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Machine learning makes strides in coronary artery disease assessments

The model accurately tracked the degree of narrowing of coronary arteries (coronary stenosis), mortality, and complications such as heart attack.

Just as with other medical requirements, the COVID-19 vaccination helps ensure the best possible outcome for organ transplant patients. Image - Lena Gulenko, (CC BY 4.0)
Just as with other medical requirements, the COVID-19 vaccination helps ensure the best possible outcome for organ transplant patients. Image - Lena Gulenko, (CC BY 4.0)

A digital marker for coronary artery disease has been designed by medical researchers at Mount Sinai. The machine learning-derived model could lead to better disease screening, diagnostics, and management.

Coronary artery disease is the most common type of heart disease. It is also the leading cause of death worldwide. It describes what happens when the heart’s blood supply is blocked or interrupted by a build-up of fatty substances in the coronary arteries.

The machine learning component was used to assess clinical data from electronic health records. The analysis has produced a computer-derived marker for coronary artery disease, with the aim of better measuring clinically important characterisations of the disease. For this the researchers used some 80,000 electronic health records from two large health system-based biobanks, the BioMe Biobank at the Mount Sinai Health System and the UK Biobank.

In order to ensure the data was representative, the medical records included participants of African, Hispanic/Latino, Asian, and European ethnicities, as well as a large share of women.

The study maps the characteristics of coronary artery disease on a spectrum. This enables an assessment of each individual’s mix of risk factors and disease processes, enabling medics to determine where they fall on the spectrum. A better understanding of this may avoid missed diagnoses, inappropriate management, and poorer clinical outcomes, say the investigators.

Areas assessed on the spectrum include the amount of plaque build-up in the arteries of the heart. Other assessments included vital signs, laboratory test results, medications, symptoms, and diagnoses.

According to lead researcher Ron Do: “The information gained from this non-invasive staging of disease could empower clinicians by more accurately assessing patient status and, therefore, inform the development of more targeted treatment plans.”

The concluding data review showed that the probabilities from the model accurately tracked the degree of narrowing of coronary arteries (coronary stenosis), mortality, and complications such as heart attack.

The next phase may lead to other researchers designing clinical trials based on appropriate patient stratification and the outcome may lead to more individualized therapeutic strategies. Before this, the researchers are planning a prospective large-scale study to further validate the clinical utility and actionability of the model.

The research appears in the medical journal The Lancet. The research paper is titled “Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts.”

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Written By

Dr. Tim Sandle is Digital Journal's Editor-at-Large for science news. Tim specializes in science, technology, environmental, business, and health journalism. He is additionally a practising microbiologist; and an author. He is also interested in history, politics and current affairs.

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