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.

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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?”

Exclusion:

AI-powered ECG model implemented on ECGs from the internal EU and external US datasets.

Primary Outcome: AI model’s ability to identify patients with angiographically confirmed OMI using only the 12-lead ECG.

Secondary Outcomes:

AI Model Performance:

STEMI Criteria Performance:

ECG Experts Performance:

OMI AI Model vs. STEMI Criteria:

OMI AI Model vs. ECG Experts:

ECG Experts vs. STEMI Criteria:

Inside the Numbers: The data for this AI model is impressive, showcasing a remarkable capability in early and accurate detection of OMI on ECGs, demonstrating a sensitivity of 80.6% (76.8–84.0) and specificity of 93.7% (92.6–94.8). The AI model not only surpassed the standard STEMI ECG criteria [sensitivity 32.5% (28.4–36.6) and specificity 97.7% (97.0–98.3)] but also matched the performance of Dr. Steve Smith and Dr. Pendell Meyers, two well known ECG experts [sensitivity 73.0% (68.7–77.0) and specificity 95.7% (94.7–96.6)]. Additionally, when considering the existing evidence, the AI model would likely outperform ED physicians’ and cardiologists’ ability to detect ischemia on ECG, who achieved sensitivities of approximately 65% and specificities ranging from 65–79% in multiple studies.7, 8, 9 This high accuracy demonstrates AI’s potential to improve diagnostic processes and patient outcomes in emergency settings.

The AI model’s PPV in this study was 0.780 (0.742–0.816), and the NPV was 0.946 (0.935–0.957) for the primary outcome. PPV and NPV are heavily influenced by disease prevalence, and a high prevalence increases the PPV, indicating that a positive test result is more likely to be a true positive. The 16% and 36.2% prevalence of OMI in the internal and external validation sets are likely much higher than expected from a random group of patients assessed for ACS in the ED on any given day. Consequently, the PPV and NPV are likely lower in a less risky population with a lower prevalence for ACS.  

The AI model’s AUC for detecting OMI was 0.938 (0.924–0.951), with an optimal threshold of 0.1106. The optimal threshold refers to the chosen point that maximizes the AI model’s accuracy. The point is a probability that ranges from 0–1. However, investigators must choose the value (optimal threshold) at which the model determines whether the ECG is positive or negative. Therefore, the optimal threshold converts a continuous variable (probability) into a binary decision, such as distinguishing between the presence or absence of OMI on ECG. If the threshold is set too low, it might result in high sensitivity but low specificity, leading to many false positives. The ROC curve is a graphical plot that illustrates the diagnostic ability of a binary classifier as its discrimination threshold is varied. In this instance, a ROC curve with an AUC of 0.938 is outstanding and highlights the potential of the AI model to optimize clinical decision-making processes.

Critical Biases and Considerations: The primary flaw in this paper is selection bias. All patients included in the derivation and validation sets were selected from ACS databases. As mentioned, the prevalence of OMI in the internal and external validation sets is very high. Physicians should exercise caution when applying this data more broadly (i.e., all patients with an ECG in the ED). 

The AI model detected OMI in 979 cases total, 267 of which also met the STEMI criteria on ECG. Therefore, 27% of the OMIs detected by the AI model might have been more obvious and less noteworthy to an emergency physician aiming to improve their diagnostic capabilities. However, the remaining 73% of AI-detected OMIs are particularly interesting because they require meticulous ECG scrutiny for accurate diagnosis. While not all these AI-detected OMI cases met the primary outcome criteria, technology can fill a void in identifying patients who may benefit from emergent intervention despite the lack of STEMI-specific criteria on ECG.

“Time is myocardium,” and the primary goal in ACS treatment is to detect OMI on ECG as early as possible to prevent myocardial necrosis. Utilization of STEMI criteria missed 330 OMI patients —false negatives. Among these, 133 had a median revascularization time of 9.3 hours but were correctly identified by the AI model on the first ECG. Early detection can potentially improve patient outcomes, especially in cases with real-world median angiography time of 9 hours. While this data is compelling, it highlights the need for prospective evaluation of the AI model compared to the performance of the average emergency physician to fully assess its clinical effectiveness.

The Future and Transformative Potential of AI: This AI model’s development and validation process mirrors that of a clinical decision instrument, beginning with retrospective derivation followed by internal and external validation. Before widespread implementation, prospective validation in various clinical settings with diverse populations is necessary. Additionally, utilization studies should confirm that the AI model achieves its intended goals, such as earlier detection of OMI and improved patient-oriented outcomes.

While the idea of AI taking over the world might be an exaggeration, its transformative impact cannot be overstated. The continuous advancement and integration of AI technologies can lead to more efficient, accurate, and personalized solutions. Moreover, AI’s continuous refinement through machine learning suggests its performance will only improve over time. As the AI model is exposed to more data and varied cases, it can refine its algorithms, enhance its accuracy, and adapt to new patterns, making it an invaluable tool in the medical field. And, unlike human counterparts, AI will not fatigue and will maintain high accuracy levels, even after the 12th hour on duty and dozens of ECG interpretations. The possibilities for AI applications in healthcare are virtually limitless. 

Author’s conclusion: “AI model outperformed gold-standard STEMI criteria in the diagnosis of OMI, but further prospective clinical studies are needed to define the role of the OMI AI model in guiding ACS triage and the timely referral of patients benefiting from immediate revascularization.”

The Queen of Hearts AI model demonstrates impressive accuracy, surpassing STEMI criteria and matching expert interpretation for detecting OMI on ECG. However, the high prevalence of OMI in the study’s datasets may overestimate AI’s ability to detect OMI in a general ED population with a lower disease prevalence. Ultimately, the model requires prospective validation in diverse clinical settings before widespread adoption— but this could be a winning hand.

Marco Propersi, DO FAAEM

Joseph Bove, DO FAAEM

Post Peer Reviewed By: Anand Swaminathan, MD (Twitter/X: @EMSwami)

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