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A case of prolapsing left atrial myxoma with Doppler documented aortic and tricuspid incompetence is reported. The valves were structurally normal. Constant trauma to the central fibrous body by the prolapsing myxoma could be responsible for incompetence of tricuspid and aortic valves.

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Introduction: Current standard electrocardiogram analysis algorithms cannot predict the presence and extent of coronary artery disease (CAD), especially in stable patients. Hypothesis: A novel artificial intelligence algorithm (ECGio) is able to predict the presence, location, and severity of coronary artery lesions in a minimally selected stable patient population as verified by coronary angiography. Methods: A cohort of 1659 stable outpatients were randomly divided into training (86%) and validation (14%) subsets, maintaining population characteristics. ECGio was trained and then validated using electrocardiograms paired with angiograms collected from electronic medical records. Coronary artery lesions were evaluated by a continuous measure of stenosis ranging from 0% - 100%. The prediction was then compared to the angiogram result (worst diameter stenosis in each vessel) with the error calculated per patient and per vessel. Results: In the primary analysis of the validation cohort, 22 had no angiographic CAD and were grouped with 56 patients with mild CAD (DS ≤30%), 31 had moderate CAD (DS 30-70%), and 113 had severe CAD (DS ≥70%). On a vessel level ECGio was able to predict stenosis severity with an overall average error of 16% as well as vessel specific errors of 18% in the LAD, 19% in the LCX, 18% in the RCA, and 8% in the Left Main. Absolute Error and Left Main error can be seen in the Figure. Conclusion: This validation study strongly suggests that it is possible to utilize an artificial intelligence algorithm to determine the presence and severity of CAD in stable patients using data from a standard 12- lead electrocardiogram.