EDUCATE: An international, randomized controlled trial for teaching electrocardiography

INTRODUCTION

Despite the critical role of electrocardiograms (ECGs) in patient care, evident gaps exist in ECG interpretation competency among healthcare professionals across various medical disciplines and training levels. Currently, no practical, evidence-based, and easily accessible ECG learning solution is available for healthcare professionals. The aim of this study was to assess the effectiveness of web-based, learner-directed interventions in improving ECG interpretation skills in a diverse group of healthcare professionals.

METHODS

In an international, prospective, randomized controlled trial, 1206 healthcare professionals from various disciplines and training levels were enrolled. They underwent a pre-intervention test featuring 30 12-lead ECGs with common urgent and non-urgent findings. Participants were randomly assigned to four groups: (i) practice ECG interpretation question bank (question bank), (ii) lecture-based learning resource (lectures), (iii) hybrid question- and lecture-based learning resource (hybrid), or (iv) no ECG learning resources (control). After four months, a post-intervention test was administered. The primary outcome was the overall change in ECG interpretation performance, with secondary outcomes including changes in interpretation time, self-reported confidence, and accuracy for specific ECG findings. Both unadjusted and adjusted scores were used for performance assessment.

RESULTS

Among 1206 participants, 863 (72 %) completed the trial. Following the intervention, the question bank, lectures, and hybrid intervention groups each exhibited significant improvements, with average unadjusted score increases of 11.4 % (95 % CI, 9.1 to 13.7; P<0.01), 9.8 % (95 % CI, 7.8 to 11.9; P<0.01), and 11.0 % (95 % CI, 9.2 to 12.9; P<0.01), respectively. In contrast, the control group demonstrated a non-significant improvement of 0.8 % (95 % CI, -1.2 to 2.8; P=0.54). While no differences were observed among intervention groups, all outperformed the control group significantly (P<0.01). Intervention groups also excelled in adjusted scores, confidence, and proficiency for specific ECG findings.

CONCLUSION

Web-based, self-directed interventions markedly enhanced ECG interpretation skills across a diverse range of healthcare professionals, providing an accessible and evidence-based solution.

International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction

Aims

A majority of acute coronary syndromes (ACS) present without typical ST elevation. One-third of non-ST-elevation myocardial infarction (NSTEMI) patients have an acutely occluded culprit coronary artery [occlusion myocardial infarction (OMI)], leading to poor outcomes due to delayed identification and invasive management. In this study, we sought to develop a versatile artificial intelligence (AI) model detecting acute OMI on single-standard 12-lead electrocardiograms (ECGs) and compare its performance with existing state-of-the-art diagnostic criteria.

Methods and results

An AI model was developed using 18 616 ECGs from 10 543 patients with suspected ACS from an international database with clinically validated outcomes. The model was evaluated in an international cohort and compared with STEMI criteria and ECG experts in detecting OMI. The primary outcome of OMI was an acutely occluded or flow-limiting culprit artery requiring emergent revascularization. In the overall test set of 3254 ECGs from 2222 patients (age 62 ± 14 years, 67% males, 21.6% OMI), the AI model achieved an area under the curve of 0.938 [95% confidence interval (CI): 0.924-0.951] in identifying the primary OMI outcome, with superior performance [accuracy 90.9% (95% CI: 89.7-92.0), sensitivity 80.6% (95% CI: 76.8-84.0), and specificity 93.7 (95% CI: 92.6-94.8)] compared with STEMI criteria [accuracy 83.6% (95% CI: 82.1-85.1), sensitivity 32.5% (95% CI: 28.4-36.6), and specificity 97.7% (95% CI: 97.0-98.3)] and with similar performance compared with ECG experts [accuracy 90.8% (95% CI: 89.5-91.9), sensitivity 73.0% (95% CI: 68.7-77.0), and specificity 95.7% (95% CI: 94.7-96.6)].

Conclusion

The present novel ECG AI model demonstrates superior accuracy to detect acute OMI when compared with STEMI criteria. This suggests its potential to improve ACS triage, ensuring appropriate and timely referral for immediate revascularization.

Comparison of diagnostic accuracy of current left bundle branch block and ventricular pacing ECG criteria for detection of occlusion myocardial infarction

BACKGROUND

Electrocardiographic detection of patients with occlusion myocardial infarction (OMI) can be difficult in patients with left bundle branch block (LBBB) or ventricular paced rhythm (VPR) and several ECG criteria for the detection of OMI in LBBB/VPR exist. Most recently, the Barcelona criteria, which includes concordant ST deviation and discordant ST deviation in leads with low R/S amplitudes, showed superior diagnostic accuracy but has not been validated externally. We aimed to describe the diagnostic accuracy of four available ECG criteria for OMI detection in patients with LBBB/VPR at the emergency department.

METHODS

The unweighted Sgarbossa criteria, the modified Sgarbossa criteria (MSC), the Barcelona criteria and the Selvester criteria were applied to chest pain patients with LBBB or VPR in a prospectively acquired database from five emergency departments.

RESULTS

In total, 623 patients were included, among which 441 (71%) had LBBB and 182 (29%) had VPR. Among these, 82 (13%) patients were diagnosed with AMI, and an OMI was identified in 15 (2.4%) cases. Sensitivity/specificity of the original unweighted Sgarbossa criteria were 26.7/86.2%, for MSC 60.0/86.0%, for Barcelona criteria 53.3/82.2%, and for Selvester criteria 46.7/88.3%. In this setting with low prevalence of OMI, positive predictive values were low (Sgarbossa: 4.6%; MSC: 9.4%; Barcelona criteria: 6.9%; Selvester criteria: 9.0%) and negative predictive values were high (all >98.0%).

CONCLUSIONS

Our results suggests that ECG criteria alone are insufficient in predicting presence of OMI in an ED setting with low prevalence of OMI, and the search for better rapid diagnostic instruments in this setting should continue.

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.

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.

ECG Interpretation Proficiency of Healthcare Professionals

ECG interpretation is essential in modern medicine, yet achieving and maintaining competency can be challenging for healthcare professionals. Quantifying proficiency gaps can inform educational interventions for addressing these challenges. Medical professionals from diverse disciplines and training levels interpreted 30 12-lead ECGs with common urgent and nonurgent findings. Average accuracy (percentage of correctly identified findings), interpretation time per ECG, and self-reported confidence (rated on a scale of 0 [not confident], 1 [somewhat confident], or 2 [confident]) were evaluated. Among the 1206 participants, there were 72 (6%) primary care physicians (PCPs), 146 (12%) cardiology fellows-in-training (FITs), 353 (29%) resident physicians, 182 (15%) medical students, 84 (7%) advanced practice providers (APPs), 120 (10%) nurses, and 249 (21%) allied health professionals (AHPs). Overall, participants achieved an average overall accuracy of 56.4% ± 17.2%, interpretation time of 142 ± 67 seconds, and confidence of 0.83 ± 0.53. Cardiology FITs demonstrated superior performance across all metrics. PCPs had a higher accuracy compared to nurses and APPs (58.1% vs 46.8% and 50.6%; P < 0.01), but a lower accuracy than resident physicians (58.1% vs 59.7%; P < 0.01). AHPs outperformed nurses and APPs in every metric and showed comparable performance to resident physicians and PCPs. Our findings highlight significant gaps in the ECG interpretation proficiency among healthcare professionals.

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.

Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.

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.

Analytical performance comparing siemens whole blood point of care Atellica VTLi to the central laboratory plasma Atellica IM high-sensitivity cardiac troponin I assays

INTRODUCTION

This study examined the analytical performance of a whole blood (WB) point of care (POC) hs-cTnI assay compared to a plasma central laboratory hs-cTnI assay in patients presenting with ischemic symptoms to a US emergency department.

METHODS

Fresh WB specimens collected at 0 and 2 h from 1089 consecutive patients (2152 total from 1076 matched specimens) were analyzed for hs-cTnI using WB on POC Siemens Atellica VTLi assay and plasma on central laboratory Siemens Atellica IM assay. Concordances were determined based on concentrations ranging from < limit of detection (LoD), LoD to overall and sex specific 99th percentiles from both the IFCC manufacturer package inserts and Universal Sample Bank (USB) data, and > 99th percentiles. Method comparisons were calculated using Passing Bablok regression and Bland Altmann plots, and linear regression determined by Pearson correlation coefficient.

RESULTS

Baseline concentration comparisons showed: POC VTLi < LoD 4-5 %, ≥ LoD 95 %; Atellica IM < LoD 5-7 %, and ≥ LoD 94-95 %. From the 2152 paired 0 and 2-hour samples, based on 99th percentiles, overall concordance was 91-92 % (kappa 0.72-0.77) and discordance 8 %. Passing Bablok regression analysis using 1924 specimens between LoD to 500 ng/L showed: slopes 0.469-0.490; y-intercepts 1.753-2.028; r values 0.631-0.817. Pearson correlation coefficient showed moderate to strong correlation strength, even with up to 53 % cTnI concentrations variance (Passing Bablok slopes) vs 27.0-40.1 % (Bland-Altmann plots).

CONCLUSIONS

Up to 95 % of measured samples were > LoD for both the POC (Atellica VTLi) and central laboratory (Atellica IM) hs-cTnI assays. Moderate to strong concordance and correlation were observed between assays, despite up to 53 % variances in cTnI concentration.

Women's Understanding of Windows of Susceptibility and the Role of the Environment in Breast Cancer Risk

Emerging evidence suggests women who are exposed to harmful environmental exposures, especially during certain critical periods across the lifespan, may increase their breast cancer risk. Such windows of susceptibility (WoS) occur throughout a woman's lifetime, during which she is especially vulnerable to the effects of harmful environmental exposures. This interaction makes the reduction of harmful environmental toxicants during those time periods a priority for community health promotion. Communicating about environmental exposures and their impact on women's health requires an assessment of sense-making around, and understanding of, the link between breast cancer and the environment. To that end, focus groups were conducted to assess the themes that emerge when women make sense of (a) their own breast cancer risk, (b) the environment-cancer connection, and (c) WoS. Results provide insight into how women understand these issues which can inform messaging strategies focused on reducing harmful environmental exposures. Implications are discussed within the context of communication efforts tailored to educate women, particularly mothers with daughters in the prepubertal and pubertal WoS who are particularly vulnerable to harmful environmental exposures.

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 Validation Study of the BRIEF-2 Among Kindergarteners and First Graders At-Risk for Behavior Problems

Early identification of executive dysfunction and timely school-based intervention efforts are critical for students at risk for problematic behaviors during early elementary school. The original Behavior Rating Inventory of Executive Functioning (BRIEF) was designed to measure real-world behavioral manifestations of executive functioning, neurocognitive processes critical for school success. With the updated BRIEF-2, independent validation is needed with kindergarten and first grade students at risk for emotional and behavioral disorders. Thus, using item level analyses, we examined the factor structure of the BRIEF-2 Teacher Rating form with 1,112 students. Results indicated little evidence for the original three-index model and supported a modified two-index model, with a Cognitive Regulation Index and an overall Behavior-Emotion Regulation Index. Criterion related validity indicated positive relationships with performance-based executive functioning (Head-Toes-Knees-Shoulders) and later internalizing and externalizing behaviors. We discuss implications of findings for early identification and school-based intervention efforts, as well as future research.

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.

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.

High-risk Electrocardiogram Patterns

This case report describes a patient in their 50s who presented to the emergency department with the presence of typical retrosternal chest pain radiating to the left scapular site.

From Q/Non-Q Myocardial Infarction to STEMI/NSTEMI: Why It's Time to Consider Another Simplified Dichotomy; a Narrative Literature Review

Acute coronary syndromes (ACSs) are classified as ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI) based on the presence of guideline-recommended ST-segment elevation (STE) criteria on the electrocardiogram (ECG). STEMI is associated with acute total coronary occlusion (ATO) and transmural myocardial necrosis and is managed with emergent reperfusion therapy, and NSTEMI is supposedly synonymous with subendocardial myocardial infarction without ATO. However, coronary angiograms reveal that a significant proportion of patients with NSTEMI have ATO. Here, we review articles that studied the frequency and cardiovascular outcomes of ATO in NSTEMI patients compared with those without ATO. We discuss ECG patterns of patients with suspected acute myocardial infarction that do not fulfill STEMI criteria but are associated with ATO. Under-recognition of these atypical patterns results in delays to reperfusion therapy. We also advocate revision of the current STEMI/NSTEMI paradigm because consideration of STE, by itself, out of context of other clinical and ECG features, leads to the ECG diagnosis of STEMI when the ECG actually represents a mimic ["Pseudo-STEMI"], and suggest renaming the ACSs classification as the Occlusion Myocardial Infarction (OMI)/Non-Occlusion Myocardial Infarction (NOMI) paradigm.

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 single‑lead ECG rhythm strips extracted from resting 12‑lead 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 12‑lead 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 12‑lead and ambulatory ECG recordings, allowing DCNNs to be device agnostic for detecting arrhythmias from single‑lead ECG recordings and enabling a range of clinical applications.

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 12‑lead 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.

Diagnostic performance of a rapid, novel, whole blood, point of care high-sensitivity cardiac troponin I assay for myocardial infarction

BACKGROUND

We evaluated the diagnostic performance of a whole blood, point of care (POC) high-sensitivity cardiac troponin I (hs-cTnI) assay for myocardial infarction (MI) compared to central laboratory assays.

METHODS

Consecutive patients presenting to the emergency department with symptoms of ischemia were studied. Serial hs-cTnI testing was based on clinical indication at presentation. Parallel measurements were made using fresh whole blood on Siemens Atellica VTLi POC assay, EDTA plasma on Abbott ARCHITECT i2000 used in practice, and heparin plasma on Siemens Atellica. MI was determined according to the Fourth Universal Definition of MI using 99th percentiles. Sensitivities and negative predictive values (NPV) were calculated using 99th percentile URLs.

RESULTS

1089 Patients, 418 females and 671 males, were enrolled. There were 91 (8.4%) MIs. At baseline (0 h), POC hs-cTnI assay had a sensitivity of 65.7% (95% CI 47.8-80.9) for females and 67.9% (54.0-79.7) for males and NPV of 96.4% (93.9-98.1) for females and 96.7% (94.9-98.0) for males. At 2 h, sensitivity improved to 82.9% (66.4-93.4) for females and 80.4% (67.6-89.8) for males, while NPV improved to 98.2% (96.1-99.3) and 97.9% (96.3-99.0), respectively. For central laboratory assays, comparable diagnostics were observed at 2 h: females - sensitivity 94.3% (80.8-99.3) for ARCHITECT and 79.4% (62.1-91.3) for Atellica, and NPV 99.3% (97.6-99.9) and 98.0% (95.8-99.2), respectively; males - sensitivity 87.5% (75.9-94.8) for ARCHITECT and 80.4% (67.6-89.8) for Atellica, NPVs of 98.7% (97.3-99.5) and 97.9% (96.3-99.0), respectively.

CONCLUSIONS

The POC, whole blood Atellica VTLi hs-cTnI assay demonstrated comparable diagnostic accuracy for MI to central laboratory assays using 99th percentiles.

Interobserver Variability Among Experienced Electrocardiogram Readers To Diagnose Acute Thrombotic Coronary Occlusion In Patients with Out of Hospital Cardiac Arrest: Impact of Metabolic Milieu and Angiographic Culprit

OBJECTIVES

We sought to evaluate interobserver concordance among experienced electrocardiogram (ECG) readers in predicting acute thrombotic coronary occlusion (ATCO) in the context of abnormal metabolic milieu (AMM) following resuscitated out of hospital cardiac arrest (OHCA).

METHODS

OHCA patients with initial shockable rhythm who underwent invasive coronary angiography (ICA) were included. AMM was defined as one of: pH < 7.1, lactate > 2 mmol/L, serum potassium < 2.8 or > 6.0 mEq/L. The initial ECG following ROSC but prior to ICA was adjudicated by 2 experienced readers using classic ST elevation myocardial infarction [STEMI] and expanded criteria and their combination to predict ATCO on ICA.

RESULTS

152 consecutive patients (mean age 58 years, 76% male) met inclusion criteria. AMM was present in 77%; and 42% had ATCO on ICA. Sensitivity, specificity, PPV, NPV using classic STEMI criteria were 50%, 98%, 94%, 72% (c-statistic 0.74); whereas for combined (STEMI + expanded) criteria they were 69%, 88%, 81%, 79% respectively (c-statistic 0.79). Inter-observer agreement (kappa) was 0.7 for classic STEMI criteria, and 0.66 for combined criteria. Agreement between readers was consistently higher when ATCO was absent and with NMM (kappa 0.78), but lower in AMM (kappa 0.6).

CONCLUSIONS

Despite experienced ECG readers, there was only modest overall concordance in predicting ATCO in the context of resuscitated OHCA. Significant interobserver variations were noted dependent on metabolic milieu and angiographic ATCO. These observations fundamentally question the role of the 12-lead ECG as primary triaging tool for early angiography among patients with OHCA.

Ischemic ST-Segment Depression Maximal in V1-V4 (Versus V5-V6) of Any Amplitude Is Specific for Occlusion Myocardial Infarction (Versus Nonocclusive Ischemia)

Background Occlusion myocardial infarctions (OMIs) of the posterolateral walls are commonly missed by ST-segment-elevation myocardial infarction (STEMI) criteria, with >50% of patients with circumflex occlusion not receiving emergent reperfusion and experiencing increased mortality. ST-segment depression maximal in leads V1-V4 (STDmaxV1-4) has been suggested as an indicator of posterior OMI. Methods and Results We retrospectively reviewed a high-risk population with acute coronary syndrome. OMI was defined from prior studies as a culprit lesion with TIMI (Thrombolysis in Myocardial Infarction) 0 to 2 flow or TIMI 3 flow plus peak troponin T >1.0 ng/mL or troponin I >10 ng/mL. STEMI was defined by the Fourth Universal Definition of Myocardial Infarction. ECGs were interpreted blinded to outcomes. Among 808 patients, there were 265 OMIs, 108 (41%) meeting STEMI criteria. A total of 118 (15%) patients had "suspected ischemic" STDmaxV1-4, of whom 106 (90%) had an acute culprit lesion, 99 (84%) had OMI, and 95 (81%) underwent percutaneous coronary intervention. Suspected ischemic STDmaxV1-4 had 97% specificity and 37% sensitivity for OMI. Of the 99 OMIs detected by STDmaxV1-4, 34% had <1 mm ST-segment depression, and only 47 (47%) had accompanying STEMI criteria, of which 17 (36%) were identified a median 1.00 hour earlier by STDmaxV1-4 than STEMI criteria. Despite similar infarct size, TIMI flow, and coronary interventions, patients with STEMI(-) OMI and STDmaxV1-4 were less likely than STEMI(+) patients to undergo catheterization within 90 minutes (46% versus 68%; P=0.028). Conclusions Among patients with high-risk acute coronary syndrome, the specificity of ischemic STDmaxV1-4 was 97% for OMI and 96% for OMI requiring emergent percutaneous coronary intervention. STEMI criteria missed half of OMIs detected by STDmaxV1-4. Ischemic STDmaxV1-V4 in acute coronary syndrome should be considered OMI until proven otherwise.

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.

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.

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.

Recognizing electrocardiographically subtle occlusion myocardial infarction and differentiating it from mimics: Ten steps to or away from cath lab

It is increasingly evident that the ST-segment elevation (STE) myocardial infarction (MI)/non-STEMI paradigm that equates STEMI with acute coronary occlusion (ACO) is deceptive. This unfortunate paradigm, adhered to by the current guidelines, misses at least one-fourth of the ACOs, and unnecessarily over-triages a similar fraction of the patients to the catheterization laboratory. Accordingly, we have been calling for a new paradigm, the occlusion/nonocclusion MI (OMI/NOMI). Although this new OMI/NOMI paradigm is not limited to an electrocardiogram (ECG), the ECG will remain the cornerstone of this new paradigm because of its speed, repeatability, noninvasive nature, wide availability, and high diagnostic power for OMI. This review provides a step-by-step approach to ECG for the diagnosis of OMI.

Efficacy of the social-emotional learning foundations curriculum for kindergarten and first grade students at risk for emotional and behavioral disorders

Researchers have shown that children's social-emotional growth is inextricably connected to academic learning. We developed the Social-Emotional Learning Foundations (SELF) intervention, a Grade K-1 curriculum merging social-emotional learning (SEL) and literacy instruction, to promote language supported self-regulation, specifically for primary grade children at early risk for emotional or behavioral difficulties. We report findings from a pretest-posttest cluster randomized efficacy trial with one fixed between-subjects factor to test the effects of teacher-delivered SEL instruction against those of business as usual (BAU). We recruited 163 kindergarten (K) and 141 first grade teachers from 52 schools across 11 school districts within one southeastern state. Our student sample (n = 1154) consisted of 627 kindergarteners and 527 first graders identified by teachers as at risk for internalizing or externalizing emotional and behavioral problems using the Systematic Screening for Behavioral Disorders; 613 of these students participated in the SELF condition and 541 participated in the BAU condition. We randomly assigned schools to SELF or BAU and used a multilevel model with three levels (i.e., children, classrooms, schools) to analyze data on subscales of six (four teacher-report and two direct) assessments related to self-regulation, social-emotional learning, social-emotional vocabulary, and general behavioral functioning. We found positive main effects of SELF compared to BAU on all but one measure, with effect sizes (calculated using Hedges' g) ranging from 0.20 to 0.65. Findings provide evidence for guiding future SEL intervention research and informing practice to improve student outcomes, particularly for children at risk for behavior problems.

Accuracy of OMI ECG findings versus STEMI criteria for diagnosis of acute coronary occlusion myocardial infarction

OBJECTIVE

In the STEMI paradigm of Acute Myocardial Infarction (AMI), many NSTEMI patients have unrecognized acute coronary occlusion MI (OMI), may not receive emergent reperfusion, and have higher mortality than NSTEMI patients without occlusion. We have proposed a new OMI vs. Non-Occlusion MI (NOMI) paradigm shift. We sought to compare the diagnostic accuracy of OMI ECG findings vs. formal STEMI criteria for the diagnosis of OMI. We hypothesized that blinded interpretation for predefined OMI ECG findings would be more accurate than STEMI criteria for the diagnosis of OMI.

METHODS

We performed a retrospective case-control study of patients with suspected acute coronary syndrome. The primary definition of OMI was either 1) acute TIMI 0-2 flow culprit or 2) TIMI 3 flow culprit with peak troponin T ≥ 1.0 ng/mL or I ≥ 10.0 ng/mL.

RESULTS

808 patients were included, of whom 49% had AMI (33% OMI; 16% NOMI). Sensitivity, specificity, and accuracy of STEMI criteria vs Interpreter 1 using OMI ECG findings among 808 patients were 41% vs 86%, 94% vs 91%, and 77% vs 89%, and for Interpreter 2 among 250 patients were 36% vs 80%, 91% vs 92%, and 76% vs 89%. STEMI(-) OMI patients had similar infarct size and mortality as STEMI(+) OMI patients, but greater delays to angiography.

CONCLUSIONS

Blinded interpretation using predefined OMI ECG findings was superior to STEMI criteria for the ECG diagnosis of Occlusion MI. These data support further investigation into the OMI vs. NOMI paradigm and suggest that STEMI(-) OMI patients could be identified rapidly and noninvasively for emergent reperfusion using more accurate ECG interpretation.

Time for a new paradigm shift in myocardial infarction

The ST-elevation myocardial infarction (STEMI)/non-STEMI paradigm per the current guidelines has important limitations. It misses a substantial proportion of acute coronary occlusions (ACO) and results in a significant amount of unnecessary catheterization laboratory activations. It is not widely appreciated how poor is the evidence base for the STEMI criteria; the recommended STEMI cutoffs were not derived by comparing those with ACO with those without and not specifically designed for distinguishing patients who would benefit from emergency reperfusion. This review aimed to discuss the origins, evidence base, and limitations of STEMI/non-STEMI paradigm and to call for a new paradigm shift to the occlusion MI (OMI)/non-OMI.

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.

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.