Search

A woman in her 50s with a background of chronic obstructive pulmonary disease secondary to smoking presented with intermittent chest tightness, dyspnoea and vomiting for 4 days. A presumed diagnosis of acute coronary syndrome (ACS) was made based on dynamic ischaemic ECG changes and elevation in high-sensitivity cardiac troponin T levels. She underwent emergent coronary angiography which demonstrated mild coronary artery disease with left ventriculography suggestive of mid-wall variant Takotsubo cardiomyopathy. Thyroid function tests performed to investigate sinus tachycardia were consistent with hyperthyroidism, and her thyroid-stimulating hormone receptor antibody was elevated. A diagnosis of thyroid storm was made in the setting of a newly diagnosed Graves' disease and the patient was subsequently commenced on guideline-based therapy. This case demonstrates that Takotsubo cardiomyopathy, a mimic of ACS, is a possible complication of thyroid storm and therefore hyperthyroidism should be considered in the list of differentials in patients presenting with Takotsubo cardiomyopathy.

AIMS: Hyperaemic and non-hyperaemic pressure ratios (NHPR) are routinely used to identify significant coronary lesions. Machine learning (ML) techniques may help better understand these indices and guide future practice. This study assessed the ability of a purpose-built ML algorithm to classify coronary ischaemia during non-hyperaemia compared with the existing gold-standard technique (fractional flow reserve, FFR). Further, it investigated whether ML could identify components of coronary and aortic pressure cycles indicative of ischaemia. METHODS AND RESULTS: Seventy-seven coronary vessel lesions (39 FFR defined ischaemia, 53 patients) with proximal and distal non-hyperaemic pressure waveforms and FFR values were assessed using supervised and unsupervised learning techniques in combination with principal component analysis (PCA). Fractional flow reserve measurements were obtained from the right coronary artery (13), left anterior descending (46), left circumflex (11), left main (1), obtuse marginal (2), and diagonal (4). The most accurate supervised learning classification utilized whole-cycle aortic with diastolic distal blood pressure waveforms, yielding a classification accuracy of 86.9% (sensitivity 86.8%, specificity 87.2%, positive predictive value 86.8%, negative predictive value 87.2%). Principal component analysis showed subtle variations in coronary pressures at the start of diastole have significant relation to ischaemia, and whole-cycle aortic pressure data are important for determining ischaemia. CONCLUSIONS: Our ML algorithm classifies significant coronary lesions with accuracy similar to previous studies comparing time-domain NHPRs with FFR. Further, it has identified characteristics of pressure waveforms that relate to function. These results provide an application of ML to ischaemia requiring only standard data from non-hyperaemic pressure measurements.