Peer-reviewed / Modelling study / People
Researchers at the Nuffield Department of Primary Care Health Sciences, University of Oxford, have developed a new calculator that estimates a person's risk of developing serious muscle disorders from statins. The tool could help patients and clinicians make more informed decisions about using these widely prescribed medications to prevent heart attacks and strokes.
Published in The Lancet Digital Health , the research behind the calculator found that more than 98% of people identified by their GPs as eligible for statin treatment were predicted to be at low risk of serious muscle disorders over the next decade, despite concerns about side effects remaining a common barrier to treatment.
The study also highlighted a substantial treatment gap. More than 60% of people eligible for statin treatment were not taking statins, despite some being at high risk of heart attack or stroke. The researchers believe the calculator could support treatment discussions by providing patient-specific estimates of risk, rather than relying solely on population averages or general concerns about side effects.
Model development
The new calculator, available via Oxford University Innovation software store, is based on a clinical prediction model developed and validated using anonymised health records from more than 5.6 million people registered with GP practices across England. The researchers used data from more than 1.7 million people to develop the model and a further 3.9 million people to test its accuracy.
The model uses 22 routinely recorded factors, including age, sex, ethnicity, body mass index, smoking status, existing health conditions, previous muscle problems, vitamin D deficiency, medication use and statin prescription, to estimate an individual's risk of serious muscle disorders over one, five and ten years.
The researchers envisage the calculator being used alongside cardiovascular risk tools such as QRISK, enabling patients and clinicians to consider both the benefits from preventing heart attacks and strokes and the potential risk of experiencing serious muscle disorders when discussing treatment options.
Statins and serious muscle disorders
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 calculator helps put those concerns in context and support discussions between doctors and patients.
The researchers focused on serious muscle disorders leading to hospital admission or death, rather than milder symptoms such as muscle aches and pains. Many mild muscle symptoms reported during statin treatment are not actually caused by statins and shouldn’t prevent patients from starting statin treatment. Serious muscle disorders are much rarer but the potential risk and severity remain an important consideration when weighing the benefits and harms of statin treatment.
Dr Ting Cai , Research Fellow in the Nuffield Department of Primary Care Health Sciences, University of Oxford, and lead author of the study, said:
"Serious muscle disorders are one of the most widely discussed concerns about statins, but our findings suggest that the risk is very low for the vast majority of people who may benefit from treatment. Understanding a person's risk can help put those concerns into perspective, support more informed treatment decisions and provide reassurance. For the small number of people at higher risk, it gives clinicians a clearer basis for discussing monitoring, checks or alternative treatment options.”
Professor James Sheppard , Professor of Primary Care Research at the University of Oxford and a senior author of the study, said:
"Treatment decisions are often based on estimates of a person's future cardiovascular risk, but much less information is available about their individual risk of adverse outcomes. This research helps address that gap by providing a way to estimate a person's risk of serious muscle disorders alongside their cardiovascular risk. Bringing those two pieces of information together could support more personalised and better-informed decisions about statin treatment."
Professor Constantinos Koshiaris, Assistant Professor of Medical Statistics at the University of Nicosia Medical School and a senior author of the study, said:
“Clinical decisions are often based on estimates of potential benefit, but understanding potential harms is equally important. This model provides a way to quantify that risk at an individual level, helping support more balanced discussion about treatment options.”
By providing personalised risk estimates, the researchers hope the calculator will help patients and clinicians weigh the benefits and risks of statins treatment more confidently and make better-informed decisions about cardiovascular disease prevention.
ENDS
Notes to editors
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Full citation: Cai T, Hirst JA, Nicholson BD, McManus RJ, Hobbs FDR, Sheppard JP, Koshiaris C. Predicting the risk of serious muscle disorders in individuals eligible for statin treatment in England: derivation and validation of a clinical prediction model. Lancet Digit Health 2026. doi:10.1016/j.landig.2026.101024.
Risk calculator: The online risk calculator based on the model will be available from the Oxford University Innovation software store: STRATIFY-StatinMD Risk Calculator - Academic use
Funding: This study was funded by a British Heart Foundation PhD Scholarship (ref: FS/19/13/34235). James Sheppard and Constantinos Koshiaris were supported by the Wellcome Trust and the Royal Society (Sir Henry Dale Fellowship, ref: 211182/Z/18/Z) and the National Institute for Health and Care Research (NIHR) School for Primary Care Research. Richard McManus was supported by an NIHR Senior Investigator award. Richard Hobbs was partially supported by the NIHR Applied Research Collaboration Oxford and Thames Valley.
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The Lancet Digital Health
Predicting the risk of serious muscle disorders in individuals eligible for statin treatment in England: derivation and validation of a clinical prediction model
25-Jun-2026