End-tidal Carbon Dioxide Trajectory-based Prognostication of Out-of-hospital Cardiac Arrest.

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.

Prognostic Impact of Heart Rhythm Shockability Trajectory in Out-of-Hospital Cardiac Arrest: A Multicenter Retrospective Study.

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 (intermediate-shockability: 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.

Interpretable Deep Learning System for Identifying Critical Patients Through the Prediction of Triage Level, Hospitalization, and Length of Stay: Prospective Study.

BACKGROUND: Triage 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. OBJECTIVE: Our 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. METHODS: In 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]). RESULTS: The 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. CONCLUSIONS: Our 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.

A novel scale for triage assessment of frailty in the emergency department (ED-FraS): a prospective videotaped study.

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.

Development and External Validation of Clinical Features-based Machine Learning Models for Predicting COVID-19 in the Emergency Department.

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.

The incidence, predictors, and causes of cardiac arrest in United States emergency departments.

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.

Prospective study of pain and patient outcomes in the emergency department: a tale of two pain assessment methods.

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.

Applying a Smartwatch to Predict Work-related Fatigue for Emergency Healthcare Professionals: Machine Learning Method.

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 <35 years in age, XGBoost classifier obtained excellent performance of AUC (0.928, 95% CI 0.839-0.991) on the test set. CONCLUSION: By using features derived from a smartwatch, we successfully built ML models capable of classifying the risk of work-related fatigue in the ED. By collecting more data to optimize the models, it should be possible to use smartwatch-based ML models in the future to predict work-related fatigue and adopt preventive measures for emergency clinicians.

Acute exacerbation of chronic obstructive pulmonary disease in United States emergency departments, 2010-2018.

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.

End-tidal Carbon Dioxide + Return of Spontaneous Circulation After Cardiac Arrest (RACA) Score to Predict Outcomes After Out-of-hospital Cardiac Arrest.

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 (EtCO2) is a quality indicator of CPR. We aimed to improve the performance of the RACA score by adding minimum EtCO2 measured during CPR to develop the EtCO2 + 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 EtCO2 measurements were included. We used the EtCO2 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 EtCO2 + 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 EtCO2 were 8.0 times (interquartile range [IQR] 3.0-12.0 times), with the median minimum EtCO2 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 EtCO2 + 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 EtCO2 + RACA score may facilitate the decision-making process regarding allocations of medical resources in EDs for OHCA resuscitation.

Point-of-care testing for adult out-of-hospital cardiac arrest resuscitated at the ED to predict ROSC: Development and external validation of POC-ED-ROSC model.

BACKGROUND AND IMPORTANCE: Most prediction models, like return of spontaneous circulation (ROSC) after cardiac arrest (RACA) or Utstein-based (UB)-ROSC score, were developed for prehospital settings to predict the probability of ROSC in patients with out-of-hospital cardiac arrest (OHCA). A prediction model has been lacking for the probability of ROSC in patients with OHCA at emergency departments (EDs). OBJECTIVE: In the present study, a point-of-care (POC) testing-based model, POC-ED-ROSC, was developed and validated for predicting ROSC of OHCA at EDs. DESIGN, SETTINGS AND PARTICIPANTS: Prospectively collected data for adult OHCA patients between 2015 and 2020 were analysed. POC blood gas analysis obtained within 5 min of ED arrival was used. OUTCOMES MEASURE AND ANALYSIS: The primary outcome was ROSC. In the derivation cohort, multivariable logistic regression was used to develop the POC-ED-ROSC model. In the temporally split validation cohort, the discriminative performance of the POC-ED-ROSC model was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and compared with RACA or UB-ROSC score using DeLong test. MAIN RESULTS: The study included 606 and 270 patients in the derivation and validation cohorts, respectively. In the total cohort, 471 patients achieved ROSC. Age, initial cardiac rhythm at ED, pre-hospital resuscitation duration, and POC testing-measured blood levels of lactate, potassium and glucose were significant predictors included in the POC-ED-ROSC model. The model was validated with fair discriminative performance (AUC: 0.75, 95% confidence interval [CI]: 0.69-0.81) with no significant differences from RACA (AUC: 0.68, 95% CI: 0.62-0.74) or UB-ROSC score (AUC: 0.74, 95% CI: 0.68-0.79). CONCLUSION: Using only six easily accessible variables, the POC-ED-ROSC model can predict ROSC for OHCA resuscitated at ED with fair accuracy.

Pulmonary embolism in United States emergency departments, 2010-2018.

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.

Preparation of Medical Supply for Prehospital Emergencies and Disasters: An Internet-Based Simulation Drill.

Background: Mass casualties caused by natural disasters and man-made events may overwhelm local emergency medical services and healthcare systems. Logistics is essential to a successful emergency medical response. Drills have been used in disaster preparedness to validate plans, policies, procedures, and agreements, and identify resource gaps. The application of the internet to facilitate the conduct of exercise was still limited. This study aimed to investigate the optimal preparation of medical supplies by medical emergency response teams (MERTs) during emergencies and disasters using an internet-based drill. Methods: An internet-based drill based on real-life mass casualty incidents (MCIs) was developed and conducted in Taiwan from June 2017 to July 2018. The drill involved an MCI with 50 events delivered under two scenarios: (1) reduced transfer capacity and well-functioning local healthcare facilities (emergency module); (2) severely reduced transfer capacity and dysfunctional local healthcare facilities (disaster module). For each event, medical supplies commonly prepared by local MERTs in Taiwan were listed in structured questionnaires and participants selected the supplies they would use. Results: Forty-three senior medical emergency responders participated in the survey (responding rate of 47.3%). Resuscitation-related supplies increased from emergency to disaster module (e.g., intubation from 9.1% to 13.9%; dopamine from 3.2% to 5.0%; all p < 0.001). In the subgroup analysis of events with life-threatening injuries, the utilization of resuscitation-related supplies (e.g., intubation from 46.6% to 65.3%; p < 0.001) remained higher in the disaster than in the emergency module. Compared to emergency medical technicians, physicians and nurses are more likely to use intravenous/intramuscular analgesics. Conclusions: The severity of scenarios and the professional background of emergency responders have a different utilization of medical supplies in the simulation drill. The internet-based drill may contribute to optimizing the preparedness of medical response to prehospital emergencies and disasters.

Social network analysis of nationwide interhospital emergency department transfers in Taiwan.

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.

Machine learning to predict in-hospital cardiac arrest from patients presenting to the emergency department.

In-hospital cardiac arrest (IHCA) in the emergency department (ED) is not uncommon but often fatal. Using the machine learning (ML) approach, we sought to predict ED-based IHCA (EDCA) in patients presenting to the ED based on triage data. We retrieved 733,398 ED records from a tertiary teaching hospital over a 7 year period (Jan. 1, 2009-Dec. 31, 2015). We included only adult patients (≥ 18 y) and excluded cases presenting as out-of-hospital cardiac arrest. Primary outcome (EDCA) was identified via a resuscitation code. Patient demographics, triage data, and structured chief complaints (CCs), were extracted. Stratified split was used to divide the dataset into the training and testing cohort at a 3-to-1 ratio. Three supervised ML models were trained and performances were evaluated and compared to the National Early Warning Score 2 (NEWS2) and logistic regression (LR) model by the area under the receiver operating characteristic curve (AUC). We included 316,465 adult ED records for analysis. Of them, 636 (0.2%) developed EDCA. Of the constructed ML models, Random Forest outperformed the others with the best AUC result (0.931, 95% CI 0.911-0.949), followed by Gradient Boosting (0.930, 95% CI 0.909-0.948) and Extra Trees classifier (0.915, 95% CI 0.892-0.936). Although the differences between each of ML models and LR (AUC: 0.905, 95% CI 0.882-0.926) were not significant, all constructed ML models performed significantly better than using the NEWS2 scoring system (AUC 0.678, 95% CI 0.635-0.722). Our ML models showed excellent discriminatory performance to identify EDCA based only on the triage information. This ML approach has the potential to reduce unexpected resuscitation events if successfully implemented in the ED information system.

High-risk Return Visits to United States Emergency Departments, 2010-2018.

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.

Pain Assessment in the Emergency Department: A Prospective Videotaped Study.

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 <0.001), with a weighted kappa coefficient of 0.39. Some discrepancies were noted: 102 (37%) patients were rated at a much lower pain score, whereas 52 (19%) patients were given a much higher pain score from physician review. The distributions of chief complaints were different between the two groups. Physician raters tended to provide lower pain scores to younger (P = 0.02) and less ill patients (P = 0.008). Additionally, attending-level physician raters were more likely to provide a higher pain score than resident-level raters (P <0.001). CONCLUSION: Patients' self-reported pain scores correlate positively with the pain score provided by physicians, with only a moderate agreement between the two. Under- and over-estimations of pain in ED patients occur in different clinical scenarios. Pain assessment in the ED should consider both patient and physician factors.

Post-resuscitation diastolic blood pressure is a prognostic factor for outcomes of cardiac arrest patients: a multicenter retrospective registry-based analysis.

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.

Physician gestalt for emergency department triage: A prospective videotaped study.

OBJECTIVE: Appropriate triage in patients presenting to the emergency department (ED) is often challenging. Little is known about the role of physician gestalt in ED triage. We aimed to compare the accuracy of emergency physician gestalt against the currently used computerized triage process. METHODS: We conducted a prospective observational study in the ED at an academic medical center. Adult patients aged ≥20 years were included and underwent a standard triage protocol. The patients underwent system-based triage using the computerized software the Taiwan Triage and Acuity Scale. The entire triage process was recorded, and triage data were collected. Five physician raters provided triage levels (physician-based) according to their perceived urgency after reviewing videos. The primary outcome was hospital admission. The secondary outcomes were ED length of stay (EDLOS) and charges. RESULTS: In total, 656 patients were recruited (mean age 52 years, 50% male). The median system-based triage level was 3. By contrast, the median physician-based triage level was 4. The physician raters tended to provide lower triage levels than the system, with an average difference of 1. There was modest concordance between the two triage methods (correlation coefficient 0.30), with a weighted kappa coefficient of 0.18. The area under the receiver operating curve for the system- and physician-based triage in predicting hospital admission were similar (0.635 vs. 0.631, p = 0.896). Attending physicians appeared to have better performance than residents in predicting admission. The variation explained (R2 ) in EDLOS and charges were similar between the two triage methods (R2  = 3% for EDLOS, 7%-9% for charges). CONCLUSIONS: Emergency physician gestalt for triage showed similar performance to a computerized system; however, physicians redistributed patients to lower triage levels. Physician gestalt has advantages for identifying low-risk patients. This approach may avoid undue time pressure for health care providers and promote rapid discharge.