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Introduction
Cardiovascular disease is a major health burden accounting for nearly one-third of all deaths around the world in 2004 and is the leading cause of premature death in the UK. Thus, early identification of individuals at an increased risk of developing cardiovascular disease is an important challenge. In the UK, tools such as QRISK2 or the Framingham equation are used to identify high-risk patients who could benefit from lifestyle changes (eg, smoking cessation, weight control) or lipid modification therapy using statins. 1 While there are clear benefits of statins for patients at high risk of cardiovascular disease, a number of studies have observed negative effects on a range of clinical outcomes, including acute renal failure, 2 cataract, 3 liver dysfunction 4 and moderate or severe myopathic events. 5 Such concerns have led to the development of four risk scores to identify those who are at greatest risk of adverse events of statins (acute renal failure, cataract, liver dysfunction and moderate or severe myopathic events) to individualise risk estimation for patients and provide more information during the general practice consultation. 6
The four risk scores (QStatin scores) were developed and validated on a large cohort of patients (3 million) from the QRESEARCH ( http://www.qresearch.org ) database; two-thirds of the cohort was randomly allocated for model development and one-third to model validation. The QRESEARCH database is a large database comprising over 12 million anonymised health records from 557 practices throughout the UK using the EMIS computer system (used in 59% of general practices in England). QStatin scores were developed on 2 million patients aged between 35 and 84 years with 1969 incident cases of acute renal failure, 36 541 incident cases of cataract, 15 020 incident cases of moderate/serious liver dysfunction and 1406 incident cases of moderate/serious myopathy between 1 January 2002 and 31 December 2008. The models were derived using a Cox proportional hazards model using fractional polynomials to model non-linear risk relationships with continuous predictors. Multiple imputation was used to replace missing values for key risk predictors (body mass index and smoking status) to reduce the biases that can occur when omitting individuals with incomplete data. The risk factors included in the final prediction models are described in table 1 and open source code...