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INTRODUCTION: Non-gated, non-contrast computed tomography (CT) scans are commonly ordered for a variety of non-cardiac indications, but do not routinely comment on the presence of coronary artery calcium (CAC)/atherosclerotic cardiovascular disease (ASCVD) which is known to correlate with increased cardiovascular risk. Artificial intelligence (AI) algorithms can help detect and quantify CAC/ASCVD which can lead to early treatment and improved outcomes. METHODS: Using an FDA-approved algorithm (NANOX AI) to measure coronary artery calcium (CAC) on non-gated, non-contrast CT chest, 536 serial scans were evaluated in this single-center retrospective study. Scans were categorized by Agatston scores as normal-mild (<100), moderate (100-399), or severe (≥400). AI results were validated by cardiologist's overread. Patient charts were retrospectively analyzed for clinical characteristics. RESULTS: Of the 527 patients included in this analysis, a total of 258 (48.96%) had moderate-severe disease; of these, 164 patients (63.57%, p< 0.001) had no previous diagnosis of CAD. Of those with moderate-severe disease 135 of 258 (52.33% p=0.006) were not on aspirin and 96 (37.21% p=0.093) were not on statin therapy. Cardiologist interpretation demonstrated 88.76% agreement with AI classification. DISCUSSION/CONCLUSION: Machine learning utilized in CT scans obtained for non-cardiac indications can detect and semi-quantitate CAC accurately. Artificial intelligence algorithms can accurately be applied to non-gated, non-contrast CT scans to identify CAC/ASCVD allowing for early medical intervention and improved clinical outcomes.
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Background: Most pericardial effusions that occur in the setting of ST-segment elevation myocardial infarction (STEMI) are small, simple, and without symptomology. However, in its most severe form, pericardial effusion can precipitate cardiac tamponade, and when untreated, can cause abrupt hemodynamic instability. Pericardial effusion may be a manifestation of left ventricular free-wall rupture, hemorrhagic pericarditis, or aortic dissection involving a coronary artery. Case Report: We describe the case of a 65-year-old male who experienced chest pain for several days prior to admission but delayed seeking care because he wished to avoid coronavirus disease 2019 exposure. Upon arrival, he was hemodynamically unstable. Electrocardiogram was consistent with anterior STEMI. Bedside echocardiogram demonstrated a hypertrophic left ventricle with preserved function and a large, complex pericardial effusion with cardiac tamponade physiology. Computed tomography of the chest identified hemopericardium but was unable to delineate etiology. The patient underwent emergent thoracotomy because of persistent shock, and during the surgery, left ventricular free-wall rupture was identified and repaired. Coronary artery bypass grafting to the patient's left anterior descending artery was also performed. The patient remained asymptomatic at 2-year follow-up. Conclusion: The differential for hemodynamic compromise in a patient with STEMI is broad, but quickly distinguishing pump failure from other life-threatening causes of shock is imperative to dictate time-sensitive management decisions. The presence of a hemorrhagic pericardial effusion in the setting of STEMI is a surrogate marker for a severe infarct and can help the bedside physician determine whether a patient will be better served in the catheterization lab for revascularization or in the operating room for surgical repair.
