Comparison of the ST-Elevation Myocardial Infarction (STEMI) vs. NSTEMI and Occlusion MI (OMI) vs. NOMI Paradigms of Acute MI

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.

Artificial neural networks for ECG interpretation in acute coronary syndrome: A scoping review

INTRODUCTION

The electrocardiogram (ECG) is a crucial diagnostic tool in the Emergency Department (ED) for assessing patients with Acute Coronary Syndrome (ACS). Despite its widespread use, the ECG has limitations, including low sensitivity of the STEMI criteria to detect Acute Coronary Occlusion (ACO) and poor inter-rater reliability. Emerging ECG features beyond the traditional STEMI criteria show promise in improving early ACO diagnosis, but complexity hinders widespread adoption. The potential integration of Artificial Neural Networks (ANN) holds promise for enhancing diagnostic accuracy and addressing reliability issues in ECG interpretation for ACO symptoms.

METHODS

Ovid MEDLINE, CINAHL, EMBASE, Cochrane, PubMed and Scopus were searched from inception through to 8th of December 2023. A thorough search of the grey literature and reference lists of relevant articles was also performed to identify additional studies. Articles were included if they reported the use of ANN for ECG interpretation of Acute Coronary Syndrome in the Emergency Department patients.

RESULTS

The search yielded a total of 244 articles. After removing duplicates and excluding non-relevant articles, 14 remained for analysis. There was significant heterogeneity in the types of ANN models used and the outcomes assessed, making direct comparisons challenging. Nevertheless, ANN appeared to demonstrate higher accuracy than physician interpreters for the evaluated outcomes and this proved independent of both specialty and years of experience.

CONCLUSIONS

The interpretation of ECGs in patients with suspected ACS using ANN appears to be accurate and potentially superior when compared to human interpreters and computerised algorithms. This appears consistent across various ANN models and outcome variables. Future investigations should emphasise ANN interpretation of ECGs in patients with ACO, where rapid and accurate diagnosis can significantly benefit patients through timely access to reperfusion therapies.

Postdilatation after stent deployment during primary percutaneous coronary intervention: a systematic review and meta-analysis

BACKGROUND

The utilization of postdilatation in primary percutaneous coronary intervention (PCI) is feared to induce suboptimal coronary blood flow and compromise the outcome of the patients. This meta-analysis sought to verify whether postdilatation during primary PCI is associated with worse angiographic or long-term clinical outcomes.

METHODS

Systematic literature searches were conducted on PubMed, The Cochrane Library, ClinicalTrials.gov, EBSCO, and Europe PMC on 10 March 2024. Eligible studies reporting the outcomes of postdilatation among ST-segment elevation myocardial infarction patients were included. The primary outcome was no-reflow condition during primary PCI based on angiographic finding. The secondary clinical outcome was major adverse cardiovascular events (MACEs) comprising all-cause death, myocardial infarction, target vessel revascularization (TVR), and stent thrombosis.

RESULTS

Ten studies were finally included in this meta-analysis encompassing 3280 patients, which was predominantly male (76.6%). Postdilatation was performed in 40.7% cases. Postdilatation was associated with increased risk of no-reflow during primary PCI [Odd Ratio (OR) = 1.33, 95% Confidence Interval (CI): 1.12-1.58; P = .001)]. Conversely, postdilatation had a tendency to reduce MACE (OR = 0.70, 95% CI: 0.51-0.97; P = .03) specifically in terms of TVR (OR = 0.41, 95% CI: 0.22-0.74; P = .003). No significant differences between both groups in relation to mortality (OR = 0.58, 95% CI: 0.32-1.05; P = .07) and myocardial infarction (OR = 1.5, 95% CI: 0.78-2.89; P = .22).

CONCLUSIONS

Postdilatation after stent deployment during primary PCI appears to be associated with an increased risk of no-reflow phenomenon after the procedure. Nevertheless, postdilatation strategy has demonstrated a significant reduction in MACE over the course of long-term follow-up. Specifically, postdilatation significantly decreased the occurrence of TVR. Key messages: What is already known on this topic?  Optimizing stent deployment by performing postdilatation during percutaneous coronary intervention (PCI) is essential for long-term clinical outcomes. However, its application during primary PCI is controversial due to the fact that it may provoke distal embolization and worsen coronary blood flow. What this study adds?  In this systematic review and meta-analysis of 10 studies, we confirm that postdilatation during primary PCI is associated with worse coronary blood flow immediately following the procedure. On the contrary, this intervention proves advantageous in improving long-term clinical outcomes, particularly in reducing target vessel revascularization. How this study might affect research, practice, or policy?  Given the mixed impact of postdilatation during primary PCI, this strategy should only be applied selectively. Future research should focus on identifying patients who may benefit from such strategy.

Smoking and Hypertriglyceridemia Predict ST-Segment Elevation Myocardial Infarction in Kosovo Patients with Acute Myocardial Infarction

BACKGROUND

Myocardial infarction (MI), presented as ST-segment elevation MI (STEMI) and non-ST-segment elevation MI (NSTEMI), is influenced by atherosclerosis risk factors.

AIM

The aim of this study was to assess the patterns of presentation of patients with acute MI in Kosovo.

METHODS

This was a cross-sectional study conducted at the University Clinical Center of Kosovo, which included all patients hospitalized with acute MI over a period of 7 years.

RESULTS

Among the 7353 patients admitted with acute MI (age 63 ± 12 years, 29% female), 59.4% had STEMI and 40.6% had NSTEMI. The patients with NSTEMI patients less (48.3% vs. 54%, p < 0.001), but more of them had diabetes (37.8% vs. 33.6%, p < 0.001), hypertension (69.6% vs. 63%, p < 0.001), frequently had a family history of coronary artery disease (CAD) (40% vs. 38%, p = 0.009), and had more females compared to the patients with STEMI (32% vs. 27%, p < 0.001). The patients with NSTEMI underwent less primary percutaneous interventions compared with the patients with STEMI (43.6% vs. 55.2%, p < 0.001). Smoking [1.277 (1.117-1.459), p ˂ 0.001] and high triglycerides [0.791 (0.714-0.878), p = 0.02] were independent predictors of STEMI.

CONCLUSIONS

In Kosovo, patients with STEMI are more common than those with NSTEMI, and they were mostly males and more likely to have diabetes, hypertension, and a family history of CAD compared to those with NSTEMI. Smoking and high triglycerides proved to be the strongest predictors of acute STEMI in Kosovo, thus highlighting the urgent need for optimum atherosclerosis risk control and education strategies.

Preclinical identification of acute coronary syndrome without high sensitivity troponin assays using machine learning algorithms

Preclinical management of patients with acute chest pain and their identification as candidates for urgent coronary revascularization without the use of high sensitivity troponin essays remains a critical challenge in emergency medicine. We enrolled 2760 patients (average age 70 years, 58.6% male) with chest pain and suspected ACS, who were admitted to the Emergency Department of the University Hospital Tübingen, Germany, between August 2016 and October 2020. Using 26 features, eight Machine learning models (non-deep learning models) were trained with data from the preclinical rescue protocol and compared to the "TropOut" score (a modified version of the "preHEART" score which consists of history, ECG, age and cardiac risk but without troponin analysis) to predict major adverse cardiac event (MACE) and acute coronary artery occlusion (ACAO). In our study population MACE occurred in 823 (29.8%) patients and ACAO occurred in 480 patients (17.4%). Interestingly, we found that all machine learning models outperformed the "TropOut" score. The VC and the LR models showed the highest area under the receiver operating characteristic (AUROC) for predicting MACE (AUROC = 0.78) and the VC showed the highest AUROC for predicting ACAO (AUROC = 0.81). A SHapley Additive exPlanations (SHAP) analyses based on the XGB model showed that presence of ST-elevations in the electrocardiogram (ECG) were the most important features to predict both endpoints.

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.

Coronary angiography after out-of-hospital cardiac arrest without ST-segment elevation: a systematic review and meta-analysis of randomised trials

BACKGROUND

Out-of-hospital cardiac arrest (OHCA) has a poor prognosis. The optimal timing and role of early coronary angiography (CAG) in OHCA patients without ST-segment elevation remains unclear. The goal of this study is to compare an early CAG versus delayed CAG strategy in OHCA patients without ST elevation.

METHODS

We systematically searched PubMed, Embase and Cochrane databases, in June 2022, for randomised controlled trials (RCTs) comparing early versus delayed early CAG. A random effects meta-analysis was performed.

RESULTS

A total of seven RCTs were included, providing a total of 1625 patients: 816 in an early strategy and 807 in a delayed strategy. In terms of outcomes assessed, our meta-analysis revealed a similar rate of all-cause mortality (pooled odds ratio [OR] 1.22 [0.99-1.50], P  = 0.06, I 2  = 0%), neurological status (pooled OR 0.94 [0.74-1.21], = 0.65, I 2  = 0%), need of renal replacement therapy (pooled OR 1.11 [0.78-1.74], P  = 0.47, I 2  = 0%) and major bleeding events (pooled OR 1.51 [0.95-2.40], P  = 0.08, I 2  = 69%).

CONCLUSION

According to our meta-analysis, in patients who experienced OHCA without ST elevation, early CAG is not associated with reduced mortality or an improved neurological status.

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.

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.

Machine Learning for the ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction at First Medical Contact

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting ECG are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but we currently have no accurate tools to identify them during initial triage. Herein, we report 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, significantly boosting both precision and sensitivity. Our derived OMI risk score provided superior rule-in and rule-out accuracy compared to routine care, and when combined with the clinical judgment of trained emergency personnel, this score 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.

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.

[STEMI mimics : ST elevations on ECG: alternative diagnoses to acute coronary occlusion]

BACKGROUND

The electrocardiogram (ECG) is an integral part of basic emergency medical diagnosis and preoperative evaluation. In cases of ST elevation myocardial infarction (STEMI) immediate treatment is mandatory after correlation of ischemic symptoms with the ECG pattern. However, there are also ECG patterns that can imitate STEMI, possibly resulting in the true underlying diagnosis being missed and inappropriate therapy being initiated.

OBJECTIVES

This paper provides an overview of the most important diagnoses that can imitate STEMI on ECG.

MATERIAL AND METHODS

A literature search was carried out to determine the most important differential diagnoses of ST elevation on ECG. These STEMI mimics are discussed in detail and their relevance for emergency medicine is explained.

RESULTS

This article provides an overview of differential diagnoses that should be known in emergency medicine when assessing an ECG with ST elevations.

CONCLUSION

Good knowledge of the ECG patterns presented here can support decision-making in emergency medicine.

Incorporation of Serial 12-Lead Electrocardiogram With Machine Learning to Augment the Out-of-Hospital Diagnosis of Non-ST Elevation Acute Coronary Syndrome

STUDY OBJECTIVE

Ischemic electrocardiogram (ECG) changes are subtle and transient in patients with suspected non-ST-segment elevation (NSTE)-acute coronary syndrome. However, the out-of-hospital ECG is not routinely used during subsequent evaluation at the emergency department. Therefore, we sought to compare the diagnostic performance of out-of-hospital and ED ECG and evaluate the incremental gain of artificial intelligence-augmented ECG analysis.

METHODS

This prospective observational cohort study recruited patients with out-of-hospital chest pain. We retrieved out-of-hospital-ECG obtained by paramedics in the field and the first ED ECG obtained by nurses during inhospital evaluation. Two independent and blinded reviewers interpreted ECG dyads in mixed order per practice recommendations. Using 179 morphological ECG features, we trained, cross-validated, and tested a random forest classifier to augment non ST-elevation acute coronary syndrome (NSTE-ACS) diagnosis.

RESULTS

Our sample included 2,122 patients (age 59 [16]; 53% women; 44% Black, 13.5% confirmed acute coronary syndrome). The rate of diagnostic ST elevation and ST depression were 5.9% and 16.2% on out-of-hospital-ECG and 6.1% and 12.4% on ED ECG, with ∼40% of changes seen on out-of-hospital-ECG persisting and ∼60% resolving. Using expert interpretation of out-of-hospital-ECG alone gave poor baseline performance with area under the receiver operating characteristic (AUC), sensitivity, and negative predictive values of 0.69, 0.50, and 0.92. Using expert interpretation of serial ECG changes enhanced this performance (AUC 0.80, sensitivity 0.61, and specificity 0.93). Interestingly, augmenting the out-of-hospital-ECG alone with artificial intelligence algorithms boosted its performance (AUC 0.83, sensitivity 0.75, and specificity 0.95), yielding a net reclassification improvement of 29.5% against expert ECG interpretation.

CONCLUSION

In this study, 60% of diagnostic ST changes resolved prior to hospital arrival, making the ED ECG suboptimal for the inhospital evaluation of NSTE-ACS. Using serial ECG changes or incorporating artificial intelligence-augmented analyses would allow correctly reclassifying one in 4 patients with suspected NSTE-ACS.

Beyond the ST-segment in Occlusion Myocardial Infarction (OMI): Diagnosing the OMI-nous

The ST-segment elevation (STE) myocardial infarction (MI)/non-STEMI (NSTEMI) paradigm has been the central dogma of emergency cardiology for the last 30 years. Although it was a major breakthrough when it was first introduced, it is now one of the most important obstacles to the further progression of modern MI care. In this article, we trace why a disease with an established underlying pathology (acute coronary occlusion [ACO]) was unintentionally labeled with a surrogate electrocardiographic sign (STEMI/NSTEMI) instead of pathologic substrate itself (ACO-MI/non-ACO-MI or occlusion MI [OMI]/non-OMI [NOMI] for short), how this fundamental mistake caused important clinical consequences, and why we should change this paradigm with a better one, namely OMI/NOMI paradigm.

Comparing Door-To-Balloon Time between ST-Elevation Myocardial Infarction Electrocardiogram and Its Equivalents

Background: In patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary interventions (pPCI), longer door-to-balloon (DTB) time is known to be associated with an unfavorable outcome. A percentage of patients with acute coronary occlusion present with atypical electrocardiographic (ECG) findings, known as STEMI-equivalents. We investigated whether DTB time for STEMI-equivalent patients was delayed. Methods: This is a retrospective study including patients arriving at an emergency department with the acute coronary syndrome in whom emergent pPCI was performed. ECGs were classified into STEMI and STEMI-equivalent groups. We compared DTB time, with its components, between the groups. We also investigated whether STEMI-equivalent ECG was an independent predictor of DTB time delayed for more than 90 min. Results: A total of 180 patients were included in the present study, and 23 patients (12.8%) presented with STEMI-equivalent ECGs. DTB time was significantly delayed in patients with STEMI-equivalent ECGs (89 (80–122) vs. 81 (70–88) min, p = 0.001). Multivariable logistic regression analysis showed that STEMI-equivalent ECG was an independent predictor of delayed DTB time (odds ratio: 4.692; 95% confidence interval: 1.632–13.490, p = 0.004). Conclusions: DTB time was significantly delayed in patients presenting with STEMI-equivalent ECGs. Prompt recognition of STEMI-equivalent ECGs by emergency physicians and interventional cardiologists might reduce DTB time and lead to a better clinical outcome.

Cell-Based and Selected Cell-Free Therapies for Myocardial Infarction: How Do They Compare to the Current Treatment Options?

Because of cardiomyocyte death or dysfunction frequently caused by myocardial infarction (MI), heart failure is a leading cause of morbidity and mortality in modern society. Paradoxically, only limited and non-curative therapies for heart failure or MI are currently available. As a result, over the past two decades research has focused on developing cell-based approaches promoting the regeneration of infarcted tissue. Cell-based therapies for myocardial regeneration include powerful candidates, such as multipotent stem cells (mesenchymal stem cells (MSCs), bone-marrow-derived stem cells, endothelial progenitor cells, and hematopoietic stem cells) and induced pluripotent stem cells (iPSCs). These possess unique properties, such as potency to differentiate into desired cell types, proliferation capacity, and patient specificity. Preclinical and clinical studies have demonstrated modest improvement in the myocardial regeneration and reduced infarcted areas upon transplantation of pluripotent or multipotent stem cells. Another cell population that need to be considered as a potential source for cardiac regeneration are telocytes found in different organs, including the heart. Their therapeutic effect has been studied in various heart pathologies, such as MI, arrhythmias, or atrial amyloidosis. The most recent cell-free therapeutic tool relies on the cardioprotective effect of complex cargo carried by small membrane-bound vesicles—exosomes—released from stem cells via exocytosis. The MSC/iPSC-derived exosomes could be considered a novel exosome-based therapy for cardiovascular diseases thanks to their unique content. There are also other cell-free approaches, e.g., gene therapy, or acellular cardiac patches. Therefore, our review provides the most recent insights into the novel strategies for myocardial repair based on the regenerative potential of different cell types and cell-free approaches.

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.

Point-of-care ultrasound may expedite diagnosis and revascularization of occult occlusive myocardial infarction

BACKGROUND

Electrocardiographically occult occlusive myocardial infarction (OOMI), defined as coronary artery occlusion requiring revascularization without ST-segment elevation on electrocardiogram (ECG), is associated with delayed diagnosis resulting in higher morbidity. Left ventricular (LV) wall motion abnormalities (WMA) appreciated on echocardiography can expedite OOMI diagnosis. We sought to determine whether point-of-care ultrasound (PoCUS) demonstrating WMA expedites revascularization time when performed on emergency department patients being evaluated for OOMI.

METHODS

This was a single-site retrospective cohort study over a 38-month period. All admitted adult ED patients ≥35 years of age evaluated by the emergency physician with PoCUS for LV function, an ECG, and a standard troponin I biomarker assay were included. Patients with ST-segment elevation myocardial infarction (STEMI), prior LV dysfunction, fever ≥100.4 °F, or hypotension were excluded. A structured chart abstraction was performed for relevant demographic and clinical characteristics.

RESULTS

We screened 1561 ED patients who underwent cardiac PoCUS for eligibility: 874 met exclusion criteria, 453 were discharged, and 234 were included in the analysis. Twenty-three patients had coronary interventions, of which 14 had WMA. PoCUS was performed 36 min (IQR -9-68) before troponin resulted (n = 234) and 39 min (IQR -23-96) before the first troponin elevation (n = 85). Twenty of the 23 patients diagnosed with OOMI had elevated troponins prior to catheterization with time from PoCUS to first troponin elevation of 43 min (IQR 9-263). Of these patients, 11 had WMA identified on PoCUS, and the WMA was appreciated 47 min (IQR 26-255) prior to troponin elevation. The time from ED arrival to revascularization was 673 min (IQR 251-2158); 432 min (IQR 209-1300) among patients with WMA (n = 14) compared with 2158 min (IQR 552-3390) for those without WMA (n = 9).

CONCLUSION

Cardiac PoCUS may identify OOMI earlier than standard evaluation and may expedite definitive management.

Effect of SWOT Analysis Combined with the Medical and Nursing Integration Emergency Nursing Process on Emergency Treatment Efficiency and Prognosis of Patients with Acute Myocardial Infarction

Acute myocardial infarction (AMI) is a common clinical emergency. Effective emergency treatment at the early stage of onset can effectively reduce the mortality rate. Time is the key of emergency treatment, which is directly related to the treatment effect and the prognosis of patients, and clinical intensive nursing intervention for emergency treatment is of great significance in improving the efficiency of emergency treatment and prognosis. In this study, the effects of routine emergency care flow and SWOT analysis combined with medical and nursing integration on emergency treatment efficiency and prognosis of patients with acute myocardial infarction were compared. The results showed that the combined scheme could improve the rescue effect and success rate of patients with acute myocardial infarction, shorten the rescue time, and reduce the mortality and complication rate of myocardial infarction, which provided a new direction for clinical emergency treatment of acute myocardial infarction.

New-Onset Atrial Fibrillation in Acute Myocardial Infarction Is a Different Phenomenon than Other Pre-Existing Types of That Arrhythmia

(1) Background: Atrial fibrillation (AF) in acute myocardial infarction (AMI) could worsen the prognosis. Yet, there is no definitive answer to whether new-onset AF (NOAF) is a more aggravating diagnosis than other types of that arrhythmia. The purpose of our study was to compare in-hospital clinical course and outcomes of NOAF patients contrary to patients with other pre-existing types of AF. (2) Methods: AMI patients hospitalized in the high-volume cardiological center within 2017−2018 were included in the study. NOAF was noticed in 106 (11%) patients, 95 (10%) with an AF history and AF during AMI formed the AF group, 60 (6%) with an AF history but without AF during AMI constituted the Prior-AF group, and 693 (73%) patients were without an AF before and during AMI. Medical history, routinely monitored clinical parameters, and in-hospital outcomes were analyzed between the groups. (3) Results: NOAF patients, contrary to others, initially had the highest high-sensitivity troponin I (hsTnI), B-type natriuretic peptide (BNP), C-reactive protein (CRP), and glucose levels, and the lowest potassium concentration, with the worst profile of changes for that parameter within the first four days of hospitalization. NOAF patients had the highest rate of ST-elevated AMI (40%), the longest hospitalization (p < 0.001), and the highest in-hospital mortality (p < 0.001). Not NOAF, but other AF groups (AF and Prior-AF groups) were more burdened with the previous comorbidities. (4) Conclusions: NOAF could be a distinct phenomenon in AMI patients, identifying those with the worst clinical in-hospital course and outcomes as compared to other types of AF.

Demographics and Characteristics of Patients Admitted With Acute Coronary Syndrome to the Coronary Care Unit at King Abdulaziz University

Background

Over the previous decade, the incidence of cardiovascular diseases (CVDs) has risen in the Middle East and will increase mortality to 23 million individuals in Saudi Arabia by 2030, according to the Saudi Ministry of Health. CVDs, including acute coronary syndrome (ACS), are the most common cause of mortality globally. This study aimed to analyze the demographic and clinical characteristics of patients with ACS admitted to the coronary care unit (CCU) in a tertiary hospital in Jeddah, Saudi Arabia. To the best of our knowledge, a lack of research in this region has been undertaken.

Methods

This retrospective records review study was conducted in a tertiary center in Jeddah, Saudi Arabia. All patients admitted to our CCU in 2017 with a final diagnosis of ACS were retrospectively enrolled. Demographic details, coronary risk factors, investigation and procedures, management, and clinical outcomes are all part of the data.

Results

Of the 615 patients included in the study, 491 (79.84%) were males, 226 (36.75%) were 55-64 years old, and 161 (26.18%) were 45-54 years old. Males had a higher rate of ST-segment elevation myocardial infarction (STEMI) (214, 43.58%), while females had a higher rate of non-ST-segment elevation myocardial infarction (NSTEMI) and unstable angina (UA) (45.96% and 37.90%, respectively). Diabetes (62.60%), dyslipidemia (62.44%), and hypertension (61.46%) were the most prevalent risk factors. Angiography and percutaneous coronary intervention (PCI) were performed in 77.72% and 61.95% of patients, respectively. Coronary artery bypass graft was only performed in 4.39% of patients. PCI was performed more frequently in patients with STEMI than in those with NSTEMI/UA (P < 0.001). A large majority of patients (99.5%) recovered and were discharged. Of the 161 (26.18%) patients who attended a follow-up visit, only 45 (33.08%) met the therapeutic objective of 1.8 mmol/L (70 mg/dl) of low-density lipoprotein cholesterol. There were 100 (16.26%) patients readmitted to the CCU, and most of these were readmitted within a year after initial admission. Readmissions were more common in females and patients diagnosed with NSTEMI/UA during initial admission (15.47% and 19.35%, respectively).

Conclusion

This study revealed that our most common demographics were males between 45 and 64 years, which is a decade younger than the global average. STEMI was the most common presentation. The most common modifiable cardiovascular risk factors were hypertension, diabetes, and dyslipidemia. The most common adverse event was reinfarction, which was closely linked to hypertension and diabetes. In this study, the recovery rate was higher than in studies from other countries; however, the majority of patients did not achieve the goal of cholesterol levels at follow-up. Our population's younger age at presentation necessitates greater attention and more stringent preventive strategies, such as lifestyle changes and evidence-based treatments for CVD risk factors, to reduce the incidence and burden of ACS on CCUs.

Diagnostic Accuracy of the Deep Learning Model for the Detection of ST Elevation Myocardial Infarction on Electrocardiogram

We aimed to measure the diagnostic accuracy of the deep learning model (DLM) for ST-elevation myocardial infarction (STEMI) on a 12-lead electrocardiogram (ECG) according to culprit artery sorts. From January 2017 to December 2019, we recruited patients with STEMI who received more than one stent insertion for culprit artery occlusion. The DLM was trained with STEMI and normal sinus rhythm ECG for external validation. The primary outcome was the diagnostic accuracy of DLM for STEMI according to the three different culprit arteries. The outcomes were measured using the area under the receiver operating characteristic curve (AUROC), sensitivity (SEN), and specificity (SPE) using the Youden index. A total of 60,157 ECGs were obtained. These included 117 STEMI-ECGs and 60,040 normal sinus rhythm ECGs. When using DLM, the AUROC for overall STEMI was 0.998 (0.996–0.999) with SEN 97.4% (95.7–100) and SPE 99.2% (98.1–99.4). There were no significant differences in diagnostic accuracy within the three culprit arteries. The baseline wanders in false positive cases (83.7%, 345/412) significantly interfered with the accurate interpretation of ST elevation on an ECG. DLM showed high diagnostic accuracy for STEMI detection, regardless of the type of culprit artery. The baseline wanders of the ECGs could affect the misinterpretation of DLM.

Intelligent Algorithm-Based Coronary Angiography Characteristics of Acute Non-ST-Segment Elevation Myocardial Infarction Patients with Different Genders

OBJECTIVE

This study was aimed at comparing the characteristics of coronary angiography based on intelligent algorithm in patients with acute non-ST-segment elevation myocardial infarction (NSTEMI) of different genders.

METHODS

Eighty patients were selected to segment the coronary angiogram using the convolutional neural network (CNN) algorithm, the input layer of the CNN was used to receive the image dataset, and three-dimensional data were input during semantic segmentation to achieve automatic segmentation of the target features. Segmentation results were quantitatively assessed by accuracy (Acc), sensitivity (Se), specificity (Sp), and Dice coefficient (Dice). The characteristics of coronary angiography were compared between the two groups.

RESULTS

The CNN algorithm had good segmentation effect, complete vessel extraction, and little noise, and Acc, Se, Sp, and Dice were 90.32%, 93.39%, 91.25%, and 89.75%, respectively. The proportion of diabetes mellitus was higher in female patients with NSTEMI (68.8%) than that in male patients (46.3%); the proportion of the left main coronary artery (LM) and left anterior descending artery (LAD) was lower in the female group (7.5%, 41.3%) than that in the male group (13.8%, 81.3%), and the difference between the two groups was statistically significant (P < 0.05).

CONCLUSION

The CNN algorithm achieves accurate extraction of vessels from coronary angiographic images, and women with diabetes and hyperlipidemia are more likely to have NSTEMI than men, especially the elderly.

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.

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.

Diagnostic performance of a new ECG algorithm for reducing false positive cases in patients suspected acute coronary syndrome

BACKGROUND

Early and correct diagnosis of ST-segment elevation myocardial infarction (STEMI) is crucial for providing timely reperfusion therapy. Patients with ischemic symptoms presenting with ST-segment elevation on the electrocardiogram (ECG) are preferably transported directly to a catheterization laboratory (Cath-lab) for primary percutaneous coronary intervention (PPCI). However, the ECG often contains confounding factors making the STEMI diagnosis challenging leading to false positive Cath-lab activation. The objective of this study was to test the performance of a standard automated algorithm against an additional high specificity setting developed for reducing the false positive STEMI calls.

METHODS

We included consecutive patients with an available digital prehospital ECG triaged directly to Cath-lab for acute coronary angiography between 2009 and 2012. An adjudicated discharge diagnosis of STEMI or no myocardial infarction (no-MI) was assigned for each patient. The new automatic algorithm contains a feature to reduce false positive STEMI interpretation. The STEMI performance with the standard setting (STD) and the high specificity setting (HiSpec) was tested against the adjudicated discharge diagnosis in a retrospective manner.

RESULTS

In total, 2256 patients with an available digital prehospital ECG (mean age 63 ± 13 years, male gender 71%) were included in the analysis. The discharge diagnosis of STEMI was assigned in 1885 (84%) patients. The STD identified 165 true negative and 1457 true positive (206 false positive and 428 false negative) cases (77.3%, 44.5%, 87.6% and 17.3% for sensitivity, specificity, PPV and NPV, respectively). The HiSpec identified 191 true negative and 1316 true positive (180 false positive and 569 false negative) cases (69.8%, 51.5%, 88.0% and 25.1% for sensitivity, specificity, PPV and NPV, respectively). From STD to HiSpec, false positive cases were reduced by 26 (12,6%), but false negative results were increased by 33%.

CONCLUSIONS

Implementing an automated ECG algorithm with a high specificity setting was able to reduce the number of false positive STEMI cases. However, the predictive values for both positive and negative STEMI identification were moderate in this highly selected STEMI population. Finally, due the reduced sensitivity/increased false negatives, a negative AMI statement should not be solely based on the automated ECG statement.

Machine learning with electrocardiograms: A call for guidelines and best practices for 'stress testing' algorithms

This paper provides a brief description of how computer programs are used to automatically interpret electrocardiograms (ECGs), and also provides a discussion regarding new opportunities. The algorithms that are typically used today in hospitals are knowledge engineered where a computer programmer manually writes computer code and logical statements which are then used to deduce a possible diagnosis. The computer programmer's code represents the criteria and knowledge that is used by clinicians when reading ECGs. This is in contrast to supervised machine learning (ML) approaches which use large, labelled ECG datasets to induct their own 'rules' to automatically classify ECGs. Although there are many ML techniques, deep neural networks are being increasingly explored as ECG classification algorithms when trained on large ECG datasets. Whilst this paper presents some of the pros and cons of each of these approaches, perhaps there are opportunities to develop hybridised algorithms that combine both knowledge and data driven techniques. In this paper, it is pointed out that open ECG data can dramatically influence what international ECG ML researchers focus on and that, ideally, open datasets could align with real world clinical challenges. In addition, some of the pitfalls and opportunities for ML with ECGs are outlined. A potential opportunity for the ECG community is to provide guidelines to researchers to help guide ECG ML practices. For example, whilst general ML guidelines exist, there is perhaps a need to recommend approaches for 'stress testing' and evaluating ML algorithms for ECG analysis, e.g. testing the algorithm with noisy ECGs and ECGs acquired using common lead and electrode misplacements. This paper provides a primer on ECG ML and discusses some of the key challenges and opportunities.

Brugada Pattern Manifesting During Hyperkalemia, Diabetic Ketoacidosis, and Acute Alcohol Intoxication

BACKGROUND Brugada syndrome is a rare ion channelopathy that can lead to sudden cardiac death and lethal arrhythmias in patients without a structural cardiac defect, the most common of which being the gain-of-function mutation of the SCN5a sodium ion channel involving phase 0 of the cardiac action potential. In 2012, BrS electrocardiogram findings were redefined and classified as either congenital Brugada syndrome (BrS) or Brugada phenocopies (BrP). Several etiologies of BrP have been reported, such as metabolic derangements, electrolyte abnormalities, cardiovascular diseases, and pulmonary embolism. CASE REPORT A 28-year-old man presented to the Emergency Department unresponsive. An initial ECG taken by Emergency Medical Services (EMS) was interpreted as a STEMI. An initial ECG in the ED showed a Brugada type I ECG pattern in leads V1-V2 and hyperacute T wave abnormalities, among other findings. Additionally, the patient had a serum potassium level of 9 mmol/L, glucose level of 1375 mmol/L, and peak cardiac troponin-I of 20.452 μg/L. All underlying medical conditions were stabilized, electrolyte and metabolic abnormalities were corrected, and subsequent normalization of electrocardiographic findings was achieved. CONCLUSIONS Distinguishing congenital Brugada syndrome from Brugada phenocopies can be difficult, especially when patients present to the ED with severe underlying conditions. Several factors can be used to direct clinical suspicion towards one or the other; however, confirmation may require EP studies and further tests. In this case, the following findings were suggestive of BrP: presence of an identifiable underlying abnormality, correction of the underlying condition resolves the ECG pattern, and the absence of family history of sudden cardiac death.

Sharing and Teaching Electrocardiograms to Minimize Infarction (STEMI): reducing diagnostic time for acute coronary occlusion in the emergency department

BACKGROUND

Limits to ST-Elevation Myocardial Infarction (STEMI) criteria may lead to prolonged diagnostic time for acute coronary occlusion. We aimed to reduce ECG-to-Activation (ETA) time through audit and feedback on STEMI-equivalents and subtle occlusions, without increasing Code STEMIs without culprit lesions.

METHODS

This multi-centre, quality improvement initiative reviewed all Code STEMI patients from the emergency department (ED) over a one-year baseline and one-year intervention period. We measured ETA time, from the first ED ECG to the time a Code STEMI was activated. Our intervention strategy involved a grand rounds presentation and an internal website presenting weekly local challenging cases, along with literature on STEMI-equivalents and subtle occlusions. Our outcome measure was ETA time for culprit lesions, our process measure was website views/visits, and our balancing measure was the percentage of Code STEMIs without culprit lesions.

RESULTS

There were 51 culprit lesions in the baseline period, and 64 in the intervention period. Median ETA declined from 28.0 min (95% confidence interval [CI] 15.0-45.0) to 8.0 min (95%CI 6.0-15.0). The website garnered 70.4 views/week and 27.7 visitors/week in a group of 80 physicians. There was no change in percentage of Code STEMIs without culprit lesions: 28.2% (95%CI 17.8-38.6) to 20.0% (95%CI 11.2-28.8%). Conclusions Our novel weekly web-based feedback to all emergency physicians was associated with a reduction in ETA time by 20 min, without increasing Code STEMIs without culprit lesions. Local ECG audit and feedback, guided by ETA as a quality metric for acute coronary occlusion, could be replicated in other settings to improve care.

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.