Point-of-care high-sensitivity cardiac troponin in suspected acute myocardial infarction assessed at baseline and 2†h.

BACKGROUND AND AIMS: Strategies to assess patients with suspected acute myocardial infarction (AMI) using a point-of-care (POC) high-sensitivity cardiac troponin I (hs-cTnI) assay may expedite emergency care. A 2-h POC hs-cTnI strategy for emergency patients with suspected AMI was derived and validated. METHODS: In two international, multi-centre, prospective, observational studies of adult emergency patients (1486 derivation cohort and 1796 validation cohort) with suspected AMI, hs-cTnI (Siemens Atellica® VTLi) was measured at admission and 2 h later. Adjudicated final diagnoses utilized the hs-cTn assay in clinical use. A risk stratification algorithm was derived and validated. The primary diagnostic outcome was index AMI (Types 1 and 2). The primary safety outcome was 30-day major adverse cardiac events incorporating AMI and cardiac death. RESULTS: Overall, 81 (5.5%) and 88 (4.9%) patients in the derivation and validation cohorts, respectively, had AMI. The 2-h algorithm defined 66.1% as low risk with a sensitivity of 98.8% [95% confidence interval (CI) 89.3%-99.9%] and a negative predictive value of 99.9 (95% CI 99.2%-100%) for index AMI in the derivation cohort. In the validation cohort, 53.3% were low risk with a sensitivity of 98.9% (95% CI 92.4%-99.8%) and a negative predictive value of 99.9% (95% CI 99.3%-100%) for index AMI. The high-risk metrics identified 5.4% of patients with a specificity of 98.5% (95% CI 96.6%-99.4%) and a positive predictive value of 74.5% (95% CI 62.7%-83.6%) for index AMI. CONCLUSIONS: A 2-h algorithm using a POC hs-cTnI concentration enables safe and efficient risk assessment of patients with suspected AMI. The short turnaround time of POC testing may support significant efficiencies in the management of the large proportion of emergency patients with suspected AMI.

Artificial Intelligence Driven Prehospital ECG Interpretation for the Reduction of False Positive Emergent Cardiac Catheterization Lab Activations: A Retrospective Cohort Study.

OBJECTIVES: Data suggest patients suffering acute coronary occlusion myocardial infarction (OMI) benefit from prompt primary percutaneous intervention (PPCI). Many emergency medical services (EMS) activate catheterization labs to reduce time to PPCI, but suffer a high burden of inappropriate activations. Artificial intelligence (AI) algorithms show promise to improve electrocardiogram (ECG) interpretation. The primary objective was to evaluate the potential of AI to reduce false positive activations without missing OMI. METHODS: Electrocardiograms were categorized by (1) STEMI criteria, (2) ECG integrated device software and (3) a proprietary AI algorithm (Queen of Hearts (QOH), Powerful Medical). If multiple ECGs were obtained and any one tracing was positive for a given method, that diagnostic method was considered positive. The primary outcome was OMI defined as an angiographic culprit lesion with either TIMI 0-2 flow; or TIMI 3 flow with either peak high sensitivity troponin-I > 5000 ng/L or new wall motion abnormality. The primary analysis was per-patient proportion of false positives. RESULTS: A total of 140 patients were screened and 117 met criteria. Of these, 48 met the primary outcome criteria of OMI. There were 80 positives by STEMI criteria, 88 by device algorithm, and 77 by AI software. All approaches reduced false positives, 27% for STEMI, 22% for device software, and 34% for AI (p < 0.01 for all). The reduction in false positives did not significantly differ between STEMI criteria and AI software (p = 0.19) but STEMI criteria missed 6 (5%) OMIs, while AI missed none (p = 0.01). CONCLUSIONS: In this single-center retrospective study, an AI-driven algorithm reduced false positive diagnoses of OMI compared to EMS clinician gestalt. Compared to AI (which missed no OMI), STEMI criteria also reduced false positives but missed 6 true OMI. External validation of these findings in prospective cohorts is indicated.

Single High-Sensitivity Point-of-Care Whole-Blood Cardiac Troponin I Measurement to Rule Out Acute Myocardial Infarction at Low Risk.

BACKGROUND: High-sensitivity cardiac troponin (hs-cTn) laboratory assays are used to rule out myocardial infarction (MI) on presentation, but prolonged result turnaround times can delay patient management. Our primary aim was to identify patients at low risk of index MI using a rapid point-of-care (POC) whole-blood hs-cTnI assay at presentation with potential early patient discharge. METHODS: Consecutive patients presenting to the emergency department from 2 prospective observational studies with suspected acute coronary syndrome were enrolled. A POC hs-cTnI assay (Atellica VTLi) threshold using whole blood at presentation, which resulted in a negative predictive value of ≥99.5% and sensitivity of >99% for index MI, was derived (SEIGE [Safe Emergency Department Discharge Rate]) and validated with plasma (SAMIE [Suspected Acute Myocardial Infarction in Emergency]). Event adjudications were established with hs-cTnI assay results from routine clinical care. The primary outcome was MI at 30 days. RESULTS: A total of 1086 patients (8.1% with MI) were enrolled in a US derivation cohort (SEIGE) and 1486 (5.5% MI) in an Australian validation cohort (SAMIE). A derivation whole-blood POC hs-cTnI concentration of <4 ng/L provided a sensitivity of 98.9% (95% CI, 93.8%-100%) and negative predictive value of 99.5% (95% CI, 97.2%-100%) for ruling out MI. In the validation cohort, the sensitivity was 98.8% (95% CI, 93.3%-100%), and negative predictive value was 99.8% (95% CI, 99.1%-100%); 17.8% and 41.8%, respectively, were defined as low risk for discharge. The 30-day adverse cardiac events were 0.1% (n=1) for SEIGE and 0.8% (n=5) for SAMIE. CONCLUSIONS: A POC whole-blood hs-cTnI assay permits accessible, rapid, and safe exclusion of MI and may expedite discharge from the emergency department. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT04772157. URL: https://www.australianclinicaltrials.gov.au/anzctr_feed/form; Unique identifier: 12621000053820.

Rapid Rule-Out of Myocardial Infarction Using a Single High-Sensitivity Cardiac Troponin I Measurement Strategy at Presentation to the Emergency Department: The SAFETY Study.

BACKGROUND: Our study addressed the diagnostic performance of the Atellica® IM High-Sensitivity Troponin I (hs-cTnI) assay for the rapid rule-out of myocardial infarction (MI) using a single hs-cTnI measurement at presentation in patients presenting to a US emergency department (ED). METHODS: This was a prospective, observational, cohort study of consecutive ED patients with suspected acute coronary syndrome, using 12-lead electrocardiogram and serial hs-cTnI measurements ordered on clinical indication (SAFETY, NCT04280926). ST-segment elevation MI patients were excluded. The optimal threshold required a sensitivity ≥99% and a negative predictive value (NPV) ≥99.5% for MI during index hospitalization as primary outcome. Type 1 MI (T1MI), myocardial injury, and 30-day adverse events were considered secondary outcomes. Event adjudications were established using the hs-cTnI assay used in clinical care. RESULTS: In 1171 patients, MI occurred in 97 patients (8.3%), 78.3% of which were type 2 MI. The optimal rule out hs-cTnI threshold was <10 ng/L, which identified 519 (44.3%) patients as low risk at presentation, with sensitivity of 99.0% (95% CI, 94.4-100) and NPV of 99.8% (95% CI, 98.9-100). For T1MI, sensitivity was 100% (95% CI, 83.9-100) and NPV 100% (95% CI, 99.3-100). Regarding myocardial injury, the sensitivity and NPV were 99.5% (95% CI, 97.9-100) and 99.8% (95% CI, 98.9-100), respectively. For 30-day adverse events, sensitivity was 96.8% (95% CI, 94.3-98.4) and NPV 97.9% (95% CI, 96.2-98.9). CONCLUSIONS: A single hs-cTnI measurement strategy enabled the rapid identification of patients at low risk of MI and 30-day adverse events, allowing potential discharge early after ED presentation. CLINICALTRIALS.GOV REGISTRATION NUMBER: NCT04280926.

Missing occlusions: Quality gaps for ED patients with occlusion MI.

BACKGROUND: ST-elevation Myocardial Infarction (STEMI) guidelines encourage monitoring of false positives (Code STEMI without culprit) but ignore false negatives (non-STEMI with occlusion myocardial infarction [OMI]). We evaluated the hospital course of emergency department (ED) patients with acute coronary syndrome (ACS) using STEMI vs OMI paradigms. METHODS: This retrospective chart review examined all ACS patients admitted through two academic EDs, from June 2021 to May 2022, categorized as 1) OMI (acute culprit lesion with TIMI 0-2 flow, or acute culprit lesion with TIMI 3 flow and peak troponin I >10,000 ng/L; or, if no angiogram, peak troponin >10,000 ng/L with new regional wall motion abnormality), 2) NOMI (Non-OMI, i.e. MI without OMI) or 3) MIRO (MI ruled out: no troponin elevation). Patients were stratified by admission for STEMI. Initial ECGs were reviewed for automated interpretation of "STEMI", and admission/discharge diagnoses were compared. RESULTS: Among 382 patients, there were 141 OMIs, 181 NOMIs, and 60 MIROs. Only 40.4% of OMIs were admitted as STEMI: 60.0% had "STEMI" on ECG, and median door-to-cath time was 103 min (IQR 71-149). But 59.6% of OMIs were not admitted as STEMI: 1.3% had "STEMI" on ECG (p < 0.001) and median door-to-cath time was 1712 min (IQR 1043-3960; p < 0.001). While 13.9% of STEMIs were false positive and had a different discharge diagnosis, 32.0% of Non-STEMIs had OMI but were still discharged as "Non-STEMI." CONCLUSIONS: STEMI criteria miss a majority of OMI, and discharge diagnoses highlight false positive STEMI but never false negative STEMI. The OMI paradigm reveals quality gaps and opportunities for improvement.

A Bayesian approach to acute coronary occlusion.

In the STEMI paradigm, the disease (acute coronary occlusion) is defined and named after one element (ST elevation, without regard to the remainder of the QRST) of one imperfect test (the ECG). This leads to delayed reperfusion for patients with acute coronary occlusion whose ECGs don't meet STEMI criteria. In this editorial, we elaborate on the article by Jose Nunes de Alencar Neto about applying Bayesian reasoning to ECG interpretation. The Occlusion MI (OMI) paradigm offers evidencebased advances in ECG interpretation, expert-trained artificial intelligence, and a paradigm shift that incorporates a Bayesian approach to acute coronary occlusion.

Kenichi Harumi Plenary Address at Annual Meeting of the International Society of Computers in Electrocardiology: "What Should ECG Deep Learning Focus on? The diagnosis of acute coronary occlusion!"

According to the STEMI paradigm, only patients whose ECGs meet STEMI criteria require immediate reperfusion. This leads to reperfusion delays and significantly increases the mortality for the quarter of "non-STEMI" patients with totally occluded arteries. The Occlusion MI (OMI) paradigm has developed advanced ECG interpretation to identify this high-risk group, including examining the ECG in totality and assessing ST/T changes in proportion to the QRS. If neural networks are only developed based on STEMI databases and to identify STEMI criteria, they will simply reinforce a failed paradigm. But if deep learning is trained to identify OMI it could revolutionize patient care. This article reviews the paradigm shift from STEMI and OMI, and examines the potential and pitfalls of deep learning. This is based on the Kenichi Harumi Plenary Address at the Annual Meeting of the International Society of Computers in Electrocardiology, given by OMI expert Dr. Stephen Smith.

A two-staged classifier to reduce false positives: On device detection of atrial fibrillation using phase-based distribution of poincaré plots and deep learning.

BACKGROUND: Mobile Cardiac Outpatient Telemetry (MCOT) can be used to screen high risk patients for atrial fibrillation (AF). These devices rely primarily on algorithmic detection of AF events, which are then stored and transmitted to a clinician for review. It is critical the positive predictive value (PPV) of MCOT detected AF is high, and this often leads to reduced sensitivity, as device manufacturers try to limit false positives. OBJECTIVE: The purpose of this study was to design a two stage classifier using artificial intelligence (AI) to improve the PPV of MCOT detected atrial fibrillation episodes whilst maintaining high levels of detection sensitivity. METHODS: A low complexity, RR-interval based, AF classifier was paired with a deep convolutional neural network (DCNN) to create a two-stage classifier. The DCNN was limited in size to allow it to be embedded on MCOT devices. The DCNN was trained on 491,727 ECGs from a proprietary database and contained 128,612 parameters requiring only 158 KB of storage. The performance of the two-stage classifier was then assessed using publicly available datasets. RESULTS: The sensitivity of AF detected by the low complexity classifier was high across all datasets (>93%) however the PPV was poor (<76%). Subsequent analysis by the DCNN increased episode PPV across all datasets substantially (>11%), with only a minor loss in sensitivity (<5%). This increase in PPV was due to a decrease in the number of false positive detections. Further analysis showed that DCNN processing was only required on around half of analysis windows, offering a significant computational saving against using the DCNN as a one-stage classifier. CONCLUSION: DCNNs can be combined with existing MCOT classifiers to increase the PPV of detected AF episodes. This reduces the review burden for physicians and can be achieved with only a modest decrease in sensitivity.

Education curriculum assessment for teaching electrocardiography: Rationale and design for the prospective, international, randomized controlled EDUCATE trial.

BACKGROUND: Electrocardiogram (ECG) interpretation training is a fundamental component of medical education across disciplines. However, the skill of interpreting ECGs is not universal among medical graduates, and numerous barriers and challenges exist in medical training and clinical practice. An evidence-based and widely accessible learning solution is needed. DESIGN: The EDUcation Curriculum Assessment for Teaching Electrocardiography (EDUCATE) Trial is a prospective, international, investigator-initiated, open-label, randomized controlled trial designed to determine the efficacy of self-directed and active-learning approaches of a web-based educational platform for improving ECG interpretation proficiency. Target enrollment is 1000 medical professionals from a variety of medical disciplines and training levels. Participants will complete a pre-intervention baseline survey and an ECG interpretation proficiency test. After completion, participants will be randomized into one of four groups in a 1:1:1:1 fashion: (i) an online, question-based learning resource, (ii) an online, lecture-based learning resource, (iii) an online, hybrid question- and lecture-based learning resource, or (iv) a control group with no ECG learning resources. The primary endpoint will be the change in overall ECG interpretation performance according to pre- and post-intervention tests, and it will be measured within and compared between medical professional groups. Secondary endpoints will include changes in ECG interpretation time, self-reported confidence, and interpretation accuracy for specific ECG findings. CONCLUSIONS: The EDUCATE Trial is a pioneering initiative aiming to establish a practical, widely available, evidence-based solution to enhance ECG interpretation proficiency among medical professionals. Through its innovative study design, it tackles the currently unaddressed challenges of ECG interpretation education in the modern era. The trial seeks to pinpoint performance gaps across medical professions, compare the effectiveness of different web-based ECG content delivery methods, and create initial evidence for competency-based standards. If successful, the EDUCATE Trial will represent a significant stride towards data-driven solutions for improving ECG interpretation skills in the medical community.

Emerging ECG methods for acute coronary syndrome detection: Recommendations & future opportunities.

Despite being the mainstay for the initial noninvasive assessment of patients with symptomatic coronary artery disease, the 12­lead ECG remains a suboptimal diagnostic tool for myocardial ischemia detection with only acceptable sensitivity and specificity scores. Although myocardial ischemia affects the configuration of the QRS complex and the STT waveform, current guidelines primarily focus on ST segment amplitude, which constitutes a missed opportunity and may explain the suboptimal diagnostic performance of the ECG. This possible opportunity and the low cost and ease of use of the ECG provide compelling motivation to enhance the diagnostic accuracy of the ECG to ischemia detection. This paper describes numerous computational ECG methods and approaches that have been shown to dramatically increase ECG sensitivity to ischemia detection. Briefly, these emerging approaches can be conceptually grouped into one of the following four approaches: (1) leveraging novel ECG waveform features and signatures indicative of ischemic injury other than the classical ST-T amplitude measures; (2) applying body surface potentials mapping (BSPM)-based approaches to enhance the spatial coverage of the surface ECG to detecting ischemia; (3) developing an inverse ECG solution to reconstruct anatomical models of activation and recovery pathways to detect and localize injury currents; and (4) exploring artificial intelligence (AI)-based techniques to harvest ECG waveform signatures of ischemia. We present recent advances, shortcomings, and future opportunities for each of these emerging ECG methods. Future research should focus on the prospective clinical testing of these approaches to establish clinical utility and to expedite potential translation into clinical practice.

Device agnostic AI-based analysis of ambulatory ECG recordings.

Deep Convolutional Neural Networks (DCNNs) have been shown to provide improved performance over traditional heuristic algorithms for the detection of arrhythmias from ambulatory ECG recordings. However, these DCNNs have primarily been trained and tested on device-specific databases with standardized electrode positions and uniform sampling frequencies. This work explores the possibility of training a DCNN for Atrial Fibrillation (AF) detection on a database of single­lead ECG rhythm strips extracted from resting 12­lead ECGs. We then test the performance of the DCNN on recordings from ambulatory ECG devices with different recording leads and sampling frequencies. We developed an extensive proprietary resting 12­lead ECG dataset of 549,211 patients. This dataset was randomly split into a training set of 494,289 patients and a testing set of the remaining 54,922 patients. We trained a 34-layer convolutional DCNN to detect AF and other arrhythmias on this dataset. The DCNN was then validated on two Physionet databases commonly used to benchmark automated ECG algorithms (1) MIT-BIH Arrhythmia Database and (2) MIT-BIH Atrial Fibrillation Database. Validation was performed following the EC57 guidelines, with performance assessed by gross episode and duration sensitivity and positive predictive value (PPV). Finally, validation was also performed on a selection of rhythm strips from an ambulatory ECG patch that a committee of board-certified cardiologists annotated. On MIT-BIH, The DCNN achieved a sensitivity of 100% and 84% PPV in detecting episodes of AF. and 100% sensitivity and 94% PPV in quantifying AF episode duration. On AFDB, The DCNN achieved a sensitivity of 94% and PPV of 98% in detecting episodes of AF, and 98% sensitivity and 100% PPV in quantifying AF episode duration. On the patch database, the DCNN demonstrated performance that was closely comparable to that of a cardiologist. The results indicate that DCNN models can learn features that generalize between resting 12­lead and ambulatory ECG recordings, allowing DCNNs to be device agnostic for detecting arrhythmias from single­lead ECG recordings and enabling a range of clinical applications.

Cardiac Troponin Testing in Patients with COVID-19: A Strategy for Testing and Reporting Results.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged late in 2019 causing COVID-19 (coronavirus disease-2019) may adversely affect the cardiovascular system. Publications from Asia, Europe, and North America have identified cardiac troponin as an important prognostic indicator for patients hospitalized with COVID-19. We recognized from publications within the first 6 months of the pandemic that there has been much uncertainty on the reporting, interpretation, and pathophysiology of an increased cardiac troponin concentration in this setting. CONTENT: The purpose of this mini-review is: a) to review the pathophysiology of SARS-CoV-2 and the cardiovascular system, b) to overview the strengths and weaknesses of selected studies evaluating cardiac troponin in patients with COVID-19, and c) to recommend testing strategies in the acute period, in the convalescence period and in long-term care for patients who have become ill with COVID-19. SUMMARY: This review provides important educational information and identifies gaps in understanding the role of cardiac troponin and COVID-19. Future, properly designed studies will hopefully provide the much-needed evidence on the path forward in testing cardiac troponin in patients with COVID-19.

Electrocardiographic Diagnosis of Acute Coronary Occlusion Myocardial Infarction in Ventricular Paced Rhythm Using the Modified Sgarbossa Criteria.

STUDY OBJECTIVE: Ventricular paced rhythm is thought to obscure the electrocardiographic diagnosis of acute coronary occlusion myocardial infarction. Our primary aim was to compare the sensitivity of the modified Sgarbossa criteria (MSC) to that of the original Sgarbossa criteria for the diagnosis of occlusion myocardial infarction in patients with ventricular paced rhythm. METHODS: In this retrospective case-control investigation, we studied adult patients with ventricular paced rhythm and symptoms of acute coronary syndrome who presented in an emergency manner to 16 international cardiac referral centers between January 2008 and January 2018. The occlusion myocardial infarction group was defined angiographically as thrombolysis in myocardial infarction grade 0 to 1 flow or angiographic evidence of coronary thrombosis and peak cardiac troponin I ≥10.0 ng/mL or troponin T ≥1.0 ng/mL. There were 2 control groups: the "non-occlusion myocardial infarction-angio" group consisted of patients who underwent coronary angiography for presumed type I myocardial infarction but did not meet the definition of occlusion myocardial infarction; the "no occlusion myocardial infarction" control group consisted of randomly selected emergency department patients without occlusion myocardial infarction. RESULTS: There were 59 occlusion myocardial infarction, 90 non-occlusion myocardial infarction-angio, and 102 no occlusion myocardial infarction subjects (mean age, 72.0 years; 168 [66.9%] men). For the diagnosis of occlusion myocardial infarction, the MSC were more sensitive than the original Sgarbossa criteria (sensitivity 81% [95% confidence interval [CI] 69 to 90] versus 56% [95% CI 42 to 69]). Adding concordant ST-depression in V4 to V6 to the MSC yielded 86% (95% CI 75 to 94) sensitivity. For the no occlusion myocardial infarction control group of ED patients, additional test characteristics of MSC and original Sgarbossa criteria, respectively, were as follows: specificity 96% (95% CI 90 to 99) versus 97% (95% CI 92 to 99); negative likelihood ratio (LR) 0.19 (95% CI 0.11 to 0.33) versus 0.45 (95% CI 0.34 to 0.65); and positive LR 21 (95% CI 7.9 to 55) versus 19 (95% CI 6.1 to 59). For the non-occlusion myocardial infarction-angio control group, additional test characteristics of MSC and original Sgarbossa criteria, respectively, were as follows: specificity 84% (95% CI 76 to 91) versus 90% (95% CI 82 to 95); negative LR 0.22 (95% CI 0.13 to 0.38) versus 0.49 (95% CI 0.35 to 0.66); and positive LR 5.2 (95% CI 3.2 to 8.6) versus 5.6 (95% CI 2.9 to 11). CONCLUSION: For the diagnosis of occlusion myocardial infarction in the presence of ventricular paced rhythm, the MSC were more sensitive than the original Sgarbossa criteria; specificity was high for both rules. The MSC may contribute to clinical decisionmaking for patients with ventricular paced rhythm.

Post-arrest wide complex rhythm: What is the cause of death?

A 72-year-old man presented to the ED following witnessed cardiac arrest. After return of spontaneous circulation, an ECG was performed which demonstrated a wide complex rhythm with "shark fin" morphology. With careful examination it is possible to identify the J point and determine that the electrocardiogram (ECG) findings actually represent massive ST-elevation indicative of occlusion myocardial infarction (OMI). Initial troponin was undetectable. The patient underwent emergent cardiac catheterization and had a 100% proximal LAD occlusion that was successfully stented. The patient was discharged home neurologically intact several days later. This case highlights the importance of careful ECG interpretation and the limitations of troponin assays in the evaluation of acute coronary syndrome. Most importantly, we demonstrate how to evaluate for ST elevation in the context of a widened QRS complex.

An unusual case of epistaxis in a four month old.

Epistaxis is a routine complaint in Emergency Medicine and presents most commonly in adults and children and its incidence increases with age. It is rare in infants and neonates. We discuss a case of epistaxis in a four-month-old male who presented to a critical access hospital. What initially appeared to be routine brisk epistaxis was later discovered to be a large, complex, epiglottic hemangioma. The patient was stabilized using topical tranexamic acid, nasal packing with ketamine sedation, and sent to a tertiary care center for definitive management. He required advanced airway management in the OR for definitive airway management for airway-obstructing hemangioma excision.

Spectrum and frequency of critical procedures performed at a Level I adult and pediatric trauma center.

OBJECTIVES: The primary objective of this study was to provide physician-level data about the frequency of critical procedures at a combined adult and pediatric Level I trauma center, high-acuity, high-volume academic ED. The inspiration for this study question came from a previous study by Mittiga et al. (2013) describing pediatric critical procedure data at a similar high-acuity, high-volume, pediatric-only academic ED. Our secondary objective is to compare our pediatric level procedural spectrum and frequency with those published by Mittiga et al. (2013). METHODS: This prospective observational study occurred over eleven consecutive months at an urban, Level I combined adult/pediatric trauma center with 96,000 annual visits (8500 pediatric). We recorded only procedures performed in the resuscitation bays. All data analysis is descriptive. RESULTS: Over eleven months, data on 3891 resuscitations were collected (3686 adults and 205 children); 38 faculty physicians supervised 1838 total critical procedures, 64 on children. The mean number of critical procedures per physician per month was 4.42 (0.15 on children). Additionally, ultrasound for intravenous access, extended focused assessment with sonography for trauma (e-FAST), or cardiac ultrasound were performed in 3862 resuscitations (178 pediatric). CONCLUSIONS: Emergency medicine faculty physicians at a combined Level I adult and pediatric trauma center performed and/or supervised 4.4 total (0.15 pediatric) critical procedures per month per faculty which is nearly 6 times more critical procedures monthly than faculty at a similar volume pediatric-only trauma center. However, fewer critical procedures were performed on children at the combined facility.

The feasibility of bedside transvaginal ultrasonography in non-pregnant women in the emergency department.

STUDY OBJECTIVE: We recorded data on the routine use of point-of-care transvaginal ultrasound (POC TVUS) for the evaluation of non-pregnant women with pelvic complaints in the Emergency Department (ED), and sought to determine how it altered the diagnostic impression and management. METHODS: This was a prospective observational study. Adult non-pregnant women with pelvic complaints undergoing POC TVUS were enrolled. Pre and post ultrasound, the treating physician completed a data collection form indicating the most likely cause of the patient's pain, current treatment plan, and expected ultrasound findings. Immediately after the ultrasound, the treating physician completed another form to indicate details about the sonographic process and findings, and whether a radiologyperformed TVUS was planned. RESULTS: Of 113 women enrolled, 79% had both ovaries visualized and the POC TVUS led to changes in plan in 43% of patients, including 3 emergent/urgent interventions; 48% of patients had unexpected findings. Of the 25% who had an additional radiology exam, there were no instances of discordant findings when both ovaries were assessed to be normal on ED Ultrasound. The ultrasound added <10 minutes in 92% of patients. With an ED ultrasound only (n = 85), the median length of stay (LOS) was 282 (IQR 197-323) minutes, compared to 437 (IQR 367-500) minutes when a radiology ultrasound was required (n = 28) (median difference,170 minutes [95% CI 122-212 min]). CONCLUSION: In this study, emergency physicians were able to efficiently obtain and interpret POC TVUS images that changed assessment of non-pregnant women with pelvic pain without significantly increasing the ED length of stay.

Diagnosis of Occlusion Myocardial Infarction in Patients with Left Bundle Branch Block and Paced Rhythms.

PURPOSE OF REVIEW: A number of criteria have been developed to aid with the diagnosis of occlusion myocardial infarction (OMI) in patients with left bundle branch block (LBBB) and ventricular paced rhythms (VPR). The current guidelines do not provide clear preference for any specific ECG criteria in LBBB and paced rhythm patients. RECENT FINDINGS: This review delineates the difficulties of electrocardiographic diagnosis of OMI in both LBBB and VPR patients. We describe the original Sgarbossa and the newer criteria and their diagnostic performances. We highlight the expected changes of newer pacing modalities and how they may interfere with the electrocardiographic diagnosis of OMI. We recommend utilizing the Cai et al. algorithm, which combines clinical assessment with the Smith Modified Sgarbossa ECG criteria, for both LBBB and right ventricular pacing patients with suspected OMI. There is limited data concerning ECG changes of OMI in patients with the newer pacing modalities, such as biventricular, His-bundle, or left bundle branch pacing.

STEMI: A transitional fossil in MI classification?

An important task in emergency cardiology is distinguishing patients with acute coronary occlusion (ACO), who will benefit from emergent reperfusion therapy, from those without ongoing myocyte loss who can be managed with medical therapy and for whom potentially harmful invasive interventions can be deferred. The electrocardiogram is critical in this process. Although the ST-segment elevation myocardial infarction (STEMI)/non-STEMI paradigm is well-established, with "STEMI" representing ACO, its evidence base is poor, and this can have dire consequences. The universally recommended STEMI criteria do not accurately diagnose ACO; in fact, they miss more than one-fourth of the patients with ACO, and also result in a substantial burden of unnecessary catheterization laboratory activations. We here discuss why we believe it is time to change the current STEMI/non-STEMI paradigm.

ECG reading differences demonstrated on two databases.

Many studies that rely on manual ECG interpretation as a reference use multiple ECG expert interpreters and a method to resolve differences between interpreters, reflecting the fact that experts sometimes use different criteria. The aim of this study was to show the effect of manual ECG interpretation style on training automated ECG interpretation. METHODS: The effect of ECG interpretation style or differing ECG criteria on algorithm training was shown in this study by careful analysis of the changes in algorithm performance when the algorithm was trained on one database and tested on a different database. Morphology related ECG interpretation was summarized in eleven abnormalities such as left bundle branch block (LBBB) and old anterior myocardial infarction (MI). Each of the two databases used in the study had a reference interpretation mapped to those eleven abnormalities. F1 algorithm performance scores across abnormalities were compared for four cases. First, the algorithm was trained and tested on randomly split database A and then trained on the training set of database A and tested on randomly chosen test set of database B. The previous two test cases were repeated for opposite databases, train and test on database B and then train on database B and test on the test set of database A. RESULTS: F1 scores across abnormalities were generally higher when training and testing on the same database. F1 scores were high for bundle branch blocks (BBB) no matter the training and testing database combination. Old anterior MI F1 score dropped for one cross-database comparison and not the other suggesting a difference in manual interpretation. CONCLUSION: For some abnormalities, human experts appear to have used different criteria for ECG interpretation, as evident by the difference between cross-database and within-database performance. Bundle branch blocks appear to be interpreted in a consistent manner.