How simple questions identify people at risk of heart attack

Asking people simple questions about their social situation in addition to medical measures will give a more accurate picture of who might have a heart attack in the future, a new study has revealed.

The study led by University College London (UCL) researchers and published in the European Heart Journal, revealed that for the first time asking questions on factors such as educational qualifications, employment, marital status, mental health, BMI, and physical activity could be crucial in identifying who is most at risk of heart disease.

The report of the study stated that these factors were found to alter treatment decisions, about whether to use preventative drugs called statins or not, for as many as one in 10 people.

The lead author of the study, Dr. Taavi Tillmann (UCL Institute of Global Health), said their study suggested that in terms of risk prediction, simple questionnaire measures on behavioural and psychosocial factors may be as informative as established biomedical risk factors such as blood pressure and cholesterol, adding that this means that in England, conversations about such topics may become a routine part of the one million health checks that already take place in general practices every year.

“Secondly, this discovery may allow some people to undertake sophisticated health checks fully online without leaving their house at all, which is well-timed given the ongoing risks of COVID-19.”

The research team looked at data from 20,000 participants across different parts of Europe who took part in cohort studies started over a decade ago. Now 10 years later, researchers know with hindsight who developed heart diseases and who did not. They went back to the original data from a decade ago, and looked to see which risk predication model is more accurate: either the traditional score model, or a new Hapiee score model that the researchers created. The Hapiee score model performed better in all counts.

The findings now provide clinicians and public health experts new validated risk prediction algorithms, and further strengthen the argument that psychosocial factors can have real-life relevance.