Algorithm could cut strokes by identifying risk factor

Thousands of strokes could be avoided in the UK each year after doctors developed an algorithm that spots patients at risk by scanning millions of GP records.

- Andrew Gregory Health editor

The tool uses machine learning to scour medical data for red flags that could identify patients with undiagnosed atrial fibrillation (AF), a heart condition that significantly increases stroke risk.

About 1.6 million people in the UK have been diagnosed with AF. But doctors fear there are thousands more who are unaware they are living with the condition because they have no symptoms. If diagnosed and treated early, AF can be managed and stroke risk reduced.

The algorithm works out someone's risk based on a number of factors including age, gender, ethnicity and whether or not they have other medical conditions such as heart failure, high blood pressure, diabetes, heart disease or chronic obstructive pulmonary disease. Estimates suggest AF is a contributing factor in 20,000 strokes each year in the UK.

It was developed by doctors and scientists at Leeds teaching hospitals NHS trust and the University of Leeds, with funding from the British Heart Foundation (BHF) charity.