An Underrecognized Cause of Recurrent Nocturnal Syncope with a Pacemaker: Sleep Rate Mode.

BACKGROUND: Nocturnal syncope is a common presentation in the emergency department, often related to orthostatic hypotension and subsequent loss of cerebral perfusion when patients get up from sleep to use the restroom faster than their cardiac output and vascular tone can accommodate. Poor hydration status and antihypertensive medications can increase this risk. Patients with syncope who present to the emergency department with a pacemaker are usually evaluated with a pacemaker interrogation to evaluate for runs of nonperfusing rhythms (e.g., ventricular tachycardia or fibrillation). Sleep rate mode (SRM) is a relatively new feature of modern pacemakers and is not currently recognized by emergency physicians. It was implemented to accommodate more physiologic fluctuations in heart rate during rapid eye movement sleep. There is a paucity of evidence supporting the clinical benefit of SRM and similarly no documentation of prior complications of SRM in the current literature. CASE REPORT: We report the case of a 92-year-old woman with a Medtronic Avisa pacemaker presenting with recurrent nocturnal syncope and bradycardia resulting in multiple emergency department visits. These episodes ultimately resolved by turning off SRM on her pacemaker. WHY SHOULD AN EMERGENCY PHYSICIAN BE AWARE OF THIS?: SRM is not currently flagged on interrogation report summaries provided to emergency physicians. This report highlights the importance of recognizing this mode as a potential etiology of nocturnal syncope related to chronotropic incompetence in patients with pacemakers.

Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department.

Clinicians in the emergency department (ED) face challenges in concurrently assessing patients with suspected COVID-19 infection, detecting bacterial coinfection, and determining illness severity since current practices require separate workflows. Here, we explore the accuracy of the IMX-BVN-3/IMX-SEV-3 29 mRNA host response classifiers in simultaneously detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and bacterial coinfections and predicting clinical severity of COVID-19. A total of 161 patients with PCR-confirmed COVID-19 (52.2% female; median age, 50.0 years; 51% hospitalized; 5.6% deaths) were enrolled at the Stanford Hospital ED. RNA was extracted (2.5 mL whole blood in PAXgene blood RNA), and 29 host mRNAs in response to the infection were quantified using Nanostring nCounter. The IMX-BVN-3 classifier identified SARS-CoV-2 infection in 151 patients with a sensitivity of 93.8%. Six of 10 patients undetected by the classifier had positive COVID tests more than 9 days prior to enrollment, and the remaining patients oscillated between positive and negative results in subsequent tests. The classifier also predicted that 6 (3.7%) patients had a bacterial coinfection. Clinical adjudication confirmed that 5/6 (83.3%) of the patients had bacterial infections, i.e., Clostridioides difficile colitis (n = 1), urinary tract infection (n = 1), and clinically diagnosed bacterial infections (n = 3), for a specificity of 99.4%. Two of 101 (2.8%) patients in the IMX-SEV-3 "Low" severity classification and 7/60 (11.7%) in the "Moderate" severity classification died within 30 days of enrollment. IMX-BVN-3/IMX-SEV-3 classifiers accurately identified patients with COVID-19 and bacterial coinfections and predicted patients' risk of death. A point-of-care version of these classifiers, under development, could improve ED patient management, including more accurate treatment decisions and optimized resource utilization. IMPORTANCE We assay the utility of the single-test IMX-BVN-3/IMX-SEV-3 classifiers that require just 2.5 mL of patient blood in concurrently detecting viral and bacterial infections as well as predicting the severity and 30-day outcome from the infection. A point-of-care device, in development, will circumvent the need for blood culturing and drastically reduce the time needed to detect an infection. This will negate the need for empirical use of broad-spectrum antibiotics and allow for antibiotic use stewardship. Additionally, accurate classification of the severity of infection and the prediction of 30-day severe outcomes will allow for appropriate allocation of hospital resources.

Transfer learning enables prediction of myocardial injury from continuous single-lead electrocardiography.

OBJECTIVE: Chest pain is common, and current risk-stratification methods, requiring 12-lead electrocardiograms (ECGs) and serial biomarker assays, are static and restricted to highly resourced settings. Our objective was to predict myocardial injury using continuous single-lead ECG waveforms similar to those obtained from wearable devices and to evaluate the potential of transfer learning from labeled 12-lead ECGs to improve these predictions. METHODS: We studied 10 874 Emergency Department (ED) patients who received continuous ECG monitoring and troponin testing from 2020 to 2021. We defined myocardial injury as newly elevated troponin in patients with chest pain or shortness of breath. We developed deep learning models of myocardial injury using continuous lead II ECG from bedside monitors as well as conventional 12-lead ECGs from triage. We pretrained single-lead models on a pre-existing corpus of labeled 12-lead ECGs. We compared model predictions to those of ED physicians. RESULTS: A transfer learning strategy, whereby models for continuous single-lead ECGs were first pretrained on 12-lead ECGs from a separate cohort, predicted myocardial injury as accurately as models using patients' own 12-lead ECGs: area under the receiver operating characteristic curve 0.760 (95% confidence interval [CI], 0.721-0.799) and area under the precision-recall curve 0.321 (95% CI, 0.251-0.397). Models demonstrated a high negative predictive value for myocardial injury among patients with chest pain or shortness of breath, exceeding the predictive performance of ED physicians, while attending to known stigmata of myocardial injury. CONCLUSIONS: Deep learning models pretrained on labeled 12-lead ECGs can predict myocardial injury from noisy, continuous monitor data early in a patient's presentation. The utility of continuous single-lead ECG in the risk stratification of chest pain has implications for wearable devices and preclinical settings, where external validation of the approach is needed.

Detection of bacterial co-infections and prediction of fatal outcomes in COVID-19 patients presenting to the emergency department using a 29 mRNA host response classifier.

Objective: Clinicians in the emergency department (ED) face challenges in concurrently assessing patients with suspected COVID-19 infection, detecting bacterial co-infection, and determining illness severity since current practices require separate workflows. Here we explore the accuracy of the IMX-BVN-3/IMX-SEV-3 29 mRNA host response classifiers in simultaneously detecting SARS-CoV-2 infection, bacterial co-infections, and predicting clinical severity of COVID-19. Methods: 161 patients with PCR-confirmed COVID-19 (52.2% female, median age 50.0 years, 51% hospitalized, 5.6% deaths) were enrolled at the Stanford Hospital ED. RNA was extracted (2.5 mL whole blood in PAXgene Blood RNA) and 29 host mRNAs in response to the infection were quantified using Nanostring nCounter. Results: The IMX-BVN-3 classifier identified SARS-CoV-2 infection in 151 patients with a sensitivity of 93.8%. Six of 10 patients undetected by the classifier had positive COVID tests more than 9 days prior to enrolment and the remaining oscillated between positive and negative results in subsequent tests. The classifier also predicted that 6 (3.7%) patients had a bacterial co-infection. Clinical adjudication confirmed that 5/6 (83.3%) of the patients had bacterial infections, i.e. Clostridioides difficile colitis (n=1), urinary tract infection (n=1), and clinically diagnosed bacterial infections (n=3) for a specificity of 99.4%. 2/101 (2.8%) patients in the IMX-SEV-3 Low and 7/60 (11.7%) in the Moderate severity classifications died within thirty days of enrollment. Conclusions: IMX-BVN-3/IMX-SEV-3 classifiers accurately identified patients with COVID-19, bacterial co-infections, and predicted patients’ risk of death. A point-of-care version of these classifiers, under development, could improve ED patient management including more accurate treatment decisions and optimized resource utilization.

SARS-CoV-2 RNAemia Predicts Clinical Deterioration and Extrapulmonary Complications from COVID-19.

BACKGROUND: The determinants of coronavirus disease 2019 (COVID-19) disease severity and extrapulmonary complications (EPCs) are poorly understood. We characterized relationships between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNAemia and disease severity, clinical deterioration, and specific EPCs. METHODS: We used quantitative and digital polymerase chain reaction (qPCR and dPCR) to quantify SARS-CoV-2 RNA from plasma in 191 patients presenting to the emergency department with COVID-19. We recorded patient symptoms, laboratory markers, and clinical outcomes, with a focus on oxygen requirements over time. We collected longitudinal plasma samples from a subset of patients. We characterized the role of RNAemia in predicting clinical severity and EPCs using elastic net regression. RESULTS: Of SARS-CoV-2-positive patients, 23.0% (44 of 191) had viral RNA detected in plasma by dPCR, compared with 1.4% (2 of 147) by qPCR. Most patients with serial measurements had undetectable RNAemia within 10 days of symptom onset, reached maximum clinical severity within 16 days, and symptom resolution within 33 days. Initially RNAemic patients were more likely to manifest severe disease (odds ratio, 6.72 [95% confidence interval, 2.45-19.79]), worsening of disease severity (2.43 [1.07-5.38]), and EPCs (2.81 [1.26-6.36]). RNA loads were correlated with maximum severity (r = 0.47 [95% confidence interval, .20-.67]). CONCLUSIONS: dPCR is more sensitive than qPCR for the detection of SARS-CoV-2 RNAemia, which is a robust predictor of eventual COVID-19 severity and oxygen requirements, as well as EPCs. Because many COVID-19 therapies are initiated on the basis of oxygen requirements, RNAemia on presentation might serve to direct early initiation of appropriate therapies for the patients most likely to deteriorate.

Syncope Time Frames for Adverse Events after Emergency Department Presentation: An Individual Patient Data Meta-Analysis.

Background and Objectives: Knowledge of the incidence and time frames of the adverse events of patients presenting syncope at the ED is essential for developing effective management strategies. The aim of the present study was to perform a meta-analysis of the incidence and time frames of adverse events of syncope patients. Materials and Methods: We combined individual patients' data from prospective observational studies including adult patients who presented syncope at the ED. We assessed the pooled rate of adverse events at 24 h, 72 h, 7-10 days, 1 month and 1 year after ED evaluation. Results: We included nine studies that enrolled 12,269 patients. The mean age varied between 53 and 73 years, with 42% to 57% females. The pooled rate of adverse events was 5.1% (95% CI 3.4% to 7.7%) at 24 h, 7.0% (95% CI 4.9% to 9.9%) at 72 h, 8.4% (95% CI 6.2% to 11.3%) at 7-10 days, 10.3% (95% CI 7.8% to 13.3%) at 1 month and 21.3% (95% CI 15.8% to 28.0%) at 1 year. The pooled death rate was 0.2% (95% CI 0.1% to 0.5%) at 24 h, 0.3% (95% CI 0.1% to 0.7%) at 72 h, 0.5% (95% CI 0.3% to 0.9%) at 7-10 days, 1% (95% CI 0.6% to 1.7%) at 1 month and 5.9% (95% CI 4.5% to 7.7%) at 1 year. The most common adverse event was arrhythmia, for which its rate was 3.1% (95% CI 2.0% to 4.9%) at 24 h, 4.8% (95% CI 3.5% to 6.7%) at 72 h, 5.8% (95% CI 4.2% to 7.9%) at 7-10 days, 6.9% (95% CI 5.3% to 9.1%) at 1 month and 9.9% (95% CI 5.5% to 17) at 1 year. Ventricular arrhythmia was rare. Conclusions: The risk of death or life-threatening adverse event is rare in patients presenting syncope at the ED. The most common adverse events are brady and supraventricular arrhythmias, which occur during the first 3 days. Prolonged ECG monitoring in the ED in a short stay unit with ECG monitoring facilities may, therefore, be beneficial.

Correction: Personalized risk stratification through attribute matching for clinical decision making in clinical conditions with aspecific symptoms: The example of syncope.

[This corrects the article DOI: 10.1371/journal.pone.0228725.].

Personalized risk stratification through attribute matching for clinical decision making in clinical conditions with aspecific symptoms: The example of syncope.

BACKGROUND: Risk stratification is challenging in conditions, such as chest pain, shortness of breath and syncope, which can be the manifestation of many possible underlying diseases. In these cases, decision tools are unlikely to accurately identify all the different adverse events related to the possible etiologies. Attribute matching is a prediction method that matches an individual patient to a group of previously observed patients with identical characteristics and known outcome. We used syncope as a paradigm of clinical conditions presenting with aspecific symptoms to test the attribute matching method for the prediction of the personalized risk of adverse events. METHODS: We selected the 8 predictor variables common to the individual-patient dataset of 5 prospective emergency department studies enrolling 3388 syncope patients. We calculated all possible combinations and the number of patients in each combination. We compared the predictive accuracy of attribute matching and logistic regression. We then classified ten random patients according to clinical judgment and attribute matching. RESULTS: Attribute matching provided 253 of the 384 possible combinations in the dataset. Twelve (4.7%), 35 (13.8%), 50 (19.8%) and 160 (63.2%) combinations had a match size ≥50, ≥30, ≥20 and <10 patients, respectively. The AUC for the attribute matching and the multivariate model were 0.59 and 0.74, respectively. CONCLUSIONS: Attribute matching is a promising tool for personalized and flexible risk prediction. Large databases will need to be used in future studies to test and apply the method in different conditions.

SARS-CoV-2 RNAaemia predicts clinical deterioration and extrapulmonary complications from COVID-19.

Background: The determinants of COVID-19 disease severity and extrapulmonary complications (EPCs) are poorly understood. We characterise the relationships between SARS-CoV-2 RNAaemia and disease severity, clinical deterioration, and specific EPCs. Methods: We used quantitative (qPCR) and digital (dPCR) PCR to quantify SARS-CoV-2 RNA from nasopharyngeal swabs and plasma in 191 patients presenting to the Emergency Department (ED) with COVID-19. We recorded patient symptoms, laboratory markers, and clinical outcomes, with a focus on oxygen requirements over time. We collected longitudinal plasma samples from a subset of patients. We characterised the role of RNAaemia in predicting clinical severity and EPCs using elastic net regression. Findings: 23·0% (44/191) of SARS-CoV-2 positive patients had viral RNA detected in plasma by dPCR, compared to 1·4% (2/147) by qPCR. Most patients with serial measurements had undetectable RNAaemia 10 days after onset of symptoms, but took 16 days to reach maximum severity, and 33 days for symptoms to resolve. Initially RNAaemic patients were more likely to manifest severe disease (OR 6·72 [95% CI, 2·45 - 19·79]), worsening of disease severity (OR 2·43 [95% CI, 1·07 - 5·38]), and EPCs (OR 2·81 [95% CI, 1·26 - 6·36]). RNA load correlated with maximum severity (r = 0·47 [95% CI, 0·20 - 0·67]). Interpretation: dPCR is more sensitive than qPCR for the detection of SARS-CoV-2 RNAaemia, which is a robust predictor of eventual COVID-19 severity and oxygen requirements, as well as EPCs. Since many COVID-19 therapies are initiated on the basis of oxygen requirements, RNAaemia on presentation might serve to direct early initiation of appropriate therapies for the patients most likely to deteriorate.

Factors Associated With Influenza in an Emergency Department Setting.

BACKGROUND: Emergency departments (EDs) become more overcrowded during peak respiratory virus season. Distinguishing influenza from other viruses is crucial to implement social distancing practices, early treatment, and prompt disposition. OBJECTIVES: We sought to determine factors associated with influenza among a prospective cohort of consecutive ED patients with acute respiratory illness (ARI). METHODS: Between December 2016 and March 2017, trained research assistants screened consecutive ED patients with ARI symptoms. ARI criteria included measured fever at home or in the ED >38°C and a cough, sore throat, or rhinorrhea with a duration of symptoms >12 hours and <1 week. After consent, research assistants collected demographics and clinical history using a standardized data form, and patients had a polymerase chain reaction-based assay that is nearly 100% sensitive for influenza. Univariate analysis was conducted on all predictor variables. Significant variables were entered into a multivariate logistic regression model to find factors that were independently associated with influenza. RESULTS: One hundred nineteen patients consented to enrollment and 31% were found to be positive for influenza. Myalgia, the absence of gastrointestinal symptoms (no diarrhea or vomiting), sore throat, chills, headache, and oxygen saturation ≥97% were significant on univariate analysis and were entered into the multivariate model. Myalgia (adjusted odds ratio [AOR] 3.9), the absence of gastrointestinal symptoms (AOR 4.7), and oxygen saturation ≥97% (AOR 2.8) were significant independent factors of influenza. CONCLUSION: The presence of myalgia, the absence of gastrointestinal symptoms, and oxygen saturation ≥97% are factors that can help distinguish influenza from other acute respiratory illnesses in the ambulatory ED population.

Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease.

Thousands of pathogens are known to infect humans, but only a fraction are readily identifiable using current diagnostic methods. Microbial cell-free DNA sequencing offers the potential to non-invasively identify a wide range of infections throughout the body, but the challenges of clinical-grade metagenomic testing must be addressed. Here we describe the analytical and clinical validation of a next-generation sequencing test that identifies and quantifies microbial cell-free DNA in plasma from 1,250 clinically relevant bacteria, DNA viruses, fungi and eukaryotic parasites. Test accuracy, precision, bias and robustness to a number of metagenomics-specific challenges were determined using a panel of 13 microorganisms that model key determinants of performance in 358 contrived plasma samples, as well as 2,625 infections simulated in silico and 580 clinical study samples. The test showed 93.7% agreement with blood culture in a cohort of 350 patients with a sepsis alert and identified an independently adjudicated cause of the sepsis alert more often than all of the microbiological testing combined (169 aetiological determinations versus 132). Among the 166 samples adjudicated to have no sepsis aetiology identified by any of the tested methods, sequencing identified microbial cell-free DNA in 62, likely derived from commensal organisms and incidental findings unrelated to the sepsis alert. Analysis of the first 2,000 patient samples tested in the CLIA laboratory showed that more than 85% of results were delivered the day after sample receipt, with 53.7% of reports identifying one or more microorganisms.

Artificial neural networks and risk stratification in emergency departments.

Emergency departments are characterized by the need for quick diagnosis under pressure. To select the most appropriate treatment, a series of rules to support decision-making has been offered by scientific societies. The effectiveness of these rules affects the appropriateness of treatment and the hospitalization of patients. Analyzing a sample of 1844 patients and focusing on the decision to hospitalize a patient after a syncope event to prevent severe short-term outcomes, this work proposes a new algorithm based on neural networks. Artificial neural networks are a non-parametric technique with the well-known ability to generalize behaviors, and they can thus predict severe short-term outcomes with pre-selected levels of sensitivity and specificity. This innovative technique can outperform the traditional models, since it does not require a specific functional form, i.e., the data are not supposed to be distributed following a specific design. Based on our results, the innovative model can predict hospitalization with a sensitivity of 100% and a specificity of 79%, significantly increasing the appropriateness of medical treatment and, as a result, hospital efficiency. According to Garson's Indexes, the most significant variables are exertion, the absence of symptoms, and the patient's gender. On the contrary, cardio-vascular history, hypertension, and age have the lowest impact on the determination of the subject's health status. The main application of this new technology is the adoption of smart solutions (e.g., a mobile app) to customize the stratification of patients admitted to emergency departments (ED)s after a syncope event. Indeed, the adoption of these smart solutions gives the opportunity to customize risk stratification according to the specific clinical case (i.e., the patient's health status) and the physician's decision-making process (i.e., the desired levels of sensitivity and specificity). Moreover, a decision-making process based on these smart solutions might ensure a more effective use of available resources, improving the management of syncope patients and reducing the cost of inappropriate treatment and hospitalization.

Using a novel rapid viral test to improve triage of emergency department patients with acute respiratory illness during flu season.

BACKGROUND: Acute respiratory illnesses (ARI) are mostly viral in etiology and cause significant morbidity and mortality. Point of care PCR (POC-PCR) is a promising new technology for rapid virus identification but utility in the Emergency Department (ED) is not yet defined. OBJECTIVES: Primarily, to investigate the value of POC-PCR in rapidly identifying RSV and influenza in the setting of ED triage. Additionally, to assess whether rapid knowledge of accurate test results would improve patient management by preventing nosocomial transmission and optimizing the prescription of antimicrobials for ARIs. STUDY DESIGN: A prospective cohort study of consecutive ED patients with ARI symptoms during peak flu season was conducted. Patient nasopharyngeal swabs were collected and tested using a POC-PCR device; physicians and patients were blinded to results. Virus positive and negative groups were compared by ED patient room placement and antimicrobial therapy ordered. Specificity and sensitivity were calculated using laboratory-PCR as the gold standard. RESULTS: Of 119 participants, 52.9% were POC-PCR positive - Influenza A (42.9%), RSV (41.3%), influenza B (15.9%). Nearly 70% of virus positive patients were placed rooms shared with non-ARI patients. Antibiotics were prescribed for 27.3% of virus positive patients, and 77.8% of oseltamivir-eligible patients did not receive therapy. POC-PCR was 100% sensitive (95% CI, 80.5-100.0%) and 95.2% specific (95% CI, 76.2-99.9%). CONCLUSIONS: Rapid POC-PCR for influenza and RSV in ED triage has excellent sensitivity and specificity and the potential to improve social distancing practices through better triage and increase appropriate prescription of antimicrobials.

Outcomes in syncope research: a systematic review and critical appraisal.

Syncope is the common clinical manifestation of different diseases, and this makes it difficult to define what outcomes should be considered in prognostic studies. The aim of this study is to critically analyze the outcomes considered in syncope studies through systematic review and expert consensus. We performed a systematic review of the literature to identify prospective studies enrolling consecutive patients presenting to the Emergency Department with syncope, with data on the characteristics and incidence of short-term outcomes. Then, the strengths and weaknesses of each outcome were discussed by international syncope experts to provide practical advice to improve future selection and assessment. 31 studies met our inclusion criteria. There is a high heterogeneity in both outcome choice and incidence between the included studies. The most commonly considered 7-day outcomes are mortality, dysrhythmias, myocardial infarction, stroke, and rehospitalisation. The most commonly considered 30-day outcomes are mortality, haemorrhage requiring blood transfusion, dysrhythmias, myocardial infarction, pacemaker or implantable defibrillator implantation, stroke, pulmonary embolism, and syncope relapse. We present a critical analysis of the pros and cons of the commonly considered outcomes, and provide possible solutions to improve their choice in ED syncope studies. We also support global initiatives to promote the standardization of patient management and data collection.

Impact of Rapid Molecular Respiratory Virus Testing on Real-Time Decision Making in a Pediatric Emergency Department.

Acute respiratory illnesses (ARIs) are usually viral [influenza, respiratory syncytial virus (RSV)] and account for 25% of emergency department (ED) peak-season visits. Laboratory PCR testing is accurate albeit slow, whereas rapid antigen testing is inaccurate. We determined the impact of bedside PCR (molecular point-of-care test; mPOCT) on pediatric ARI management. This was a prospective cohort study of consecutive pediatric patients with ED-ordered respiratory PCR test, enrolled over 9 weeks during peak flu season. On ordering, ED physicians were interviewed to ascertain real-time plans if given immediate influenza/RSV PCR results for the current patient. Two groups were compared: actual management and management adjusted for mPOCT results. We compared ED length of stay (LOS), tests ordered, and antibiotic/antiviral ordering. One-hundred thirty-six respiratory PCR panels were ordered, 71 by admitting team, 61 for ED management. Of 61 ED-initiated tests, physicians indicated in 39 cases (64%) they would change patient management were bedside viral results available. Physicians would have decreased ED LOS by 33 minutes, ordered fewer tests (18%; P < 0.001) with average patient charge savings of $669, fewer antibiotics among discharged patients (17%; P = 0.043), and increased appropriate antiviral use (13%; P = 0.023). Rapid bedside ARI mPOCT PCR has the potential to decrease ED LOS, reduce diagnostic tests and patient charges, and increase appropriate use of antibiotics and antiviral agents.

Predictors of Nondiagnostic Ultrasound for Appendicitis.

BACKGROUND: Ionizing radiation and cost make ultrasound (US), when available, the first imaging study for the diagnosis of suspected pediatric appendicitis. US is less sensitive and specific than computed tomography (CT) or magnetic resonance imaging (MRI) scans, which are often performed after nondiagnostic US. OBJECTIVES: We sought to determine predictors of nondiagnostic US in order to guide efficient ordering of imaging studies. METHODS: A prospective cohort study of consecutive patients 4 to 30 years of age with suspected appendicitis took place at an emergency department with access to 24/7 US, MRI, and CT capabilities. Patients with US as their initial study were identified. Clinical (i.e., duration of illness, highest fever, and right lower quadrant pain) and demographic (i.e., age and sex) variables were collected. Body mass index (BMI) was calculated based on Centers for Disease Control and Prevention criteria; BMI >85th percentile was categorized as overweight. Patients were followed until day 7. Univariate and stepwise multivariate logistic regression analysis was performed. RESULTS: Over 3 months, 106 patients had US first for suspected appendicitis; 52 (49%) had nondiagnostic US results. Eighteen patients had appendicitis, and there were no missed cases after discharge. On univariate analysis, male sex, a yearly increase in age, and overweight BMI were associated with nondiagnostic US (p < 0.05). In the multivariate model, only BMI (odds ratio 4.9 [95% CI 2.0-12.2]) and age (odds ratio 1.1 [95% CI 1.02-1.20]) were predictors. Sixty-eight percent of nondiagnostic US results occurred in overweight patients. CONCLUSION: Overweight and older patients are more likely to have a nondiagnostic US or appendicitis, and it may be more efficient to consider alternatives to US first for these patients. Also, this information about the accuracy of US to diagnose suspected appendicitis may be useful to clinicians who wish to engage in shared decision-making with the parents or guardians of children regarding imaging options for children with acute abdominal pain.

Neural networks as a tool to predict syncope risk in the Emergency Department.

AIMS: There is no universally accepted tool for the risk stratification of syncope patients in the Emergency Department. The aim of this study was to investigate the short-term predictive accuracy of an artificial neural network (ANN) in stratifying the risk in this patient group. METHODS AND RESULTS: We analysed individual level data from three prospective studies, with a cumulative sample size of 1844 subjects. Each dataset was reanalysed to reduce the heterogeneity among studies defining abnormal electrocardiogram (ECG) and serious outcomes according to a previous consensus. Ten variables from patient history, ECG, and the circumstances of syncope were used to train and test the neural network. Given the exploratory nature of this work, we adopted two approaches to train and validate the tool. One approach used 4/5 of the data for the training set and 1/5 for the validation set, and the other approach used 9/10 for the training set and 1/10 for the validation set. The sensitivity, specificity, and area under the receiver operating characteristic curve of ANNs in identifying short-term adverse events after syncope were 95% [95% confidence interval (CI) 80-98%], 67% (95% CI 62-72%), 0.69 with the 1/5 approach and 100% (95% CI 84-100%), 79% (95% CI 72-85%), 0.78 with the 1/10 approach. CONCLUSION: The results of our study suggest that ANNs are effective in predicting the short-term risk of patients with syncope. Prospective studies are needed in order to compare ANNs' predictive capability with existing rules and clinical judgment.

Syncope: a review of emergency department management and disposition.

Syncope is defined as a transient loss of consciousness due to cerebral hypoperfusion with spontaneous return to baseline function without intervention. It is a common chief complaint of patients presenting to the emergency department. The differential diagnosis for syncope is broad and the management varies significantly depending on the underlying etiology. In the emergency department, determining the cause of a syncopal episode can be difficult. However, a thorough history and certain physical exam findings can assist in evaluating for life-threatening diagnoses. Risk-stratifying patients into low, moderate and high-risk groups can assist in medical decision making and help determine the patient's disposition. Advancements in ambulatory monitoring have made it possible to obtain prolonged cardiac evaluations of patients in the outpatient setting. This review will focus on the diagnosis and management of the various types of syncope.

Predictors of infection from dog bite wounds: which patients may benefit from prophylactic antibiotics?

OBJECTIVES: To determine a current infection rate of dog bite wounds and predictors of wounds at risk for infection that may benefit from prophylactic antibiotics. METHODS: A prospective multicentre observational study was conducted over 4.5 years. At the time of treatment Emergency Physicians completed a structured data form evaluating patient, wound and treatment characteristics of patients with dog bite wounds. Patients were followed up at 30 days to assess for infection. Predictor variables were analysed with univariate analysis, using either χ(2), parametric or nonparametric methods where appropriate. Significant variables and those with important interactions on univariate analysis were considered in a logistic regression (LR) analysis. RESULTS: 495 patients with dog bites were enrolled and 345 had complete follow-up. Eighteen patients (5.2%, 95% CI 3.1% to 8.1%) had bites that became infected. On univariate analysis, only puncture wounds were found to be significantly associated with infection RR 2.8 (95% CI 1.2 to 6.9). However, location and wound closure met criteria for entry into the model having important interactions; facial wounds had a higher risk of infection than anticipated but were also more likely to be closed (p < 0.0001). A LR model considering puncture wounds, wound closure and wound location found that puncture wounds (OR 4.1 [95% CI 1.4 to 11.7]) and wound closure (OR 3.1 [95% CI 1.03 to 9.0]) were independent predictors of infection. The incidence of infection in wounds that were not punctured or closed during treatment was only 2.6% (95% CI 0.7% to 6.5%). CONCLUSIONS: Puncture wounds or wounds closed during treatment are dog bite wounds at a high risk of infection and should be considered for treatment with prophylactic antibiotics.