ABSTRACT
A 62-year-old man presents to the Cardiology Department with a history of angina on exertion. Invasive coronary angiography revealed a severe three vessels coronary artery disease. The "Hybrid Heart Team" successfully performed a fully robotically assisted hybrid revascularization combining robotically enhanced-minimally invasive direct coronary artery bypass on the left anterior descending (LAD) and robotically assisted percutaneous coronary intervention on non-LAD lesions.
Subject(s)
Coronary Artery Disease , Percutaneous Coronary Intervention , Robotic Surgical Procedures , Coronary Artery Bypass , Coronary Artery Disease/surgery , Coronary Vessels/diagnostic imaging , Coronary Vessels/surgery , Humans , Male , Middle Aged , Minimally Invasive Surgical Procedures , Treatment OutcomeABSTRACT
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.
ABSTRACT
We report a case of a clinically asymptomatic patient with extreme P-wave separation on the electrocardiogram mimicking atrial tachycardia with atrioventricular block. The accurate examination of the patient, analysis of the electrocardiogram, and echocardiographic findings led to proper diagnosis, management, and treatment. (Level of Difficulty: Intermediate.).