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.

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.

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.

Pain trajectories in the emergency department: Patient characteristics and clinical outcomes.

BACKGROUND: Little is known about pain trajectories in the emergency department (ED), which could inform the heterogeneous response to pain treatment. We aimed to identify clinically relevant subphenotypes of pain resolution in the ED and their relationships with clinical outcomes. 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 data including patient demographics, triage data, repeated pain scores evaluated on a numeric rating scale, pain characteristics, laboratory markers, and patient disposition. The primary outcome measures were hospitalization and ED revisit. RESULTS: 28,105 adult ED patients were included with a total of 154,405 pain measurements. Three distinct pain trajectory groups were identified: no pain (57.1%); moderate-to-severe pain, fast resolvers (17.9%); and moderate pain, slow resolvers (24.9%). The fast resolvers responded well to treatment and were independently associated with a lower risk of hospitalization (adjusted odds ratio [aOR], 0.75; 95% confidence interval [CI], 0.70-0.81). By contrast, the slow resolvers had lingering pain in the ED and were independently associated with a higher risk of ED revisit (aOR, 2.65; 95%CI, 1.85-3.69). This group also had higher levels of inflammatory markers, including a higher leukocyte count and a higher level of C-reactive protein. CONCLUSIONS: We identified three novel pain subphenotypes with distinct patterns in clinical characteristics and patient outcomes. A better understanding of the pain trajectories may help with the personalized approach to pain management in the ED.

A Novel Deep Learning-Based System for Triage in the Emergency Department Using Electronic Medical Records: Retrospective Cohort Study.

BACKGROUND: Emergency 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. OBJECTIVE: This study aims to develop a deep learning-based triage system using patients' ED electronic medical records to predict clinical outcomes after ED treatments. METHODS: We 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). RESULTS: A 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. CONCLUSIONS: Our 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.

Clinical and Immunological Factors in Emphysema Progression. Five-Year Prospective Longitudinal Exacerbation Study of Chronic Obstructive Pulmonary Disease (LES-COPD).

RATIONALE: Cross-sectional studies of T-cell responses to self-antigens correlate with baseline emphysema severity. OBJECTIVES: We investigated whether clinical and/or immunological factors could predict disease progression, such as emphysema, FEV1, and 6-minute-walk distance (6MWD), in former and active smokers in a 5-year prospective study. METHODS: We recruited 224 ever smokers over 40 years of age and with greater than a 15 pack-year smoking history. MEASUREMENTS AND MAIN RESULTS: Repeated spirometry, 6MWD, and peripheral blood T-cell cytokine responses to lung elastin fragments were measured. Baseline and repeat chest computed tomography (CT) scans (34 to 65 mo apart) were used to quantify emphysema progression. Of the 141 ever-smokers with baseline and repeat CT scans, the mean (SD) annual rate of change in percent emphysema was +0.46 (0.92), ranging from -1.8 to +4.1. In multivariable analyses, the rate of emphysema progression was greater in subjects who had lower body mass index (BMI) (+0.15 per 5-unit decrease in BMI; 95% confidence interval, +0.03 to +0.29). In active smokers, increased IFN-γ and IL-6 T-cell responses had a positive association with the annual rate of emphysema progression. Male sex and IL-6 T-cell responses to elastin fragments were significantly associated with annual 6MWD decline, whereas IL-13 was associated with an increase in annual 6MWD. CONCLUSIONS: The rate of emphysema progression quantified by CT scans among ever-smokers was highly variable; clinical factors and biomarkers explained only some of the variability. Aggressive clinical care that targets active smokers with autoreactive T cells and low BMI may temporize progression of emphysema.

Comparison of US emergency department acute asthma care quality: 1997-2001 and 2011-2012.

BACKGROUND: It remains unclear whether the quality of acute asthma care in US emergency departments (EDs) has improved over time. OBJECTIVES: We investigated changes in concordance of ED asthma care with 2007 National Institutes of Health guidelines, identified ED characteristics predictive of concordance, and tested whether higher concordance was associated with lower risk of hospitalization. METHODS: We performed chart reviews in ED patients aged 18 to 54 years with asthma exacerbations in 48 EDs during 2 time periods: 1997-2001 (2 prior studies) and 2011-2012 (new study). Concordance with guideline recommendations was evaluated by using item-by-item quality measures and composite concordance scores at the patient and ED levels; these scores ranged from 0 to 100. RESULTS: The analytic cohort comprised 4039 patients (2119 from 1997-2001 vs 1920 from 2011-2012). Over these 16 years, emergency asthma care became more concordant with level A recommendations at both the patient and ED levels (both P < .001). By contrast, concordance with non-level A recommendations (peak expiratory flow measurement and timeliness) decreased at both the patient (median score, 75 [interquartile range, 50-100] to 50 [interquartile range, 33-75], P < .001) and ED (mean score, 67 [SD, 7] to 50 [SD, 16], P < .001) levels. Multivariable analysis demonstrated ED concordance was lower in Southern and Western EDs compared with Midwestern EDs. After adjusting for severity, guideline-concordant care was associated with lower risk of hospitalization (odds ratio, 0.37; 95% CI, 0.26-0.53). CONCLUSIONS: Between 1997 and 2012, we observed changes in the quality of emergency asthma care that differed by level of guideline recommendation and substantial interhospital and geographic variations. Greater concordance with guideline-recommended management might reduce unnecessary hospitalizations.

Analysis of transtheoretical model of health behavioral changes in a nutrition intervention study--a continuous time Markov chain model with Bayesian approach.

Continuous time Markov chain (CTMC) models are often used to study the progression of chronic diseases in medical research but rarely applied to studies of the process of behavioral change. In studies of interventions to modify behaviors, a widely used psychosocial model is based on the transtheoretical model that often has more than three states (representing stages of change) and conceptually permits all possible instantaneous transitions. Very little attention is given to the study of the relationships between a CTMC model and associated covariates under the framework of transtheoretical model. We developed a Bayesian approach to evaluate the covariate effects on a CTMC model through a log-linear regression link. A simulation study of this approach showed that model parameters were accurately and precisely estimated. We analyzed an existing data set on stages of change in dietary intake from the Next Step Trial using the proposed method and the generalized multinomial logit model. We found that the generalized multinomial logit model was not suitable for these data because it ignores the unbalanced data structure and temporal correlation between successive measurements. Our analysis not only confirms that the nutrition intervention was effective but also provides information on how the intervention affected the transitions among the stages of change. We found that, compared with the control group, subjects in the intervention group, on average, spent substantively less time in the precontemplation stage and were more/less likely to move from an unhealthy/healthy state to a healthy/unhealthy state.

Airway surface mycosis in chronic TH2-associated airway disease.

BACKGROUND: Environmental fungi have been linked to TH2 cell-related airway inflammation and the TH2-associated chronic airway diseases asthma, chronic rhinosinusitis (CRS) with nasal polyps (CRSwNP), and allergic fungal rhinosinusitis (AFRS), but whether these organisms participate directly or indirectly in disease pathology remains unknown. OBJECTIVE: To determine the frequency of fungus isolation and fungus-specific immunity in patients with TH2-associated and non-TH2-associated airway disease. METHODS: Sinus lavage fluid and blood were collected from sinus surgery patients (n = 118) including patients with CRSwNP, patients with CRS without nasal polyps, patients with AFRS, and non-CRS/nonasthmatic control patients. Asthma status was determined from medical history. Sinus lavage fluids were cultured and directly examined for evidence of viable fungi. PBMCs were restimulated with fungal antigens in an enzyme-linked immunocell spot assay to determine total memory fungus-specific IL-4-secreting cells. These data were compared with fungus-specific IgE levels measured from plasma by ELISA. RESULTS: Filamentous fungi were significantly more commonly cultured in patients with TH2-associated airway disease (asthma, CRSwNP, or AFRS: n = 68) than in control patients with non-TH2-associated disease (n = 31): 74% vs 16%, respectively (P < .001). Both fungus-specific IL-4 enzyme-linked immunocell spot (n = 48) and specific IgE (n = 70) data correlated with TH2-associated diseases (sensitivity 73% and specificity 100% vs 50% and 77%, respectively). CONCLUSIONS: The frequent isolation of fungi growing directly within the airways accompanied by specific immunity to these organisms only in patients with TH2-associated chronic airway diseases suggests that fungi participate directly in the pathogenesis of these conditions. Efforts to eradicate airway fungi from the airways should be considered in selected patients.

Adjunctive traditional Chinese medicine therapy improves survival in patients with advanced breast cancer: a population-based study.

BACKGROUND: Traditional Chinese medicine (TCM) is one of the most common complementary and alternative medicines used in the treatment of patients with breast cancer. However, the clinical effect of TCM on survival, which is a major concern in these individuals, lacks evidence from large-scale clinical studies. METHODS: The authors used the Taiwan National Health Insurance Research Database to conduct a retrospective population-based cohort study of patients with advanced breast cancer between 2001 and 2010. The patients were separated into TCM users and nonusers, and Cox regression models were applied to determine the association between the use of TCM and patient survival. RESULTS: A total of 729 patients with advanced breast cancer receiving taxanes were included in the current study. Of this cohort, the mean age was 52.0 years; 115 patients were TCM users (15.8%) and 614 patients were TCM nonusers. The mean follow-up was 2.8 years, with 277 deaths reported to occur during the 10-year period. Multivariate analysis demonstrated that, compared with nonusers, the use of TCM was associated with a significantly decreased risk of all-cause mortality (adjusted hazards ratio [HR], 0.55 [95% confidence interval, 0.33-0.90] for TCM use of 30-180 days; adjusted HR, 0.46 [95% confidence interval, 0.27-0.78] for TCM use of >180 days). Among the frequently used TCMs, those found to be most effective (lowest HRs) in reducing mortality were Bai Hua She She Cao, Ban Zhi Lian, and Huang Qi. CONCLUSIONS: The results of the current observational study suggest that adjunctive TCM therapy may lower the risk of death in patients with advanced breast cancer. Future randomized controlled trials are required to validate these findings.

Frequent utilization of the emergency department for acute exacerbation of chronic obstructive pulmonary disease.

BACKGROUND: Little is known about patients who frequently visit the emergency department (ED) for acute exacerbation of chronic obstructive pulmonary disease (AECOPD). We aimed to quantify the proportion and characteristics of patients with frequent ED visits for AECOPD and associated healthcare utilization. METHODS: We conducted a retrospective cohort study of adults aged ≥40 years with at least one ED visit for AECOPD between 2010 and 2011, derived from population-based all-payer data of State ED and Inpatient Databases for two large and diverse states: California and Florida. Outcome measures were frequency of ED visits for AECOPD, 30-day ED revisits, subsequent hospitalizations, near-fatal events (AECOPD involving mechanical ventilation), and charges for both ED and inpatient services (available only for Florida) during the year after the first ED visit. RESULTS: The analytic cohort comprised 98,280 unique patients with 154,736 ED visits for AECOPD. During the 1-year period, 29.4% (95% CI, 29.1%-29.7%) of the patients had two or more (frequent) visits, accounting for 55.2% (95% CI, 54.9%-55.4%) of all ED visits for AECOPD. In the multivariable model, significant predictors of frequent ED visits were age 55-74 years (vs. 40-54 years), male sex, non-Hispanic white or black race, Medicaid insurance (vs. private), and lower median household income (all P < 0.001). At the visit-level, 12.3% of ED visits for AECOPD were 30-day revisit events (95% CI, 12.1%-12.4%). Additionally, 62.8% of ED visits for AECOPD (95% CI, 62.6%-63.0%) resulted in a hospitalization; patients with frequent ED visits comprised 55.5% (95% CI, 55.2%-55.8%) of all hospitalizations. Furthermore, 7.3% (95% CI, 7.3%-7.5%) of ED visits for AECOPD led to a near-fatal event; patients with frequent ED visits accounted for 64.4% (95% CI, 63.5%-65.3%) of all near-fatal events. Total charges for AECOPD were $1.94 billion (95% CI, $1.90-1.97 billion) in Florida; patients with frequent ED visits accounted for $1.07 billion (95% CI, $1.04-1.09 billion). CONCLUSIONS: In this large cohort study, we found that 29% had frequent ED visits for AECOPD and that lower socioeconomic status was significantly associated with a higher frequency of ED visits. Individuals with frequent ED visits for AECOPD accounted for a substantial amount of healthcare utilization and financial burden.

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.

Deep Learning-Based Model for Non-invasive Hemoglobin Estimation via Body Parts Images: A Retrospective Analysis and a Prospective Emergency Department Study.

Anemia is a significant global health issue, affecting over a billion people worldwide, according to the World Health Organization. Generally, the gold standard for diagnosing anemia relies on laboratory measurements of hemoglobin. To meet the need in clinical practice, physicians often rely on visual examination of specific areas, such as conjunctiva, to assess pallor. However, this method is subjective and relies on the physician's experience. Therefore, we proposed a deep learning prediction model based on three input images from different body parts, namely, conjunctiva, palm, and fingernail. By incorporating additional body part labels and employing a fusion attention mechanism, the model learns and enhances the salient features of each body part during training, enabling it to produce reliable results. Additionally, we employ a dual loss function that allows the regression model to benefit from well-established classification methods, thereby achieving stable handling of minority samples. We used a retrospective data set (EYES-DEFY-ANEMIA) to develop this model called Body-Part-Anemia Network (BPANet). The BPANet showed excellent performance in detecting anemia, with accuracy of 0.849 and an F1-score of 0.828. Our multi-body-part model has been validated on a prospectively collected data set of 101 patients in National Taiwan University Hospital. The prediction accuracy as well as F1-score can achieve as high as 0.716 and 0.788, respectively. To sum up, we have developed and validated a novel non-invasive hemoglobin prediction model based on image input from multiple body parts, with the potential of real-time use at home and in clinical settings.

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.

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.

External Validation and Comparison of Statistical and Machine Learning-Based Models in Predicting Outcomes Following Out-of-Hospital Cardiac Arrest: A Multicenter Retrospective Analysis.

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<0.001) and the Swedish Cardiac Arrest Risk Score model (P=0.007). Subgroup analysis indicated no significant difference in predictive performance for patients resuscitated before versus after 2020. CONCLUSIONS: In this external validation, both statistical and machine learning-based models demonstrated excellent and fair performance, respectively, in predicting neurological outcomes despite different model architectures. The predictive performance of all evaluated clinical scoring systems was not significantly influenced by the COVID-19 pandemic.

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.

Age-related differences in asthma outcomes in the United States, 1988-2006.

BACKGROUND: Relatively little is known about the effect of age on asthma outcomes in adults, particularly at a national level. OBJECTIVE: To investigate age-related differences in asthma outcomes in a nationally representative, longitudinal study. METHODS: We analyzed data from the Third National Health and Nutrition Examination Survey (1988-1994) with linked mortality files through 2006. Adults with physician-diagnosed asthma were identified and were divided into 2 age groups: younger adults (17-54 years of age) and older adults (55 years or older). The outcome measures were both cross-sectional (health care use, comorbidity, and lung function) and longitudinal (all-cause mortality). RESULTS: There were an estimated 9,566,000 adults with current asthma. Of these, 73% were younger adults and 27% older adults. Compared with younger adults, older adults had more hospitalizations in the past year, more comorbidities, and poorer lung function (eg, lower forced expiratory volume in 1 second) (P < .05 for all). During a median follow-up of 15 years, significant baseline predictors of higher all-cause mortality included older age (≥55 vs <55 years old: adjusted hazard ratio [HR], 6.77; 95% confidence interval [CI], 3.15-14.54), poor health status (fair and poor vs excellent health status: adjusted HR, 10.07; 95% CI, 3.75-27.01), and vitamin D deficiency (vitamin D level <30 vs ≥50 nmol/L: adjusted HR, 2.19; 95% CI, 1.05-4.58), whereas Mexican American ethnicity (adjusted HR, 0.31; 95% CI, 0.14-0.65) was associated with lower mortality. Controlling for age, asthma was not associated with increased all-cause mortality (adjusted HR, 1.28; 95% CI, 0.99-1.65). CONCLUSION: Older adults with asthma have a substantial burden of morbidity and increased mortality. The ethnic differences in asthma mortality and the vitamin D-mortality link merit further investigation.

Age-related differences in clinical outcomes for acute asthma in the United States, 2006-2008.

BACKGROUND: Little is known about the effect of age on acute asthma outcomes. OBJECTIVE: We sought to investigate age-related differences in the emergency department (ED) presentation and clinical outcomes for patients with acute asthma. METHODS: We analyzed the 2006-2008 Nationwide Emergency Department Sample, the largest, all-payer, US ED and inpatient database. ED visits for acute asthma were identified with a principal diagnosis of International Classification of Disease, ninth revision, Clinical Modification code 493.xx. Patients were divided into 3 age groups: children (<18 years), younger adults (18-54 years), and older adults (≥55 years). The outcome measures were in-hospital all-cause mortality, near-fatal asthma-related events (noninvasive or mechanical ventilation), hospital charges, admission rates, and hospital length of stay. RESULTS: There were an estimated 1,813,000 visits annually for acute asthma from approximately 4,700 EDs. The estimated overall annual number of in-hospital asthma-related deaths was 1,144 (0.06%); 101 died in the ED, and 1,043 died as inpatients. By age group, there were 37 asthma-related deaths per year in children, 204 in younger adults, and 903 in older adults. Compared with younger adults, older adults had higher mortality, had higher rates of near-fatal asthma-related events, had higher hospital charges, were more likely to be hospitalized, and had a longer hospital length of stay (P < .001 for all). After adjusting for comorbidities, older asthmatic patients had a 5-fold increased risk of overall mortality (adjusted odds ratio, 5.2; 95% CI, 4.0-6.9), compared with younger adults. CONCLUSIONS: Older adults with acute asthma have a substantial burden of morbidity and mortality. With the US population aging, there is an urgent need for targeted interventions for this high-risk population.