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Arrhythmia, a frequently encountered and life-threatening cardiac disorder, can manifest as a transient or isolated event. Traditional automatic arrhythmia detection methods have predominantly relied on QRS-wave signal detection. Contemporary research has focused on the utilization of wearable devices for continuous monitoring of heart rates and rhythms through single-lead electrocardiogram (ECG), which holds the potential to promptly detect arrhythmias. However, in this study, we employed a convolutional neural network (CNN) to classify distinct arrhythmias without QRS wave detection step. The ECG data utilized in this study were sourced from the publicly accessible PhysioNet databases. Taking into account the impact of the duration of ECG signal on accuracy, this study trained one-dimensional CNN models with 5-s and 10-s segments, respectively, and compared their results. In the results, the CNN model exhibited the capability to differentiate between Normal Sinus Rhythm (NSR) and various arrhythmias, including Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Wolff-Parkinson-White syndrome (WPW), Ventricular Fibrillation (VF), Ventricular Tachycardia (VT), Ventricular Flutter (VFL), Mobitz II AV Block (MII), and Sinus Bradycardia (SB). Both 10-s and 5-s ECG segments exhibited comparable results, with an average classification accuracy of 97.31%. It reveals the feasibility of utilizing even shorter 5-s recordings for detecting arrhythmias in everyday scenarios. Detecting arrhythmias with a single lead aligns well with the practicality of wearable devices for daily use, and shorter detection times also align with their clinical utility in emergency situations.

IntroductionRib fractures are a prevalent injury among trauma patients, and accurate and timely diagnosis is crucial to mitigate associated risks. Unfortunately, missed rib fractures are common, leading to heightened morbidity and mortality rates. While more sensitive imaging modalities exist, their practicality is limited due to cost and radiation exposure. Point of care ultrasound offers an alternative but has drawbacks in terms of procedural time and operator expertise. Therefore, this study aims to explore the potential of deep convolutional neural networks (DCNNs) in identifying rib fractures on chest radiographs.MethodsWe assembled a comprehensive retrospective dataset of chest radiographs with formal image reports documenting rib fractures from a single medical center over the last five years. The DCNN models were trained using 2000 region-of-interest (ROI) slices for each category, which included fractured ribs, non-fractured ribs, and background regions. To optimize training of the deep learning models (DLMs), the images were segmented into pixel dimensions of 128 × 128.ResultsThe trained DCNN models demonstrated remarkable validation accuracies. Specifically, AlexNet achieved 92.6%, GoogLeNet achieved 92.2%, EfficientNetb3 achieved 92.3%, DenseNet201 achieved 92.4%, and MobileNetV2 achieved 91.2%.DiscussionBy integrating DCNN models capable of rib fracture recognition into clinical decision support systems, the incidence of missed rib fracture diagnoses can be significantly reduced, resulting in tangible decreases in morbidity and mortality rates among trauma patients. This innovative approach holds the potential to revolutionize the diagnosis and treatment of chest trauma, ultimately leading to improved clinical outcomes for individuals affected by these injuries. The utilization of DCNNs in rib fracture detection on chest radiographs addresses the limitations of other imaging modalities, offering a promising and practical solution to improve patient care and management.

Abstract Purpose Some predictive systems using machine learning models have been developed to predict sepsis; however, they were mostly built with a low percent of missing values, which does not correspond with the actual clinical situation. In this study, we developed a machine learning model with a high rate of missing and erroneous data to enable prediction under missing, noisy, and erroneous inputs, as in the actual clinical situation. Materials and methods The proposed artificial neural network model was implemented using the MATLAB ANN toolbox, based on stochastic gradient descent. The dataset was collected over the past decade with approval from the appropriate institutional review boards, and the sepsis status was identified and labeled using Sepsis-3 clinical criteria. The imputation method was built by last observation carried forward and mean value, aimed to simulate clinical situation. Results The mean area under the receiver operating characteristic (ROC) curve (AUC) of classifying sepsis and nonsepsis patients was 0.82 and 0.786 at 0 h and 40 h prior to onset, respectively. The highest model performance was found for one-hourly data, demonstrating that our ANN model can perform adequately with limited hourly data provided. Conclusions Our model has the moderate ability to predict sepsis up to 40 h in advance under simulated clinical situation with real-world data.

Introduction: Mortality of trauma patients is strongly associated with Injury Severity Score (ISS). However, the interaction between ISS and age has not been studied extensively. We evaluated ISS in different age groups in relation to mortality rate. Materials and methods: This was a retrospective review of 955 trauma patients who visited Mackay Memorial Hospital during January–December 2009. Age, sex, emergency department (ED) stay time, hospital stay time, out-of-hospital cardiac arrest, in-hospital cardiac arrest, ED survival rate, emergency operation, and mortality were analyzed. Patients were separated into two groups by age below or above 65 years, and hospital courses and mortality rate were compared. Results: Nine hundred and fifty-five trauma patients visited our ED in 2009: 212 in the aged group (99 male patients; 46.7%) and 743 in the young group (472 male patients; 63.5%). The mean age of the elderly group was 77±8 years (mean±standard deviation), and 38±16 years in the young group. Mean ISS was 12±12 in the elderly group and 10±13 in the young group. The mean ED stay time was 2.9±3.8 hours for the elderly group and 3.0±4.6 hours for the young group. One hundred and sixty-three patients were admitted and the mean hospital stay time was 12.7±13.6 days in the elderly group, whereas 559 patients were admitted and the mean hospital stay time was 9.9±13.7 days in the young group (p=0.018). The mortality rate among hospitalized patients was 3.1% in the elderly group and 2.9% in the young group. On receiver operating characteristic curve analysis, the optimal cut-off ISS for mortality rate among young patients (area under the curve: 0.899) and elderly patients (area under the curve: 0.782) was 17 (sensitivity: 81.3%; specificity: 88.7%) and 14 (sensitivity: 80%; specificity: 82.3%), respectively. Conclusion: The elderly group seemed to have no significant difference from the younger group for mortality rate, ED stay time and hospital stay time. In the elderly group, lower ISS predicted a higher mortality than in the younger group.

Objectives: Emergency room visits by nursing home patients with bloodstream infections (BSIs) are not uncommon and pose challenges to clinicians when selecting appropriate antimicrobial agents. This research aims to discover distinct bacteriology in nursing home patients by focusing particularly on antimicrobial-resistant bacteria. Methods: This retrospective study enrolled patients aged 65 years or older who had documented positive blood culture reports between May 2006 and June 2008. Patients were further categorized into subgroups according to nursing home history and the timely trend of hospital exposure. Results: The adjusted risk of BSIs associated with nursing home exposure was significantly higher in Methicillin-resistant Staphylococcus aureus (odds ratio (OR) 2.0; 95% CI 1.5–2.8), Methicillin-resistant coagulase-negative Staphylococcus species (OR 1.7; 95% CI 1.2–2.5), and Enterobacteriaceae-extended spectrum β-lactamase-producing Enterobacteriaceae (OR 2.4; 95% CI 1.5–3.9), but not in Pseudomonas aeruginosa (OR 1.4; 95% CI 0.9–2.2) or Acinetobacter baumannii (OR 0.9; 95% CI 0.5–1.5). Conclusion: Host environments—nursing home or hospital exposure—were shown to be more significant than medical comorbid conditions for acquiring antimicrobial-resistant pathogens. Each unique environment increased the risk for acquiring some pathogens, but not all. In addition, these environmental factors may also exert cumulative effects toward some specific pathogens.

Background: Pulmonary embolism (PE) has been associated with the risk of arterial cardiovascular events, with diagnosis being difficult in older adults. Multiple factors contribute to mortality and morbidity in older adults. Hence, we aimed to investigate the difference of subsequent cardiovascular events (SCVE) and demography between younger and older adults. Methods: We undertook a retrospective cohort study using data from a 1100-bed urban medical center in Taipei, Taiwan. All patients who presented to the emergency department with a documented diagnosis of PE (ICD-9 of 415.19), from January 2003 to December 2008, were enrolled and specified into two age groups. Clinical follow-up was conducted until December 2009, with a median time of 3.5 years. The SCVE included new onset of ischemic stroke or heart attack and recurrence of PE. The time between when a patient was presented to the ER and when the diagnosis took place was assessed. Results: A total of 104 patients were studied, among which 58 patients were placed in the elderly group (≥ 65 years old) and 46 were placed in the young group (< 65 years old). There were significantly more females in the elderly group (n=43; 74.1%; p

The article presents a case study of a 61-year-old man who was admitted to an emergency department (ED) because of syncope after soaking his feet in a solution containing 50% of saponated cresol. He was lethargic with a strong medicinal smell and brownish discolouration of his bilateral feet, on physical examination. Laboratory examination shows the presence of black urine. He was diagnosed with cresol intoxication which was managed through decontamination and supportive care.