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Take a look at a selection of our recent media coverage:

Highly purified EPA reduces adverse cardiovascular events in at-risk patients

20th December 2022

The use of a purified EPA formulation lowered adverse cardiovascular events in those with coronary artery disease compared to a control group

Use of a purified eicosapentaenoic acid (EPA) formulation in patients with coronary artery disease prescribed statins, reduced the incidence of adverse cardiovascular outcomes such as myocardial infarction and stroke compared to a control group not taking the formulation, according to the findings of a study by Japanese researchers presented at the American Heart Association (AHA) conference in Chicago.

Statin therapy leads to a reduction of cardiovascular events in both primary and secondary prevention. Since no treatment is fully effective in all patients, one therapy that has attracted interest over many years has been the omega-3 polyunsaturated fatty acids (PUFAs). However, recent studies with PUFAs have been contradictory with one trial in patients with elevated triglyceride levels despite the use of statins, finding that the risk of ischaemic events, was significantly lower among those who received 2 g of icosapent ethyl twice daily (a highly purified EPA) than among those who received placebo. In contrast, a second trial, again in statin-treated patients, concluded that the addition of omega-3 to usual background therapies resulted in no significant difference in a composite outcome of major adverse cardiovascular events.

Due to these contrary findings, The Japanese team sought greater clarity of the role of PUFAs and presented data from the Randomized Trial for Evaluating the Secondary Prevention Efficacy of Combination Therapy – statin and EPA (RESPECT-EPA) trial. Participants, all of whom had chronic coronary artery disease and were treated with statin therapy, were randomised to highly purified EPA (1800 mg/day) or control groups (i.e., no EPA therapy) and the primary endpoint was cardiovascular death, non-fatal myocardial infarction, non-fatal ischaemic stroke, unstable angina pectoris, and clinically indicated coronary revascularisation. A further inclusion criterion was a low EPA/arachidonic acid ratio (< 0.40).

Highly purified EPA and cardiovascular outcomes

A total of 2506 participants with a mean age of 68 years (17% female), of whom 45% had diabetes, were included and randomised to the highly purified EPA (icosapent ethyl 1800 mg daily, n = 1,249) or control and followed for 5 years.

The primary outcome occurred in 10.9% of the icosapent ethyl group vs. 14.9% of the control group (p = 0.055). For the secondary outcomes, sudden cardiac death, myocardial infarction, unstable angina, or coronary revascularisation, this occurred in 8.0% in the icosapent ethyl group vs. 11.3% in the control group (p = 0.031). However, there was a higher incidence of gastrointestinal disorders in the icosapent ethyl group (3.4% vs 1.2%, p < 0.001).

The authors concluded that among Japanese patients with chronic coronary artery disease and treated with statin therapy, icosapent ethyl may be associated with a reduction in adverse cardiovascular outcomes.

Citation
Bavry AA et al. Randomized Trial for Evaluation in Secondary Prevention Efficacy of Combination Therapy–Statin and Eicosapentaenoic Acid – RESPECT-EPA. Presented at the American Heart Association Scientific Sessions, Chicago, IL, November 6, 2022

AI read chest radiographs help to identify severe CAD

21st November 2022

An AI read chest radiograph can be used to predict the presence of severe coronary artery disease and is superior to accepted risk scores

An AI read chest radiograph can be used to identify patients with severe coronary artery disease who have been referred with suspected angina according to the results of a study by German researchers.

Globally, cardiovascular diseases are the leading cause of death resulting in an estimated 17.9 million lives lost every year. The first step in assessing a patient with suspected stable coronary artery disease (CAD) is to determine the pre-test probability and there are several risk scores available including the Diamond-Forrester score, the CAD Consortium clinical score and CONFIRM risk score. However, the development of artificial intelligence (AI) systems and their use with cardiovascular imaging, is likely to better characterise disease and personalise therapy. This potential made the German researchers re-think about how simple first-line diagnostic tools such as a radiograph might benefit from incorporation of an AI system. They set out to explore whether an AI read chest radiograph might be of value for the detection of significant coronary artery disease (CAD).

The researchers retrospectively considered patients referred to hospital with suspected angina and who underwent coronary angiography and had a chest radiograph. They included only those patients who had posteroanterior (with the patient standing) and anteroposterior (patient sitting) chest radiographs simply because lateral radiographs were not used in all patients. The team used a deep convolutional neural network (DCNN) which was trained to detect significant CAD based on invasive coronary angiography reports. They DCNN had a binary classification, i.e., severe CAD was either absent or present and the model was trained and validated on patients referred for angiography. The performance of the model was assessed based on the area under the receiver operating characteristics curve (AUC) and the associated sensitivity and specificity.

AI read chest radiographs and coronary artery disease

A total of 7728 participants with a mean age of 74 years (70.3% male) were included in the retrospective analysis and of whom, 53% had severe CAD confirmed by invasive angiography.

The AI chest read model had an AUC of 0.73 (95% CI 0.69 – 0.76) and a sensitivity of 90% and a specificity of 31%. Adding the patient’s angina status improved the predictive power of the model for the detection of severe CAD (AUC = 0.77). Moreover, addition of the Diamond Forrester score also improved the predictive power to a similar level (AUC = 0.76).

Using logistic regression, the DCNN prediction was the strongest and independent determinant of severe CAD (odds ratio, OR = 1.04, 95% CI 1.03 – 1.04, p < 0.001).

The authors concluded that an AI-read chest radiograph could be used to determine the pre-test probability of significant CAD in patients referred for suspected angina and called for future studies to externally validate the algorithm to develop a clinically applicable tool, that could support CAD screening in broader settings.

Citation
D’Ancona G et al. Deep learning to detect significant coronary artery disease from plain chest radiographs AI4CAD. Int J Cardiol 2022

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