Faculty member is senior author of University of Oxford study behind new statin risk calculator

Dr Constantinos Koshiaris, Assistant Professor of Medical Statistics, is the senior author of a University of Oxford study, published last week in The Lancet Digital Health, that developed a new tool to predict an individual’s risk of developing serious muscle disorders associated with statin treatment.

Statins are widely prescribed to reduce the risk of heart attacks and strokes, but concerns about potential side effects, particularly muscle-related problems, can deter some people from starting or continuing treatment, even when they are likely to benefit. The new model helps put those concerns in context by supporting more informed discussions between patients and healthcare professionals.

The researchers found that more than 60% of people eligible for statin treatment were not taking statins, despite many being at high risk of heart attack or stroke. The study also showed that the majority of individuals identified as eligible for statin treatment were predicted to be at low risk of serious muscle disorders over the following decade.

The clinical prediction model was developed and validated using anonymised health records from more than 5.6 million people registered with GP practices across England. The resulting risk calculator is available via the University of Oxford Innovation software store.

The researchers believe the tool could improve shared decision-making by providing personalised estimates of the risk of serious muscle disorders. This will enable clinicians and patients to weigh the potential benefits against the potential risks of treatment more effectively, rather than relying on population averages or general concerns about side effects.

Commenting on the study, Dr Constantinos Koshiaris and Dr Ting Cai said: ‘Although concerns about muscle side effects can discourage people from taking statins, our findings show that the risk of serious muscle disorders is very low for most people who are eligible for treatment. By providing personalised estimates of risk, this model supports more informed discussions between patients and clinicians, helping them weigh both the potential benefits and harms of treatment’.