Occlusion Myocardial Infarction Research Articles

Chronotropic incompetence predictors in a Cardiac Rehabilitation Program following myocardial infarction

Abstract Background Chronotropic incompetence (CI) has been described in many patients after myocardial infarction (MI) and can negatively affect physical capacity, especially if negative chronotropic therapy (NCT) is prescribed. Prediction of CI after MI could be useful for tailored medical therapy and exercise training prescription. Purpose We aim to predict CI on initial exercise ECG testing (ExECG) in MI patients submitted to a Cardiac Rehabilitation Program (CRP). Methods Patients with ST-segment elevation MI (STEMI) or occlusion MI (OMI) submitted to our CRP were prospectively included. After an initial follow-up phase of clinical stabilization and medical therapy optimization, ExECG (conventional or cardiopulmonary) was performed. Peak VO2 and chronotropic reserve index [(maximum heart rate – resting heart rate) / ([220-age] – resting heart rate)] were measured. Chronotropic incompetence was defined as <80% chronotropic reserve index (≤62% in patients treated with NCT). Pharmacological therapy with NCT was registered in absolute and equivalent doses per bisoprolol 2.5mg units (B2.5EU). The effect of NCT on CI was studied by ANOVA test, and predictors of CI by binary logistic regression analysis. The model was tested by area under the curve (AUC) analysis in receiver operating characteristic curves. A p<0.05 was considered statistically significant. Results The cohort comprised 143 patients, mostly middle-aged (mean 60.3±15.8 years) males (85.3%) with anterior or inferior MI (44.8% and 44.8%, respectively). Mean left ventricular ejection fraction (LVEF) was 52.4±10.6% and 36.4% depicted LVEF<50%. NCT was prescribed in most patients (n=125, 87.4%), and median equivalent dose was 1 [0.5-1] B2.5EU. CI was noted on more than half of the population on initial ExECG (n=75, 52.4%). Higher NCT doses per B2.5EU associated with worse mean chronotropic reserve indexes: 72.8±24.4% in patients without NCT; 71.7±18.7% in patients with 0.5 B2.5EU; 62.8±18.2% in patients with 1 B2.5EU; and 58.7±16.9% in patients with ≥2 B2.5EU (ANOVA p=0.04). On binary logistic regression analysis, only two variables were independent predictors of CI: diabetes mellitus (HR 3.02 [1.26-7.23], p=0.01) and LVEF (0.97 [0.94-0.99] per increased %, p=0.04). However, the predictive power of the model was poor (AUC 0.62 [0.53-0.71], p=0.01). Conclusions Chronotropic incompetence after a MI is associated with lower LVEF, diabetes mellitus, and higher doses of NCT. Although significant interindividual variability exists in chronotropic response in the post-infarction period, these factors could be taken into consideration for tailored prescription (or deprescription), especially in patients who may not derive any prognostic benefit from NCT.Chronotropic incompetence in CRP

Prolonged dual antiplatelet therapy in an extended Phase 3 Cardiac Rehabilitation Program following myocardial infarction

Abstract Background Prolonged dual antiplatelet therapy (P-DAPT) is recommended in selected patients beyond 12 months after myocardial infarction (MI). Several risk scores have been developed to estimate the ischemic and bleeding risk and individualize the decision-making process. Purpose We aim to analyse the factors, including ischemic and bleeding scores, associated with P-DAPT prescription in MI patients submitted to a Cardiac Rehabilitation Program (CRP). Methods Patients with ST-segment elevation MI (STEMI) or occlusion MI (OMI) who were submitted to our CRP underwent Phase 2 CRP and were prospectively included. Patients were followed in an extended Phase 3 CRP, and a follow-up visit was scheduled 12 months after hospital discharge. Baseline and follow-up clinical characteristics and treatment after the 12-months visit were registered, specifically P-DAPT. Ischemic (DAPT score) and bleeding (PRECISE-DAPT and CRUSADE scores) risk scores were retrospectively computed. Predictors of P-DAPT were studied by univariate analysis and multivariable binary logistic regression. The model was tested by area under the curve (AUC) analysis in receiver operating characteristic curves. A p<0.05 was considered statistically significant. Results The cohort comprised 78 patients (mean age 61±11 years, 84.6% male). Anterior and inferior STEMI were the most common presentations (43.6% and 47.4%, respectively). The most prevalent cardiovascular risks factors were hypercholesterolemia (85.9%), hypertension (53.8%), smoking habit (46.2%) and diabetes mellitus (23.1%). P-DAPT was prescribed in 39 (50%) patients beyond 12 months after MI, primarily combining aspirin 100mg o.d. and ticagrelor 60mg b.i.d. (n=33, 84.6%). On multivariate analysis, variables associated with P-DAPT prescription were hypertension (HR 4.92 [1.37-17.68], p=0.02), diabetes mellitus (HR 5.13 [1.09-24.09], p=0.04), multivessel disease (HR 5.54 [1.55-19.79], p=0.008), and stent diameter <3mm (HR 5.46 [1.25-23.78], p=0.02). The predictive power of the model was excellent (AUC 0.88 [0.8-0.96], p<0.001). Ischemic (DAPT score) and bleeding (PRECISE-DAPT and CRUSADE) risk scores were not associated with P-DAPT neither in continuous form nor by using their standard cutoffs (≥2, <25 and >40 points, respectively). Conclusions In a real-life cohort of MI patients undergoing extended Phase 3 Cardiac Rehabilitation, P-DAPT was prescribed in half of the population. Clinical (hypertension and diabetes mellitus) and angiographic (multivessel disease and stent diameter <3mm) variables straightforwardly predict the likelihood of receiving P-DAPT, unlike ischemic and bleeding risk scores. The clinical consequences of this management should be further explored.Prolonged DAPT in Cardiac Rehabilitation

TCT-194 Evaluating Artificial Intelligence Prediction of Occlusive Myocardial Infarction From 12 Lead ECGs After Resuscitated Out of Hospital Cardiac Arrest

TCT-194 Evaluating Artificial Intelligence Prediction of Occlusive Myocardial Infarction From 12 Lead ECGs After Resuscitated Out of Hospital Cardiac Arrest

OMI/NOMI: Time for a New Classification of Acute Myocardial Infarction.

Forty percent of patients with acute coronary occlusion myocardial infarction (OMI) do not present with STEMI criteria, which delays their treatment and increases morbidity and mortality. The need to identify these patients promptly is crucial, and this sets the stage for the proposed reclassification. Many of these patients can be identified by other ECG and clinical features. Background/Objectives: We sought to evaluate cases of STEMI and NSTEMI that result in OMI. Additionally, we focused on the consequences of delayed revascularization in NSTEMI patients with acute coronary occlusion (NSTEMI-OMI). Methods: The study is a retrospective analysis conducted on 334 patients who underwent coronary angiography for acute coronary syndrome at UHC "Mother Teresa", Tirana, Albania, during January-May 2023. "OMI was defined as an acute culprit lesion with TIMI 0-2 flow, or an acute culprit lesion with TIMI 3 flow intervened upon and with highly elevated troponin (cTnI > 10.0 ng/mL, hs-cTnI > 5000 ng/L)". The presence or absence of STEMI criteria were determined in the final diagnosis written on the chart by a cardiologist using the third universal definition of MI. Ejection fraction (EF), total ischemia time, length of stay, and complications were compared between groups. Mechanical complications include acute ventricular failure, cardiogenic shock, rupture of the interventricular septum, rupture of the free wall, rupture of the papillary muscle, and pericarditis. Electrical complications include ventricular arrhythmias, supraventricular arrhythmias, and atrioventricular and interventricular blocks. Results: There were 334 patients included, 98 (29.3%) of whom were NSTEMI-OMI patients. Ninety-six patients (40%) of OMI patients did not fulfill the STEMI criteria. Only 11 patients (11%) of STEMI(-)OMI had PCI performed within the first 12 h vs. 76 patients (77%) with STEMI(+)OMI, p < 0.001. There was no difference in the percent of patients requiring PCI between the STEMI(+)OMI 98 patients (93%) and STEMI(-)OMI 87 patients (89%) (p = 0.496). The overall in-hospital mortality was 19 patients (5.7%), with subgroup mortality of 14 patients (4.2%) with STEMI(+)OMI, 2 patients (0.6%) with STEMI(+) NOMI, and 3 patients (0.9%) with STEMI(-)OMI, 0% STEMI(-)NOMI, (p = 0.013). Patients with mechanical complications included 67 patients (46.8%) with STEMI(+)OMI and 45 patients (46.4%) with STEMI(-)OMI. In addition, 26 patients (18.5%) with STEMI(+)OMI and 13 patients (13.1%) with STEMI(-)OMI developed electrical complications. Conclusions: STEMI(-)OMI patients had significant delays in catheterization, yet had angiographic findings, rates of PCI, and complications similar to STEMI(+)OMI. These data add further support to refocusing the paradigm of acute MI to improve recognition and rapid reperfusion of all OMIs, rather than only those with STEMI criteria.

Clinical usability of deep learning-based saliency maps for occlusion myocardial infarction identification from the prehospital 12-Lead electrocardiogram

IntroductionDeep learning (DL) models offer improved performance in electrocardiogram (ECG)-based classification over rule-based methods. However, for widespread adoption by clinicians, explainability methods, like saliency maps, are essential. MethodsOn a subset of 100 ECGs from patients with chest pain, we generated saliency maps using a previously validated convolutional neural network for occlusion myocardial infarction (OMI) classification. Three clinicians reviewed ECG-saliency map dyads, first assessing the likelihood of OMI from standard ECGs and then evaluating clinical relevance and helpfulness of the saliency maps, as well as their confidence in the model's predictions. Questions were answered on a Likert scale ranging from +3 (most useful/relevant) to −3 (least useful/relevant). ResultsThe adjudicated accuracy of the three clinicians matched the DL model when considering area under the receiver operating characteristics curve (AUC) and F1 score (AUC 0.855 vs. 0.872, F1 score = 0.789 vs. 0.747). On average, clinicians found saliency maps slightly clinically relevant (0.96 ± 0.92) and slightly helpful (0.66 ± 0.98) in identifying or ruling out OMI but had higher confidence in the model's predictions (1.71 ± 0.56). Clinicians noted that leads I and aVL were often emphasized, even when obvious ST changes were present in other leads. ConclusionIn this clinical usability study, clinicians deemed saliency maps somewhat helpful in enhancing explainability of DL-based ECG models. The spatial convolutional layers across the 12 leads in these models appear to contribute to the discrepancy between ECG segments considered most relevant by clinicians and segments that drove DL model predictions.

Prevalence and Predictors of Occlusive Myocardial Infarction in Patients Presenting With Non-ST-Elevation Acute Coronary Syndrome in Duhok, Iraq: A Cross-Sectional Study.

Background and aim Myocardial infarction is a major global health issue and the leading cause of death. Non-ST segment elevation acute coronary syndrome (NSTE-ACS) could behave like ST-segment elevation ACS in terms of causing total or near total occlusion of the coronary artery and leading to occlusive myocardial infarction (OMI). This study aims to assess OMI prevalence and associated factors in NSTE-ACS patients in Duhok, Iraq, to improve diagnosis and treatment outcomes. Materials and methods This prospective cross-sectional study, conducted at Azadi Heart Center and Zakho Teaching Hospital from March 2023 to March 2024, included 189 NSTE-ACS patients undergoing coronary angiography. Data collection encompassed demographics, clinical profiles, electrocardiographic (ECG) patterns, cardiac biomarkers, and angiographic outcomes. Patients were categorized into those with and without occlusive myocardial infarction (OMI). Results A total of 189 NSTE-ACS patients with a mean age of 58.65 (±10.38 SD) years were enrolled in the study. The overall OMI rate was 29.63%. OMI patients were older and had a higher prevalence of hypertension, dyslipidemia, and a family history of ischemic heart disease (IHD). Significant ECG changes associated with OMI included biphasic T-wave inversion and ST depression in specific leads. Marked elevation in troponin levels was also noted in OMI patients. The left anterior descending (LAD) artery was the most common culprit artery. Conclusions About one-quarter of our study cohort exhibited OMI. The condition was linked to clinical, ECG, and elevated troponin levels. The study underscores the importance of promptly recognizing occlusive myocardial infarction (OMI) in NSTE-ACS patients for better outcomes. Regular audits are imperative to augment awareness among healthcare professionals at cardiac centers regarding updated protocols and guidelines.

Saliency maps to enhance explainability of occlusion myocardial infarction classification among pre-hospital chest pain patients

Saliency maps to enhance explainability of occlusion myocardial infarction classification among pre-hospital chest pain patients

A Case of Occlusive Myocardial Infarction Caused by Nephrotic Syndrome in a 26-Year-Old Type 1 Diabetic

A Case of Occlusive Myocardial Infarction Caused by Nephrotic Syndrome in a 26-Year-Old Type 1 Diabetic

Detection of acute coronary occlusion with a novel mobile electrocardiogram device: a pilot study.

Many portable electrocardiogram (ECG) devices have been developed to monitor patients at home, but the majority of these devices are single lead and only intended for rhythm disorders. We developed the miniECG, a smartphone-sized portable device with four dry electrodes capable of recording a high-quality multi-lead ECG by placing the device on the chest. The aim of our study was to investigate the ability of the miniECG to detect occlusive myocardial infarction (OMI) in patients with chest pain. Patients presenting with acute chest pain at the emergency department of the University Medical Center Utrecht or Meander Medical Center, between May 2021 and February 2022, were included in the study. The clinical 12-lead ECG and the miniECG before coronary intervention were recorded. The recordings were evaluated by cardiologists and compared the outcome of the coronary angiography, if performed. A total of 369 patients were measured with the miniECG, 46 of whom had OMI. The miniECG detected OMI with a sensitivity and specificity of 65 and 92%, compared with 83 and 90% for the 12-lead ECG. Sensitivity of the miniECG was similar for different culprit vessels. The miniECG can record a multi-lead ECG and rule-in ST-segment deviation in patients with occluded or near-occluded coronary arteries from different culprit vessels without many false alarms. Further research is required to add automated analysis to the recordings and to show feasibility to use the miniECG by patients at home.

Beyond STEMI-NSTEMI Paradigm: Dante Pazzanese's Proposal for Occlusion Myocardial Infarction Diagnosis.

Although the existing framework for classifying acute myocardial infarction (AMI) into STEMI and NSTEMI has been beneficial, it is now considered to be falling short in addressing the complexity of acute coronary syndromes. The study aims to scrutinize the current STEMI-NSTEMI paradigm and advocate for a more nuanced framework, termed as occlusion myocardial infarction (OMI) and non-occlusion myocardial infarction (NOMI), for a more accurate diagnosis and management of AMI. A comprehensive analysis of existing medical literature was conducted, with a focus on the limitations of the STEMI-NSTEMI model. The study also outlines a new diagnostic approach for patients presenting with chest pain in emergency settings. The traditional STEMI-NSTEMI model falls short in diagnostic precision and effective treatment, especially in identifying acute coronary artery occlusions. The OMI-NOMI framework offers a more anatomically and physiologically accurate model, backed by a wealth of clinical research and expert opinion. It underscores the need for quick ECG assessments and immediate reperfusion therapies for suspected OMI cases, aiming to improve patient outcomes. The OMI-NOMI framework offers a new avenue for future research and clinical application. It advocates for a more comprehensive understanding of the underlying mechanisms of acute coronary syndromes, leading to individualized treatment plans. This novel approach is expected to ignite further scholarly debate and research, particularly in the Brazilian cardiology sector, with the goal of enhancing diagnostic accuracy and treatment effectiveness in AMI patients.

Why do we keep missing left circumflex artery myocardial infarctions?

BackgroundDiagnosis of left circumflex artery (LCx) myocardial infarctions via 12‑lead electrocardiogram (ECG) has posed a challenge to healthcare professionals for many years. Methods and resultsA retrospective observational study was performed to analyze patients admitted with myocardial infarction. The study used electronic medical records and specific ICD-10 codes to identify eligible patients, resulting in 2032 encounters. After independent adjudication of cardiac biomarkers, coronary angiography, and electrocardiographic changes, a final patient population of 58 encounters with acute occlusion myocardial infarction (OMI) with a culprit LCx lesion was established. OMI was defined as a lesion with either thrombolysis in myocardial infarction flow (TIMI) 0–2 or TIMI 3 with Troponin I > 1 ng/mL (Reference range 0.00–0.03 ng/mL). ECGs of these patients were then independently evaluated and grouped into 8 different classifications based on the presence or absence of ST elevation and/or depression in corresponding leads. ECG patterns and anatomical characteristics (proximal or distal to the first obtuse marginal artery) of the LCx lesions were then correlated. The appropriateness of triage and delay in reperfusion therapy were also assessed. Those with a left dominant or codominant circulation, and with LCx lesions proximal to the first obtuse marginal artery, were more likely to present with no or subtle ST-segment changes that led to delays in reperfusion therapy. ConclusionsPatients with left or codominant coronary artery circulation, with OMI proximal to the first obtuse marginal artery, may be less likely to have “classic” findings of ST-segment elevation on ECG due to cancellation forces in the limb leads.

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

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. 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)]. 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.

ECG-based deep learning for detecting epicardial coronary occlusion in acute myocardial infarction

Abstract Background One third of Non-ST-elevation myocardial infarction (NSTEMI) patients have occlusion myocardial infarction (OMI) associated with poor short- and long-term outcomes due to delayed invasive management. Purpose We sought to develop and externally validate a versatile artificial intelligence (AI)-model detecting OMI on single standard 12-lead electrocardiograms (ECGs) and compare its performance to existing state-of-the-art criteria. Methods An AI model was developed using 18,616 ECGs from 10,692 unique contacts (22.9% OMI) of 10,543 patients (age 66±14 years, 65.9% males) with acute coronary syndrome (ACS) originating from an international database and a tertiary care center. This AI model was tested on a holdout set of 3,254 ECGs from 2,263 unique contacts (20% OMI) of 2,222 patients (age 62±14 years, 67% males) and compared with STEMI criteria and annotations of ECG experts in detecting OMI on 12-lead ECGs using sensitivity, specificity, and predictive values. Results The OMI AI model achieved an AUROC of 0.941 (95% CI: 0.926, 0.954) in identifying the primary outcome of OMI on 12-lead ECGs in the holdout set [Figure 1A], yielding robust performance across genders and age subgroups (ranging from 0.907 to 0.951 AUROC) [Figure 1B]. Sensitivity in detecting OMI was significantly higher for OMI AI model compared to STEMI criteria (82.6% [95% CI: 78.9%, 86.1%] vs. 34.4% [95% CI: 30.0%, 38.8%]) and statistically equal compared to ECG experts 75.9% [95% CI: 71.9%, 80%] in identifying OMI [Table 1]. The OMI AI Model showed statistically superior performance compared to STEMI criteria and equal (non-inferior) performance to ECG experts when evaluated using industry-standard metrics. Conclusions This sizeable validation of an AI ECG model demonstrates the ability of AI to detect acute OMI and suggests a potential in improving ACS patient triage and need for immediate revascularization.AI Model Performance on Holdout SetBenchmark Comparison

Poor prognosis of total culprit artery occlusion in patients presenting with NSTEMI

Abstract Background The prognosis of STEMI and NSTEMI patients stratified according to invasive angiographic findings of an occluded culprit coronary, i.e., occlusion myocardial infarction (OMI), remains unclear. Purpose This retrospective single-center study aimed to compare the clinical presentation, management, and all-cause mortality of STEMI, NSTEMI-OMI, and NSTEMI-Non-OMI (NSTEMI-NOMI). Methods Demographics, clinical and procedural data were pooled for all patients with an index ACS event between January 2011 and May 2021. Patients were classified according to the presence of OMI determined by angiography and troponin. All-cause mortality was compared by Kaplan Meier analysis at 1-year and landmark analysis up to 5 years after the ACS event. Results A total of 9,943 patients [64.1 ± 14.2 years, 62.2% males] were included and classified into STEMI (n=507), NSTEMI-OMI (n=685), NSTEMI-NOMI (n=1,535) and MI ruled-out (n=7,216). Median time to coronary angiography (CAG) was 1.4 hours for STEMI compared to 16.3 and 45.8 hours for NSTEMI-OMI and NSTEMI-NOMI, respectively (p&amp;lt;0.001) [Figure 1]. At one year, all-cause mortality occurred in 9.9% of STEMI patients vs. 18.1% of NSTEMI-OMI patients (hazard ratio, 1.84; 95% CI, 1.32 to 2.55; p&amp;lt;0.001) and 7.9% of NSTEMI-NOMI patients (hazard ratio, 0.79; 95% CI, 0.56 to 1.09; p=0.150) [Figure 2]. An unfavorable mortality outcome for NSTEMI-OMI compared to STEMI was maintained in the age and gender-adjusted analyses and landmark analysis beyond 1-year (hazard ratio, 2.59; 95% CI, 1.75 to 3.82; p&amp;lt;0.001). Conclusions Despite similar baseline demographics and angiographic findings, patients with NSTEMI-OMI have increased delays to reperfusion and unfavorable short and long-term all-cause mortality compared to STEMI. These findings support the need for more accurate and timely identification of NSTEMI-OMI.Time to Coronary Angiography (CAG)All-cause 1-year Mortality

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

BackgroundElectrocardiographic 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. MethodsThe 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. ResultsIn 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%). ConclusionsOur 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.

Ventricular Tachycardia.

This scenario was developed to educate emergency medicine residents on the diagnosis and management of ventricular tachycardia (VT) that is refractory to single dose anti-arrhythmic management. Electrical storm, defined as three or more episodes of sustained VT, ventricular fibrillation, or appropriate shocks from an implantable cardioverter defibrillator within 24 hours,1 has a mortality rate up to 14% in the first 48 hours.2 Ventricular tachycardia may present in a heterogenous fashion, not only with stable versus unstable clinical presentations, but also with different electrocardiographic morphologies and etiologies.1 Understanding how to rapidly diagnose, treat, and utilize second or third-line treatments is vital in the setting of refractory ventricular tachycardia rather than relying on the success of first-line agents. Appreciation for what medications are readily available in your crash cart and medication dispensing cabinet is critical for timely management for refractory ventricular tachycardia. At the conclusion of the simulation session, learners will be able to: 1) identify the different etiologies of VT, including structural heart disease, acute ischemia, and acquired or congenital QT syndrome; 2) describe confounding factors of VT, such as electrolyte abnormalities and sympathetic surge; 3) describe how to troubleshoot an unsuccessful synchronized cardioversion, including checking equipment connections, increasing delivered energy, and changing pad placement; 4) compare and contrast treatments of VT based on suspected underlying etiology; 5) describe reasons to activate the cardiac catheterization lab other than occlusive myocardial infarction; and 6) identify appropriate disposition of the patient to the cardiac catheterization lab. This session was conducted using high-fidelity simulation, followed by a debriefing session and lecture on the diagnosis, differential diagnosis, and management of VT. Debriefing methods may be left to the discretion of participants, but the authors have utilized advocacy-inquiry techniques. This scenario may also be run as an oral board case. Our residents are provided a survey at the completion of the debriefing session so they may rate different aspects of the simulation, as well as provide qualitative feedback on the scenario. The local institution's simulation center's electronic feedback form is based on the Center of Medical Simulation's Debriefing Assessment for Simulation in Healthcare (DASH) Student Version Short Form3 with the inclusion of required qualitative feedback if an element was scored less than a 6 or 7. Twelve learners completed a feedback form. This session received 6 and 7 scores (consistently effective/very good and extremely effective/outstanding, respectively) other than three isolated 5 scores. The lowest average score was 6.67 for "Before the simulation, the instructor set the stage for an engaging learning experience." The highest average score was 7 for "The instructor helped me see how to improve or how to sustain good performance." The form also includes an area for general feedback about the case at the end. Illustrative examples of feedback include: "Excellent care and debrief." Specific scores are available upon request. This is a cost-effective method for reviewing VT diagnosis and management. The case may be modified for appropriate audiences, such as describing what medications may be readily available in a free-standing emergency department or pre-hospital setting. Medical simulation, ventricular tachycardia, cardiac emergencies, dysrhythmias, cardiology, emergency medicine.

Sgarbossa Negative With Concordant ST Depression—Occlusion Myocardial Infarction Diagnosed by the BARCELONA Algorithm

Sgarbossa Negative With Concordant ST Depression—Occlusion Myocardial Infarction Diagnosed by the BARCELONA Algorithm

Missing occlusions: Quality gaps for ED patients with occlusion MI

BackgroundST-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. MethodsThis 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. ResultsAmong 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.” ConclusionsSTEMI 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 proposal for better visualization of Aslanger pattern

In the context of reporting a case of a patient with Aslanger pattern, the application of additional leads in the frontal plane is proposed to facilitate the recognition of this condition - indicating occlusive myocardial infarction - as eligible for the current ST-segment elevation myocardial infarction (STEMI) criteria and consequently for immediate reperfusion strategy recommendation.

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.