| アブストラクト | OBJECTIVE: Concerns about muscle-related adverse events have posed a dilemma when considering statin prescription for prevention of cardiovascular disease (CVD). This study aimed to develop a prediction model for an individual's risk of muscle disorders to support clinical decision making in primary care. DESIGN AND METHOD: A prospective cohort design was adopted, using electronic health records from the Clinical Practice Research Datalink in the UK. Males aged over 50 and females aged over 60, who were potentially eligible for statin treatment based on their underlying CVD risk, were followed-up for ten years. The primary outcome was hospitalisation or death in those with a diagnosis of muscle disorders. The Fine-Gray proportional sub-distribution hazards model was fitted to address competing risk of death from other causes. Statin prescriptions within the 12 months before follow up and other predictors were included in the model based on a literature review. RESULTS: The cohort included 1,785,207 patients, with a mean age of 64 and 44% females. Patients prescribed statins were predicted to have a higher risk of muscle disorders (atorvastatin: hazard ratio = 1.77 [95% confidence interval: 1.58 - 1.97]; rosuvastatin: 2.04 [1.58 - 2.63]; simvastatin: 1.58 [1.45 - 1.71]; other statins (fluvastatin/pravastatin): 1.38 [1.14 - 1.68]). Female sex, deprivation, smoking, obesity, frailty, liver or kidney disease, rheumatic arthritis, previous muscle problems, degenerative joint disorders, hypothyroidism, vitamin D or B12 deficiency, and the use of drugs that are potentially myotoxic or interact with statins also increased an individual's risk (Table). An automated risk calculator was developed based on the model (Figure). CONCLUSIONS: This model uses routinely available patient characteristics and medical history to predict an individual's risk of muscle disorders. The calculator may help clinicians and patients communicate the safety concerns and make shared decisions or monitoring strategies on statin treatment. External validation of this model is ongoing to support general application of the risk calculator in clinical practice. |
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