption of the DNR policy may achieve substantial savings in health expenses and improve patients' quality of life.

OBJECTIVES:The objective of this study was to examine the association between obesity and all-cause mortality, length of stay and hospital cost among patients with sepsis 20 years of age or older. MATERIALS AND METHODS:It was a retrospective cohort study. The dataset was the Nationwide Inpatient Sample 2011, the largest publicly available all-payer inpatient care database in the United States. Hospitalizations of sepsis patients 20 years of age or older were included. All 25 primary and secondary diagnosis fields were screened to identify patients with sepsis using International Classification of Diseases, Ninth Revision, Clinical Modification codes. Obesity was the exposure of interest. It was one of the 29 standardized Elixhauser comorbidity measures and readily available in the dataset as a dichotomized variable. The outcome measures were all-cause in-hospital death, length of stay and hospital cost. RESULTS:After weighting, our sample projected to a population size of 1,763,000, providing an approximation for the number of hospital discharges of all sepsis patients 20 years of age or older in the US in 2011. The overall all-cause mortality rate was 14.8%, the median hospital length of stay was 7 days and the median hospital cost was $15,917. After adjustment, the all-cause mortality was lower (adjusted OR = 0.84; 95% CI = 0.81 to 0.88); the average hospital length of stay was longer (adjusted difference = 0.65 day; 95% CI = 0.44 to 0.86) and the hospital cost per stay was higher (adjusted difference = $2,927; 95% CI = $1,606 to $4,247) for obese sepsis patients as compared to non-obese ones. CONCLUSION:With this large and nationally representative sample of over 1,000 hospitals in the US, we found that obesity was significantly associated with a 16% decrease in the odds of dying among hospitalized sepsis patients; however it was also associated with greater duration and cost of hospitalization.

INTRODUCTION: Despite the frequency of emergency department (ED) visits for chronic obstructive pulmonary disease (COPD) exacerbation, little is known about practice variation in EDs.

BACKGROUND: This study aimed to investigate the association between the temporal transitions in heart rhythms during cardiopulmonary resuscitation (CPR) and outcomes after out-of-hospital cardiac arrest. METHODS: This was an analysis of the prospectively collected databases in 3 academic hospitals in northern and central Taiwan. Adult patients with out-of-hospital cardiac arrest transported by emergency medical service between 2015 and 2022 were included. Favorable neurological recovery and survival to hospital discharge were the primary and secondary outcomes, respectively. Time-specific heart rhythm shockability was defined as the probability of shockable rhythms at a particular time point during CPR. The temporal changes in the time-specific heart rhythm shockability were calculated by group-based trajectory modeling. Multivariable logistic regression analyses were performed to examine the association between the trajectory group and outcomes. Subgroup analyses examined the effects of extracorporeal CPR in different trajectories. RESULTS: The study comprised 2118 patients. The median patient age was 69.1 years, and 1376 (65.0%) patients were male. Three distinct trajectories were identified: high-shockability (52 patients; 2.5%), intermediate-shockability (262 patients; 12.4%), and low-shockability (1804 patients; 85.2%) trajectories. The median proportion of shockable rhythms over the course of CPR for the 3 trajectories was 81.7% (interquartile range, 73.2%-100.0%), 26.7% (interquartile range, 16.7%-37.5%), and 0% (interquartile range, 0%-0%), respectively. The multivariable analysis indicated both intermediate- and high-shockability trajectories were associated with favorable neurological recovery (intermediate-shockability: adjusted odds ratio [aOR], 4.98 [95% CI, 2.34-10.59]; high-shockability: aOR, 5.40 [95% CI, 2.03-14.32]) and survival (intermediateshockability: aOR, 2.46 [95% CI, 1.44-4.18]; high-shockability: aOR, 2.76 [95% CI, 1.20-6.38]). The subgroup analysis further indicated extracorporeal CPR was significantly associated with favorable neurological outcomes (aOR, 4.06 [95% CI, 1.11-14.81]) only in the intermediate-shockability trajectory. CONCLUSIONS: Heart rhythm shockability trajectories were associated with out-of-hospital cardiac arrest outcomes, which may be a supplementary factor in guiding the allocation of medical resources, such as extracorporeal CPR. [ABSTRACT FROM AUTHOR]