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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.
RESUMEN
BACKGROUND: Although high left ventricular filling pressures [left ventricular (LV) end-diastolic pressure or pulmonary capillary wedge pressure (PCWP)] are widely taken as surrogates for LV diastolic dysfunction, the actual distending pressure that governs LV diastolic stretch is transmural pressure difference (∆PTM). Clinically, preferring ∆PTM over PCWP may improve diagnostic and therapeutic decision-making. We aimed to compare the clinical implications of diastolic function characterization based on PCWP or ∆PTM. METHODS: We retrospectively screened our hospital database for adult patients with a clinical diagnosis of heart failure who underwent right heart catheterization. Echocardiographic diastolic dysfunction was graded according to the current guidelines. LV end-diastolic properties were assessed with construction of complete end-diastolic pressure-volume relationship (EDPVR) curves using the single-beat method. Survival status was checked via the electronic national health-care system. RESULTS: A total of 693 cases were identified in our database; the final study population comprised 621 cases. ∆PTM-based, but not PCWP-based, EDPVR diastolic stiffness constants were significantly predictive of advanced diastolic dysfunction. PCWP-based diastolic stiffness constants were not able to predict 5-year mortality, whereas ∆PTM-based EDPVR stiffness constants and volumes all turned out to have significant predictive power for 5-year mortality. CONCLUSION: Left ventricular diastolic function assessment can be improved using ∆PTM instead of PCWP. As ∆PTM ultimately linked to right-sided functions, this approach emphasizes the limitations of taking LV diastolic function as an isolated phenomenon and underlines the need for a complete hemodynamic assessment involving the right heart in therapeutic and prognostic decision-making processes.