Using Machine Learning to Risk Stratify Emergency Department Patients With Chest Pain but No Acute Myocardial Infarction: A Multicenter Retrospective Analysis.

Background: This study aimed to develop a machine learning-based model to predict the risk of major adverse cardiac events (MACE) in patients presenting to the emergency department (ED) with chest pain, for whom acute myocardial infarction was excluded after serial high-sensitivity cardiac troponin testing.

Methods: This retrospective analysis included adult patients presenting with chest pain at 5 study hospitals between 2021 and 2024 in Texas. Patients diagnosed with acute myocardial infarction during the index visit were excluded.

Clinical and Economic Evaluation of a Real-Time Chest X-Ray Computer-Aided Detection System for Misplaced Endotracheal and Nasogastric Tubes and Pneumothorax in Emergency and Critical Care Settings: Protocol for a Cluster Randomized Controlled Trial.

Background: Advancements in artificial intelligence (AI) have driven substantial breakthroughs in computer-aided detection (CAD) for chest x-ray (CXR) imaging. The National Taiwan University Hospital research team previously developed an AI-based emergency CXR system (Capstone project), which led to the creation of a CXR module. This CXR module has an established model supported by extensive research and is ready for application in clinical trials without requiring additional model training.

The Effect of Pain on the Relationship Between Triage Acuity and Emergency Department Hospitalization Rate and Length of Stay.

Objectives: Little is known about the effect of pain on the relationship between triage and patient outcomes in United States emergency departments (ED). In this study we aimed to describe pain-associated ED visits and to explore how pain modifies the ability of ED triage to predict patient outcomes (hospitalization and ED length of stay [EDLOS)].

Methods: We obtained data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), 2010-2021.

The Prognostic Accuracy of Frailty and Vulnerability Screening for Older Adults in the Emergency Department: A Systematic Review and Meta-analysis.

Objective: Frailty and vulnerability are associated with increased morbidity and mortality in older adults, yet the optimal screening tool for predicting adverse outcomes in the emergency department (ED) remains unclear. Our question is: Which frailty or vulnerability screening instrument has the highest prognostic accuracy for adverse outcomes in older adults visiting the ED?

Methods: We included observational studies involving patients aged more than or equal to 60 years presenting to the ED that applied frailty or vulnerability instruments and reported sensitivity, specificity, and area under the curve (AUC). We searched MEDLINE, EMBASE, Cochrane Library, CINAHL, and CNKI through January 2025.

Developing Machine-Learning Models to Predict Bacteremia in Febrile Adults Presenting to the Emergency Department: A Retrospective Cohort Study from a Large Center.

Introduction: Bacteremia, a common disease but difficult to diagnose early, may result in significant morbidity and mortality without prompt treatment. We aimed to develop machine-learning (ML) algorithms to predict patients with bacteremia from febrile patients presenting to the emergency department (ED) using data that is readily available at the triage.

Methods: We included all adult patients (≥18 years of age) who presented to the emergency department (ED) of National Taiwan University Hospital (NTUH), a tertiary teaching hospital in Taiwan, with the chief complaint of fever or measured body temperature more than 38°C, and who received at least one blood culture during the ED encounter.

Comparison of neuroprognostic performance between manually and automatically computed gray-white matter ratios on brain computed tomography following cardiac arrest: A systematic review and meta-analysis.

Aim: To compare the prognostic accuracy of manually (mGWR) and automatically (aGWR) computed gray-white matter ratios on brain computed tomography for predicting neurological outcomes in adult post-cardiac arrest patients.

Methods: We systematically searched the PubMed and Embase databases from their inception to August 2024. Studies providing sufficient data on mGWR or aGWR to predict neurological outcomes in adult post-cardiac arrest patients were selected.

Deep Learning-Based Instance Appraisable Model (EDi Pain) for Pain Estimation via Facial Videos: A Retrospective Analysis and a Prospective Emergency Department Study.

Pain assessment is a critical aspect of medical care, yet automated systems for clinical pain estimation remain rare. Tools such as the visual analog scale (VAS) are commonly used in emergency departments (EDs) but rely on subjective self-reporting, with pain intensity often fluctuating during triage. An effective automated system should utilize objective labels from healthcare professionals and identify key frames from video sequences for accurate inference.

Factors associated with overall and high-risk return visits to the emergency department: a vital sign trajectory approach.

Background: For patients and emergency department (ED) physicians, return visits to the ED represent a potentially detrimental issue. In this study, our goal was to examine factors associated with overall and high-risk ED revisits. Specifically, as vital signs during the ED stay may provide important clues for subsequent revisits, we also examined the association between vital sign trajectories and post-ED revisits.

Population Attributable Fraction of Diabetes on the Risk of Gastrointestinal Cancers: Overall and by Specific Cancer Sites in a Cohort Study.

Background: This study aims to assess the population attributable fraction (PAF) of diabetes on the gastrointestinal cancers overall and by specific cancer sites.

Methods: This study analyzed healthcare data from Taiwan (2006-2019) for 2,362,587 patients with and without diabetes. Gastrointestinal cancers were identified via cancer registry data.

Association between post-arrest 12-lead electrocardiographic features and neurologically intact survival for patients of in-hospital cardiac arrest.

Twelve-lead electrocardiogram (ECG) may provide prognostic information for in-hospital cardiac arrest (IHCA). This study aimed to identify post-arrest ECG features and their temporal changes associated with IHCA outcomes. This single-center retrospective study included patients experiencing IHCA between 2005 and 2022.

Deep Learning-Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study.

Background: In-hospital cardiac arrest (IHCA) is a severe and sudden medical emergency that is characterized by the abrupt cessation of circulatory function, leading to death or irreversible organ damage if not addressed immediately. Emergency department (ED)-based IHCA (EDCA) accounts for 10% to 20% of all IHCA cases. Early detection of EDCA is crucial, yet identifying subtle signs of cardiac deterioration is challenging.

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.

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

Background: During cardiopulmonary resuscitation (CPR), end-tidal carbon dioxide (EtCO) is primarily determined by pulmonary blood flow, thereby reflecting the blood flow generated by CPR. We aimed to develop an EtCO 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.

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.

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.

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 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.

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.

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.

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.