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Abstract Background Effective communication between patients and healthcare providers in the emergency department (ED) is challenging due to the dynamic nature of the ED environment. This study aimed to trial a chat service enabling patients in the ED and their family members to ask questions freely, exploring the service’s feasibility and user experience. Objectives To identify the types of needs and inquiries from patients and family members in the ED that could be addressed through the chat service and to assess the user experience of the service. Methods We enrolled patients and family members aged over 19 years in the ED, providing the chat service for up to 4 h per ED visit. Trained research nurses followed specific guidelines to respond to messages from the participants. After participation, participants were required to complete a survey. Those who agreed also participated in interviews to provide insights on their experiences with the ED chat service. Results A total of 40 participants (20 patients and 20 family members) sent 305 messages (72 by patients and 233 by family members), with patients sending an average of 3.6 messages and family members 11.7. Research nurses resolved 41.4% of patient inquiries and 70.9% of family member inquiries without further healthcare provider involvement. High usability was reported, with positive feedback on communication with healthcare workers, information accessibility, and emotional support. Conclusions The ED chat service was found to be feasible and led to positive user experiences for both patients and their family members.

BackgroundSince its introduction, telemedicine for patients with chronic diseases has been studied in various clinical settings. However, there is limited evidence of the effectiveness and medical safety of the nationwide adoption of telemedicine. ObjectiveThis study aimed to analyze the effects of telemedicine on chronic diseases during the COVID-19 pandemic under a temporary telemedicine policy in South Korea using national claims data. MethodsHealth insurance claims data were extracted over 2 years: 1 year before (from February 24, 2019, to February 23, 2020) and 1 year after the policy was implemented (from February 24, 2020, to February 23, 2021). We included all patients who used telemedicine at least once in the first year after the policy was implemented and compared them with a control group of patients who never used telemedicine. The comparison focused on health care use; the medication possession ratio (MPR); and admission rates to general wards (GWs), emergency departments (EDs), and intensive care units (ICUs) using difference-in-differences analysis. A total of 4 chronic diseases were targeted: hypertension, diabetes mellitus (DM), chronic obstructive pulmonary disease (COPD), and common mental disorders. ResultsA total of 1,773,454 patients with hypertension; 795,869 patients with DM; 37,460 patients with COPD; and 167,084 patients with common mental disorders were analyzed in this study. Patients diagnosed with hypertension or DM showed increased MPRs without an increase in GW, ED, or ICU admission rates during the policy year. Moreover, patients in the DM group who did not use telemedicine had higher rates of ED, GW, and ICU admissions, and patients in the hypertension group had higher rates of GW or ICU admissions after 1 year of policy implementation. This trend was not evident in COPD and common mental disorders. ConclusionsThe temporary telemedicine policy was effective in increasing medication adherence and reducing admission rates for patients with hypertension and DM; however, the efficacy of the policy was limited for patients with COPD and common mental disorders. Future studies are required to demonstrate the long-term effects of telemedicine policies with various outcome measures reflecting disease characteristics.

Abstract Background Many cases of deep vein thrombosis (DVT) are diagnosed in the emergency department, and abbreviated lower extremity venous point-of-care ultrasound (POCUS) has already shown an accuracy comparable to that of specialists. This study aimed to identify the learning curve necessary for emergency medicine (EM) residents to achieve expertise-level accuracy in diagnosing DVT through a 3-point lower extremity venous POCUS. Methods This prospective study was conducted at an emergency department between May 2021 and October 2022. Four EM residents underwent a one-hour POCUS training session and performed DVT assessments in participants with DVT symptoms or confirmed pulmonary embolism. POCUS was performed at three proximal lower extremity sites to evaluate the thrombi presence and vein compressibility, with results validated by specialized radiology ultrasound. Cumulative sum (CUSUM) and the Bush and Mosteller models were used to analyze the learning curve, while generalized estimating equations were used to identify factors affecting diagnostic accuracy. Results 91 POCUS scans were conducted in 49 patients, resulting in 22% DVT confirmed by specialized venous ultrasound. In the CUSUM analysis, all four EM residents attained a 90% success rate at the common femoral vein, whereas only half achieved this rate when all three sites were considered. According to Bush and Mosteller models, 13–18 cases are required to attain 90–95% diagnostic accuracy. After 10–16 cases, the examination time for each resident decreased, and a 20% increase in examiner confidence was linked to a 2.506-fold increase in the DVT diagnosis accuracy. Conclusion EM residents generally required 13–18 cases for 90–95% DVT diagnostic accuracy, but proficiency varied among individuals, particularly requiring more cases for regions outside the common femoral vein.

Abstract Emergency departments (ED) are complex, triage is a main task in the ED to prioritize patient with limited medical resources who need them most. Machine learning (ML) based ED triage tool, Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable ML framework with single center. We aimed to develop SERP with 3 Korean multicenter cohorts based on common data model (CDM) without data sharing and compare performance with inter-hospital validation design. This retrospective cohort study included all adult emergency visit patients of 3 hospitals in Korea from 2016 to 2017. We adopted CDM for the standardized multicenter research. The outcome of interest was 2-day mortality after the patients’ ED visit. We developed each hospital SERP using interpretable ML framework and validated inter-hospital wisely. We accessed the performance of each hospital’s score based on some metrics considering data imbalance strategy. The study population for each hospital included 87,670, 83,363 and 54,423 ED visits from 2016 to 2017. The 2-day mortality rate were 0.51%, 0.56% and 0.65%. Validation results showed accurate for inter hospital validation which has at least AUROC of 0.899 (0.858–0.940). We developed multicenter based Interpretable ML model using CDM for 2-day mortality prediction and executed Inter-hospital external validation which showed enough high accuracy.

Abstract To save time during transport, where resuscitation quality can degrade in a moving ambulance, it would be prudent to continue the resuscitation on scene if there is a high likelihood of ROSC occurring at the scene. We developed the pre-hospital real-time cardiac arrest outcome prediction (PReCAP) model to predict ROSC at the scene using prehospital input variables with time-adaptive cohort. The patient survival at discharge from the emergency department (ED), the 30-day survival rate, and the final Cerebral Performance Category (CPC) were secondary prediction outcomes in this study. The Pan-Asian Resuscitation Outcome Study (PAROS) database, which includes out-of-hospital cardiac arrest (OHCA) patients transferred by emergency medical service in Asia between 2009 and 2018, was utilized for this study. From the variables available in the PAROS database, we selected relevant variables to predict OHCA outcomes. Light gradient-boosting machine (LightGBM) was used to build the PReCAP model. Between 2009 and 2018, 157,654 patients in the PAROS database were enrolled in our study. In terms of prediction of ROSC on scene, the PReCAP had an AUROC score between 0.85 and 0.87. The PReCAP had an AUROC score between 0.91 and 0.93 for predicting survived to discharge from ED, and an AUROC score between 0.80 and 0.86 for predicting the 30-day survival. The PReCAP predicted CPC with an AUROC score ranging from 0.84 to 0.91. The feature importance differed with time in the PReCAP model prediction of ROSC on scene. Using the PAROS database, PReCAP predicted ROSC on scene, survival to discharge from ED, 30-day survival, and CPC for each minute with an AUROC score ranging from 0.8 to 0.93. As this model used a multi-national database, it might be applicable for a variety of environments and populations.

BackgroundIn the wake of challenges brought by the COVID-19 pandemic to conventional medical education, the demand for innovative teaching methods has surged. Nurse training, with its focus on hands-on practice and self-directed learning, encountered significant hurdles with conventional approaches. Augmented reality (AR) offers a potential solution to addressing this issue. ObjectiveThe aim of this study was to develop, introduce, and evaluate an AR-based educational program designed for nurses, focusing on its potential to facilitate hands-on practice and self-directed learning. MethodsAn AR-based educational program for nursing was developed anchored by the Kern six-step framework. First, we identified challenges in conventional teaching methods through interviews and literature reviews. Interviews highlighted the need for hands-on practice and on-site self-directed learning with feedback from a remote site. The training goals of the platform were established by expert trainers and researchers, focusing on the utilization of a ventilator and extracorporeal membrane oxygenation system. Intensive care nurses were enrolled to evaluate AR education. We then assessed usability and acceptability of the AR training using the System Usability Scale and Technology Acceptance Model with intensive care nurses who agreed to test the new platform. Additionally, selected participants provided deeper insights through semistructured interviews. ResultsThis study highlights feasibility and key considerations for implementing an AR-based educational program for intensive care unit nurses, focusing on training objectives of the platform. Implemented over 2 months using Microsoft Dynamics 365 Guides and HoloLens 2, 28 participants were trained. Feedback gathered through interviews with the trainers and trainees indicated a positive reception. In particular, the trainees mentioned finding AR particularly useful for hands-on learning, appreciating its realism and the ability for repetitive practice. However, some challenges such as difficulty in adapting to the new technology were expressed. Overall, AR exhibits potential as a supplementary tool in nurse education. ConclusionsTo our knowledge, this is the first study to substitute conventional methods with AR in this specific area of critical care nursing. These results indicate the multiple principal factors to take into consideration when adopting AR education in hospitals. AR is effective in promoting self-directed learning and hands-on practice, with participants displaying active engagement and enhanced skill acquisition. Trial RegistrationClinicalTrials.gov NCT05629663; https://clinicaltrials.gov/study/NCT05629663.

Abstract Various efforts have been made to diagnose acute cardiovascular diseases (CVDs) early in patients. However, the sole option currently is symptom education. It may be possible for the patient to obtain an early 12-lead electrocardiogram (ECG) before the first medical contact (FMC), which could decrease the physical contact between patients and medical staff. Thus, we aimed to verify whether laypersons can obtain a 12-lead ECG in an off-site setting for clinical treatment and diagnosis using a patch-type wireless 12-lead ECG (PWECG). Participants who were ≥ 19 years old and under outpatient cardiology treatment were enrolled in this simulation-based one-arm interventional study. We confirmed that participants, regardless of age and education level, can use the PWECG on their own. The median age of the participants was 59 years (interquartile range [IQR] = 56–62 years), and the median duration to obtain a 12-lead ECG result was 179 s (IQR = 148–221 s). With appropriate education and guidance, it is possible for a layperson to obtain a 12-lead ECG, minimizing the contact with a healthcare provider. These results can be used subsequently for treatment.

Background and aims: This study developed a clinical support system based on federated learning to predict the need for a revised Korea Triage Acuity Scale (KTAS) to facilitate triage. Methods: This was a retrospective study that used data from 11,952,887 patients in the Korean National Emergency Department Information System (NEDIS) from 2016 to 2018 for model development. Separate cohorts were created based on the emergency medical center level in the NEDIS: regional emergency medical center (REMC), local emergency medical center (LEMC), and local emergency medical institution (LEMI). External and temporal validation used data from emergency department (ED) of the study site from 2019 to 2021. Patient features obtained during the triage process and the initial KTAS scores were used to develop the prediction model. Federated learning was used to rectify the disparity in data quality between EDs. The patient's demographic information, vital signs in triage, mental status, arrival information, and initial KTAS were included in the input feature. Results: 3,626,154 patients' visits were included in the regional emergency medical center cohort; 8,278,081 patients' visits were included in the local emergency medical center cohort; and 48,652 patients’ visits were included in the local emergency medical institution cohort. The study site cohort, which is used for external and temporal validation, included 135,780 patients visits. Among the patients in the REMC and study site cohorts, KTAS level 3 patients accounted for the highest proportion at 42.4% and 45.1%, respectively, whereas in the LEMC and LEMI cohorts, KTAS level 4 patients accounted for the highest proportion. The area under the receiver operating characteristic curve for the prediction model was 0.786, 0.750, and 0.770 in the external and temporal validation. Patients with revised KTAS scores had a higher admission rate and ED mortality rate than those with unaltered KTAS scores. Conclusions: This novel system might accurately predict the likelihood of KTAS acuity revision and support clinician-based triage.

Objective Falls are one of the most frequently occurring adverse events among hospitalized patients. The Morse Fall Scale, which has been widely used for fall risk assessment, has the two limitations of low specificity and difficulty in practical implementation. The aim of this study was to develop and validate an interpretable machine learning model for prediction of falls to be integrated in an electronic medical record (EMR) system. Methods This was a retrospective study involving a tertiary teaching hospital in Seoul, Korea. Based on the literature, 83 known predictors were grouped into seven categories. Interpretable fall event prediction models were developed using multiple machine learning models including gradient boosting and Shapley values. Results Overall, 191,778 cases with 272 fall events (0.1%) were included in the analysis. With the validation cohort of 2020, the area under the receiver operating curve (AUROC) of the gradient boosting model was 0.817 (95% confidence interval [CI], 0.720–0.904), better performance than random forest (AUROC, 0.801; 95% CI, 0.708–0.890), logistic regression (AUROC, 0.802; 95% CI, 0.721–0.878), artificial neural net (AUROC, 0.736; 95% CI, 0.650–0.821), and conventional Morse fall score (AUROC, 0.652; 95% CI, 0.570–0.715). The model’s interpretability was enhanced at both the population and patient levels. The algorithm was later integrated into the current EMR system. Conclusion We developed an interpretable machine learning prediction model for inpatient fall events using EMR integration formats.

Abstract The study aims to measure the effectiveness of an AI-based traumatic intracranial hemorrhage prediction model in the decisions of emergency physicians regarding ordering head computed tomography (CT) scans. We developed a deep-learning model for predicting traumatic intracranial hemorrhages (DEEPTICH) using a national trauma registry with 1.8 million cases. For simulation, 24 cases were selected from previous emergency department cases. For each case, physicians made decisions on ordering a head CT twice: initially without the DEEPTICH assistance, and subsequently with the DEEPTICH assistance. Of the 528 responses from 22 participants, 201 initial decisions were different from the DEEPTICH recommendations. Of these 201 initial decisions, 94 were changed after DEEPTICH assistance (46.8%). For the cases in which CT was initially not ordered, 71.4% of the decisions were changed (p

This study aimed to compare the accuracy of real-time trans-tracheal ultrasound (TTUS) with capnography to confirm intubation in cardiopulmonary resuscitation (CPR) while wearing a powered air-purifying respirator (PAPR). This setting reflects increased caution due to contagious diseases. This single-center, prospective, comparative study enrolled patients requiring CPR while wearing a PAPR who visited the emergency department of a tertiary medical center from December 2020 to August 2022. A physician performed the TTUS in real time and recorded the tube placement assessment. Another healthcare provider attached waveform capnography to the tube and recorded end-tidal carbon dioxide (EtCO2) after five ventilations. The accuracy and agreement of both methods compared with direct laryngoscopic visualization of tube placement, and the time taken by both methods was evaluated. Thirty-three patients with cardiac arrest were analyzed. TTUS confirmed tube placement with 100% accuracy, sensitivity, and specificity, whereas capnography demonstrated 97% accuracy, 96.8% sensitivity, and 100% specificity. The Kappa values for TTUS and capnography compared to direct visualization were 1.0 and 0.7843, respectively. EtCO2 was measured in 45 (37–59) seconds (median (interquartile range)), whereas TTUS required only 12 (8–23) seconds, indicating that TTUS was significantly faster (p < 0.001). No significant correlation was found between the physician’s TTUS proficiency and image acquisition time. This study demonstrated that TTUS is more accurate and faster than EtCO2 measurement for confirming endotracheal tube placement during CPR, particularly in the context of PAPR usage in pandemic conditions.

This pilot study aimed to develop a new, reliable, and easy-to-use method for the evaluation of diastolic function through the M-mode measurement of mitral valve (MV) movement in the parasternal long axis (PSLA), similar to E-point septal separation (EPSS) used for systolic function estimation. Thirty healthy volunteers from a tertiary emergency department (ED) underwent M-mode measurements of the MV anterior leaflet in the PSLA view. EPSS, A-point septal separation (APSS), A-point opening length (APOL), and E-point opening length (EPOL) were measured in the PSLA view, along with the E and A velocities and e’ velocity in the apical four-chamber view. Correlation analyses were performed to assess the relationship between M-mode and Doppler measurements, and the measurement time was evaluated. No significant correlations were found between M-mode and Doppler measurements in the study. However, M-mode measurements exhibited high reproducibility and faster acquisition, and the EPOL value consistently exceeded the APOL value, resembling the E and A pattern. These findings suggest that visually assessing the M-mode pattern on the MV anterior leaflet in the PSLA view may be a practical approach to estimating diastolic function in the ED. Further investigations with a larger and more diverse patient population are needed to validate these findings.

BackgroundRecently, the demand for mechanical ventilation (MV) has increased with the COVID-19 pandemic; however, the conventional approaches to MV training are resource intensive and require on-site training. Consequently, the need for independent learning platforms with remote assistance in institutions without resources has surged. ObjectiveThis study aimed to determine the feasibility and effectiveness of an augmented reality (AR)–based self-learning platform for novices to set up a ventilator without on-site assistance. MethodsThis prospective randomized controlled pilot study was conducted at Samsung Medical Center, Korea, from January to February 2022. Nurses with no prior experience of MV or AR were enrolled. We randomized the participants into 2 groups: manual and AR groups. Participants in the manual group used a printed manual and made a phone call for assistance, whereas participants in the AR group were guided by AR-based instructions and requested assistance with the head-mounted display. We compared the overall score of the procedure, required level of assistance, and user experience between the groups. ResultsIn total, 30 participants completed the entire procedure with or without remote assistance. Fewer participants requested assistance in the AR group compared to the manual group (7/15, 47.7% vs 14/15, 93.3%; P=.02). The number of steps that required assistance was also lower in the AR group compared to the manual group (n=13 vs n=33; P=.004). The AR group had a higher rating in predeveloped questions for confidence (median 3, IQR 2.50-4.00 vs median 2, IQR 2.00-3.00; P=.01), suitability of method (median 4, IQR 4.00-5.00 vs median 3, IQR 3.00-3.50; P=.01), and whether they intended to recommend AR systems to others (median 4, IQR 3.00-5.00 vs median 3, IQR 2.00-3.00; P=.002). ConclusionsAR-based instructions to set up a mechanical ventilator were feasible for novices who had no prior experience with MV or AR. Additionally, participants in the AR group required less assistance compared with those in the manual group, resulting in higher confidence after training. Trial RegistrationClinicalTrials.gov NCT05446896; https://beta.clinicaltrials.gov/study/NCT05446896

Background and objectives: It is often challenging even for skilled rescuers to provide adequate positive pressure ventilation consistently. This study aimed to investigate the effectiveness of a newly developed real-time ventilation feedback device (RTVFD) that estimates tidal volume (TV) and ventilation interval (VI) in real time. Materials and methods: We conducted a randomised, crossover, manikin simulation study. A total of 26 medical providers were randomly assigned to the RTVFD-assisted ventilation (RAV) first group (n = 13) and the non-assisted ventilation (NV) first group (n = 13). Participants provided ventilation using adult and paediatric bag valves (BVs) for 2 min each. After a washout period, the simulation was repeated by exchanging the participants’ groups. Results: The primary outcome was optimal TV in the RAV and NV groups using adult and paediatric BVs. A secondary outcome was optimal VI in the RAV and NV groups using adult and paediatric BVs. The proportions of optimal TV values were higher for the RAVs when using both adult and paediatric BVs (adult BV: 47.29% vs. 18.46%, p < 0.001; paediatric BV: 89.51% vs. 72.66%, p < 0.001) than for the NVs. The proportions of optimal VI were significantly higher in RAVs when using both adult and paediatric BVs than that in NVs (adult BV: 95.64% vs. 50.20%, p < 0.001; paediatric BV: 95.83% vs. 57.14%, p < 0.001). Additionally, we found that with paediatric BVs, the simulation had a higher OR for both optimal TV (13.26; 95% CI, 9.96–17.65; p < 0.001) and VI (1.32; 1.08–1.62, p = 0.007), regardless of RTVFD use. Conclusion: Real-time feedback using RTVFD significantly improves the TV and VI in both adult and paediatric BVs in a manikin simulation study.

Though chest radiography is a first-line diagnostic tool in the emergency department (ED), interpretation has a high error rate. We aimed to evaluate the usability and acceptability of deep learning-based computer-aided detection for chest radiography (DeepCADCR) in an ED environment. We conducted a single-institution survey of emergency physicians (EPs) who had used DeepCADCR (Lunit INSIGHT Chest Xray (CXR), version 3.1.4.1) as part of their ED workflow for at least three months. We developed 22 questions that assessed the subscales of effectiveness, efficiency, safety, satisfaction, and reliability. A seven-point Likert agreement scale was used to rate the responses. A total of 23 EPs who completed the survey was enrolled in the study. When averaged by subscale, satisfaction scores were highest (mean 4.71, standard deviation (SD) 1.43), and safety scores were lowest (mean 4.3, SD 0.72). When scores were converted to acceptability, the total average acceptance of DeepCADCR was 86.0%, with higher scores in ED residents than ED specialists for all subscales. Use of DeepCADCR in the ED workflow was well accepted by EPs. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the association of characteristics and clinical outcomes of patients who presented with mushroom intoxication. We conducted a retrospective study of mushroom intoxication cases using national registry data (representative of the period 01 January 2011 to 31 December 2019). Specifically, we analyzed the demographics, vital signs, mental status, intoxication related factors, and clinical presentations of the patients. The outcomes were assessed based on general ward admission, intensive care unit (ICU) admission, length of hospitalization, and mortality. The t-test or chisquare test were used to compare the emergency department (ED) discharge group and admission group. Logistic regressions were used to predict patients that were high-risk with regards to being admitted. A total of 393 patients with mushroom intoxication were presented at the ED, and the highest number of visits occurred in the month of September. Overall, 277 (70.5%) patients were discharged from the ED and 116 (29.5%) patients were admitted. Of these, 25 (6.4%) patients were admitted to the ICU, and 3 (2.6%) patients died. Patients are more likely to be admitted to the general ward or ICU when symptoms presented after 6 h: 6 to 24 h (Odds ratio (OR) 2.158; 95% Confidence interval (CI), 1.218-3.816) or >24 h (OR: 3.382; 95% CI, 1.438-8.050). Additionally, when the patients presented with diarrhea, they were more likely to be discharged with favorable outcomes with a less likelihood for admission (OR: 0.237; 95% CI, 0.093-0.523). Most cases of mushroom intoxication presented with gastrointestinal symptoms and followed a benign course. The longer time to onset of symptoms increased the likelihood of hospitalization. Clinicians should evaluate more carefully, observe, or admit those with delayed symptom onset. [ABSTRACT FROM AUTHOR]

Background: Accurate documentation of cardiopulmonary resuscitation (CPR) is essential. However, traditional methods, particularly handwriting, often introduce errors and increase the workload of the medical staff. This study aimed to describe the process of developing a tablet application for documenting CPR and to evaluate its accuracy in comparison with a paper-based method. Methods: We organized a multidisciplinary team of medical professionals, developers, and designers. We used a participative human-centered design (HCD) approach that consisted of discovering, defining, developing and delivering solutions. We conducted a simulation study to compare the accuracy of the CPR documentation application with that of the handwriting method, focusing on documentation completeness and temporal fidelity. We evaluated the usability of the application using a SystemUsability Scale (SUS) and semi-structured interviews. Results: We developed the "CPReCoder" in accordance with the HCD process. The study application consists of two screens: a CPR recording screen and a reporting screen. The CPR recording screen is divided into three zones: zone 1 (patient and prehospital area), zone 2 (CPR code button area) and zone 3 (time information and log area). In the simulation study, the documentation completeness of the "CPReCoder" was significantly higher than that of the handwritten record (96.8% vs. 88.1%, p < 0.001). Both approaches exhibit comparable temporal fidelity. The SUS score of the application was 87.9 points, indicating excellent usability. According to the responses in the interviews, the main benefit of CPReCoder was its ability to reduce workload. Conclusions: We described the process of creating a CPR recording application. Use of the application resulted in a more complete documentation than the handwriting method, and its usability was excellent. [ABSTRACT FROM AUTHOR]

In patients with non-small cell lung cancer (NSCLC) visiting the emergency department (ED), clinical decisions must be made based on their disease prognosis. This study aims to predict the disease outcome of patients visiting the ED for the first time after NSCLC diagnosis. This study included patients who visited the ED in 2016-2020 after being diagnosed with NSCLC in study site or within 30 days before the first outpatient clinic visit after diagnosis. Primary outcome of prediction model was 3-month mortality from the initial ED visit. We analyzed the association between outcome and each variable as a risk factor and built a prediction model using these variables. Both oncologic factors and ED-associated factors were associated with the 3-month mortality of NSCLC from the first ED visit. We also visualized the treatment trace as a sequence and utilized it in prediction model building. The areas under the receiver operating curve (AUROCs) of the prediction model of 3-month mortality from the first ED visit ranged from 0.677 (95% Confidence Interval (CI), 0.640-0.708) to 0.729 (95% CI, 0.697-0.761). This study provides the prediction model about 3-month survival in first ED visit point and identified patient and disease-related factors to predict the prognosis of patients. [ABSTRACT FROM AUTHOR]