Pneumonia, the leading reason underlying childhood deaths, may be triggered or exacerbated by air pollution. To date, only a few studies have examined the association of air pollution with emergency department (ED) visits for pediatric pneumonia, with inconsistent results. Therefore, we aimed to elucidate the impact of short-term exposure to particulate matter (PM) and other air pollutants on the incidence of ED visits for pediatric pneumonia. PM2.5, PM10, and other air pollutant levels were measured at 11 air quality-monitoring stations in Kaohsiung City, Taiwan, between 2008 and 2014. Further, we extracted the medical records of non-trauma patients aged ≤17 years and who had visited an ED with the principal diagnosis of pneumonia. A time-stratified case–crossover study design was employed to determine the hazard effect of air pollution in a total of 4024 patients. The single-pollutant model suggested that per interquartile range increment in PM2.5, PM10, nitrogen dioxide (NO2), and sulfur dioxide (SO2) on 3 days before the event increased the odds of pediatric pneumonia by 14.0% [95% confidence interval (CI), 5.1–23.8%], 10.9% (95% CI, 2.4–20.0%), 14.1% (95% CI, 5.0–24.1%), and 4.5% (95% CI, 0.8–8.4%), respectively. In two-pollutant models, PM2.5 and NO2 were significant after adjusting for PM10 and SO2. Subgroup analyses showed that older children (aged ≥4 years) were more susceptible to PM2.5 (interaction p = 0.024) and children were more susceptible to NO2 during warm days (≥26.5 °C, interaction p = 0.011). Short-term exposure to PM2.5 and NO2 possibly plays an important role in pediatric pneumonia in Kaohsiung, Taiwan. Older children are more susceptible to PM2.5, and all children are more susceptible to NO2 during warm days.

Background. Out‐of‐hospital cardiac arrest (OHCA) is a major health problem worldwide, and neurologic injury remains the leading cause of morbidity and mortality among survivors of OHCA. The purpose of this study was to investigate whether a machine learning algorithm could detect complex dependencies between clinical variables in emergency departments in OHCA survivors and perform reliable predictions of favorable neurologic outcomes. Methods. This study included adults (≥18 years of age) with a sustained return of spontaneous circulation after successful resuscitation from OHCA between 1 January 2004 and 31 December 2014. We applied three machine learning algorithms, including logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGB). The primary outcome was a favorable neurological outcome at hospital discharge, defined as a Glasgow‐Pittsburgh cerebral performance category of 1 to 2. The secondary outcome was a 30‐day survival rate and survival‐to‐discharge rate. Results. The final analysis included 1071 participants from the study period. For neurologic outcome prediction, the area under the receiver operating curve (AUC) was 0.819, 0.771, and 0.956 in LR, SVM, and XGB, respectively. The sensitivity and specificity were 0.875 and 0.751 in LR, 0.687 and 0.793 in SVM, and 0.875 and 0.904 in XGB. The AUC was 0.766 and 0.732 in LR, 0.749 and 0.725 in SVM, and 0.866 and 0.831 in XGB, for survival‐to‐discharge and 30‐day survival, respectively. Conclusions. Prognostic models trained with ML technique showed appropriate calibration and high discrimination for survival and neurologic outcome of OHCA without using prehospital data, with XGB exhibiting the best performance.

Background: Global asthma-related mortality tallies at around 2.5 million annually. Although asthma may be triggered or exacerbated by particulate matter (PM) exposure, studies investigating the relationship of PM and its components with emergency department (ED) visits for pediatric asthma are limited. This study aimed to estimate the impact of short-term exposure to PM constituents on ED visits for pediatric asthma. Methods: We retrospectively evaluated non-trauma patients aged younger than 17 years who visited the ED with a primary diagnosis of asthma. Further, measurements of PM with aerodynamic diameter of < 10 μm (PM₁₀), PM with aerodynamic diameter of < 10 μm (PM_2.5), and four PM_2.5 components (i.e., nitrate (NO³⁻), sulfate (SO₄²⁻), organic carbon (OC), and elemental carbon (EC)) were collected between 2007 and 2010 from southern particulate matter supersites. These included one core station and two satellite stations in Kaohsiung City, Taiwan. A time-stratified case-crossover study was conducted to analyze the hazard effect of PM. Results: Overall, 1597 patients were enrolled in our study. In the single-pollutant model, the estimated risk increase for pediatric asthma incidence on lag 3 were 14.7% [95% confidence interval (CI), 3.2–27.4%], 13.5% (95% CI, 3.3–24.6%), 14.8% (95% CI, 2.5–28.6%), and 19.8% (95% CI, 7.6–33.3%) per interquartile range increments in PM_2.5, PM₁₀, nitrate, and OC, respectively. In the two-pollutant models, OC remained significant after adjusting for PM_2.5, PM₁₀, and nitrate. During subgroup analysis, children were more vulnerable to PM_2.5 and OC during cold days (< 26 °C, interaction p = 0.008 and 0.012, respectively). Conclusions: Both PM_2.5 concentrations and its chemical constituents OC and nitrate are associated with ED visits for pediatric asthma. Among PM_2.5 constituents, OC was most closely related to ED visits for pediatric asthma, and children are more vulnerable to PM_2.5 and OC during cold days.

Focused Assessment with Sonography in Trauma (FAST) could play a role in reduced time to operative care, shortened hospital length of stay, and lower medical costs.¹ In a 2005 study, using prehospital FAST was associated with changes to the admitting hospital in 22% of patients.^2,3 Ultrasonography is highly operator-dependent, and the FAST examination is considered to be among the hardest protocols for image interpretation.⁴ Thus, this study aimed to examine the association between artificial intelligence (AI) guidance and quality of FAST performed by novice ultrasonography operators.

Blood culture is frequently used to detect bacteremia in febrile children. However, a high rate of negative or false-positive blood culture results is common at the pediatric emergency department (PED). The aim of this study was to use machine learning to build a model that could predict bacteremia in febrile children. We conducted a retrospective case-control study of febrile children who presented to the PED from 2008 to 2015. We adopted machine learning methods and cost-sensitive learning to establish a predictive model of bacteremia. We enrolled 16,967 febrile children with blood culture tests during the eight-year study period. Only 146 febrile children had true bacteremia, and more than 99% of febrile children had a contaminant or negative blood culture result. The maximum area under the curve of logistic regression and support vector machines to predict bacteremia were 0.768 and 0.832, respectively. Using the predictive model, we can categorize febrile children by risk value into five classes. Class 5 had the highest probability of having bacteremia, while class 1 had no risk. Obtaining blood cultures in febrile children at the PED rarely identifies a causative pathogen. Prediction models can help physicians determine whether patients have bacteremia and may reduce unnecessary expenses.

Blood glucose (BG) monitoring is important for critically ill patients, as poor sugar control has been associated with increased mortality in hospitalized patients. However, constant BG monitoring can be resource-intensive and pose a healthcare burden in clinical practice. In this study, we aimed to develop a personalized machine-learning model to predict dysglycemia from electrocardiogram (ECG) data. We used the Medical Information Mart for Intensive Care III database as our source of data and obtained more than 20 ECG records from each included patient during a single hospital admission. We focused on lead II recordings, along with corresponding blood sugar data. We processed the data and used ECG features from each heartbeat as inputs to develop a one-class support vector machine algorithm to predict dysglycemia. The model was able to predict dysglycemia using a single heartbeat with an AUC of 0.92 ± 0.09, a sensitivity of 0.92 ± 0.10, and specificity of 0.84 ± 0.04. After applying 10 s majority voting, the AUC of the model’s dysglycemia prediction increased to 0.97 ± 0.06. This study showed that a personalized machine-learning algorithm can accurately detect dysglycemia from a single-lead ECG.