Background: Cath lab activation based on ST-elevation myocardial infarction (STEMI) criteria is founded on aging data and requires evolution. In the “Occlusive Myocardial Infarction (OMI) Manifesto,” emergency physicians Dr. Steve Smith, Dr. Pendell Meyers, and Dr. Scott Weingart introduced a new paradigm —OMI vs. non-occlusive myocardial infarction (NOMI). The OMI/NOMI paradigm focuses on the presence of coronary occlusion, while STEMI/NSTEMI categorizes myocardial infarctions based on electrocardiogram (ECG) findings. Patients with OMI exhibit higher mortality and worse left ventricular function compared to those with NOMI.1, 2, 3 Detecting OMI is more difficult and necessitates scrutiny of the ECG, which is challenging in a busy emergency department where ED clinicians are interrupted more than ten times per hour.4, 5 Some OMI ECG signs include ST elevation in only one lead, subtle ST elevation with minimal reciprocal changes, isolated ST depressions, and hyperacute T waves. To meet this challenge, Dr. Steve Smith, Dr. Pendell Meyers (Dr. Smith’s ECG Blog), and their team developed The Queen of Hearts, a machine-learning AI model that has the potential to aid in the early detection of subtle OMI ECG changes. Accurately identifying OMI changes in ECG that STEMI criteria might otherwise miss would allow for more timely intervention, potentially salvaging more myocardium. An AI model that is highly sensitive in detecting OMI while maintaining a high degree of specificity would be an ideal tool to support emergency physicians’ clinical decision-making. The performance of this tool is unknown. Click here for Direct Download of the Podcast. Paper: Herman R, Meyers HP, Smith SW, et al. International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction. Eur Heart J Digit Health. 2023;5(2):123-133. Published 2023 Nov 28. PMID: 38505483 Clinical question: “Can an AI model detect an OMI lesion using a single 12-lead ECG?” What They Did: Investigators performed a retrospective derivation study followed by validation on an internal data set from the same Acute Coronary Syndrome (ACS) database. Cases eligible for inclusion were randomly assigned to a model development training set (derivation set) and testing set (validation set). The training set included ECG feature extraction and classification Feature extraction used 60,000 parameters The classification component combined all extracted features and used an additional 150,000 parameters. The validation data set was used for hyperparameter tuning and threshold selection. Investigators then tested the AI model on two data sets An internal European data set (internal validation set) A separate US data set (external validation set) from the DOMI ARIGATO database. They compared the AI model with the existing criteria for detecting OMI on 12-lead ECGs and analyzed the AI model in various subgroups. Population: Derivation Set: Random selection of ACS patients from the Cardiovascular Centre Aalst in Belgium and ACS patients from an international image database patient. EU Internal Test Set: Random Selection of ACS patients from the Cardiovascular Centre Aalst in Belgium and ACS patients from an international image database patient. US External Test Set: Patients from the DOMI ARIGATO database. Exclusion: ECGs >24 h before CAG and post-CAG ECGs with poor signal quality ECGs with missing Expert Annotation, undigitizable ECGs, Baseline ECGs (additionally excluded from the US External Database) Intervention: AI-powered ECG model implemented on ECGs from the internal EU and external US datasets. Comparator: Blinded physician annotations of the standard ‘STEMI criteria’ on ECG Blinded subjective ECG expert annotations of OMI Angiographic clinical outcome data Outcomes: Primary Outcome: AI model’s ability to identify patients with angiographically confirmed OMI using only the 12-lead ECG. Secondary Outcomes: OMI AI model performance across demographic and ECG subgroups A comparison of the AI model performance against the existing STEMI criteria for detecting acute coronary occlusion from 12-lead ECGs A sensitivity analysis of AI model performance using various angiographic and laboratory cut-offs of OMI An evaluation of misclassified cases Results: The derivation set used in the AI model development included 18,616 ECGs from 10,543 patients with clinically validated outcomes. The overall test set included 3254 ECGs from 2222 patients The internal EU testing cohort 2016 ECGs from 1630 patients The US testing cohort 1238 ECGs from 633 patients The prevalence of OMI differed between the internal EU and the external US test sets, 16% compared with 36.2%, respectively ( 0.001). The patients in the US test set were younger, had more ECGs recorded before catheterization, and were more likely to present with a STEMI-positive ECG. AI Model Performance: Achieved an Area Under the ROC Curve (AUC) of 0.938 [95% CI: 0.924–0.951]. Accuracy: 90.9% [95% CI: 89.7–92.0]. Sensitivity: 80.6% [95% CI: 76.8–84.0]. Specificity: 93.7% [95% CI: 92.6–94.8]. STEMI Criteria Performance: STEMI criteria accuracy: 83.6% [95% CI: 82.1–85.1]. Sensitivity: 32.5% [95% CI: 28.4–36.6]. Specificity: 97.7% [95% CI: 97.0–98.3]. ECG Experts Performance: Accuracy of ECG experts was 90.8% [95% CI: 89.5–91.9]. Sensitivity: 73.0% [95% CI: 68.7–77.0]. Specificity: 95.7% [95% CI: 94.7–96.6]. OMI AI Model vs. STEMI Criteria: The OMI AI model performs significantly better than the STEMI criteria in sensitivity, Negative Predictive Value (NPV), Matthews correlation coefficient (MCC), and AUC. However, it has lower specificity and Positive Predictive Value (PPV) compared to the STEMI criteria. OMI AI Model vs. ECG Experts: The OMI AI model has higher sensitivity and NPV than ECG experts. It shows equal performance in AUC and is adjudicated as equal overall to ECG experts. Specificity and PPV are lower than ECG experts, and MCC is neutral. ECG Experts vs. STEMI Criteria: ECG experts have higher sensitivity, NPV, MCC, and AUC than STEMI criteria. They perform the same in specificity and PPV compared to STEMI criteria, leading to significantly better adjudication. Strengths: Rigorous Methodological Approach: The study follows a comprehensive methodological approach, encompassing stages of development, validation, and comparison. Large and Diverse Dataset: The model was trained and tested on a substantial dataset of 18,616 ECGs from 10,543 patients with ACS across multiple international cohorts. This diversity enhances the model’s generalizability and robustness. Expert Interpretation and Validation: All cases in the derivation set included expert ECG interpretations alongside clinically validated angiographic outcome data, ensuring high accuracy in the model’s training process. High Agreement Among Experts: Two authors, serving as ECG experts, annotated all tracings for the presence of OMI while being blinded to all clinical data. They achieved a 94% agreement (kappa = 0.849), demonstrating the reliability of the expert annotations. Independent Review: Blinded independent clinical reviewers adjudicated all angiographic data in the EU internal testing set, adding an extra layer of objectivity and reliability to the validation process. Comprehensive Performance Comparison: The study compares the AI model’s performance with existing STEMI criteria and expert ECG interpretations. This sets a quantifiable humanistic standard, highlighting the AI model’s enhanced performance. Limitations: Applicability Limited to ACS Patients: The AI model was developed using patients and ECGs exclusively from ACS databases, restricting its applicability to only those with ACS. Disease-Oriented Outcomes: The outcomes in this study are disease-oriented. While facilitating the diagnosis of OMI may lead to improved patient-oriented outcomes, this was not directly studied. Limited Generalizability to Asymptomatic Patients: The study is not generalizable to a broader population of asymptomatic patients and was not designed to quantify other relevant clinical endpoints such as mortality, in-hospital complications, or major adverse cardiovascular events (MACE). Lack of Prospective Validation: The validation set was analyzed retrospectively, lacking prospective validation to confirm the model’s effectiveness in real-world clinical settings. Randomization Process Not Mentioned: The randomization process used to allocate cases to the derivation or validation set is not mentioned, which may impact the robustness of the findings. Comparison Limited to 12-Lead ECG: The AI model was compared to the 12-lead ECG alone. Some patients undergo emergency angiography without clear STEMI criteria based on the full clinical picture. Therefore, the interpretation of the overall gain is limited without a pragmatic comparison to real-world clinical practices and patient-oriented outcomes. Limited Generalizability to Younger Population and Women: Approximately 10% of ECGs were from patients ≤45 years of age, and three-quarters of the cases were from males, limiting the generalizability to younger populations and women. Inappropriate Use of P-Values: The inclusion of p-values in Tables 1 and 2 is puzzling, as this is not a randomized controlled trial (RCT). Demographic differences between validation sets are expected and desired for external validity. Variability in Care Standards: Significant differences in clinical presentation and management between patients in Europe and the USA (e.g., the USA had younger patients and more ECGs before catheterization) could affect the model’s performance across different healthcare systems. Subjective Outcome Verification: The detection of OMI relied on visual verification of TIMI flow on angiograms, which may be subjective. Conflict o