Real-World Prevalence of Statin Intolerance in General Practice

AimsThis study focused on patients who were prescribed statins as primary prevention of cardiovascular diseases. This study aimed to identify statin intolerant patients and determine the prevalence of statin intolerance by implementing electronic health record (EHR)-detectable statin intolerance electronic phenotyping algorithms, and to validate these algorithms. MethodsThis study used the Electronic Practice Based Research Network (ePBRN) dataset. The methodology took place in four stages: (1) literature review to identify electronic phenotypes, (2) implementation of electronic phenotypes on ePBRN, (3) development and implementation of reference standard, (4) validation of electronic phenotypes. ResultsSix EHR-detectable statin intolerance electronic phenotypes were identified, including the Minnesota Combined Rule-Based algorithm, Japan-Statin induced myopathy (SIMs), USA-SIMs, Singapore-SIMs (algorithms A, B, C, and D), Japan-Statin-associated muscle toxicity (SAMT), and NHS-UK-Statin intolerance pathway. The prevalence of statin intolerance among those prescribed statins in ePBRN was 5.09%. The Singapore SIMs-B algorithm showed the highest accuracy (57.05%), sensitivity (92.95%), negative predictive value (43.43%), and F1 (71.51%) scores, while the Japan SAMT algorithm showed the highest specificity (99.13%), positive predictive value (76.19%), and correlation coefficient (0.05%). ConclusionThe prevalence of statin intolerance in ePBRN is at the low end of the 5-15% range reported in Australia and globally. The differences in prevalence calculations may be due to the varying definitions of intolerance. Our findings suggest that EHR-detectable phenotypes should be used as decision-support aid rather than as definitive diagnostic tools and that clinical judgement and patient engagement are necessary for the management of suspected statin intolerance. Key pointsThis study found that: O_LIThe prevalence of statin intolerance among those prescribed statins in the ePBRN dataset was 5.09%, which is at the low end of the 5-15% range reported in Australia and globally. C_LIO_LIDifferent phenotyping algorithms show various prevalence estimations, which may be due to the varying definitions of intolerance. C_LIO_LIEHR-detectable phenotypes should be used as decision-support aids rather than as definitive diagnostic tools and that clinical judgement and patient engagement is necessary for the management of suspected statin intolerance. C_LI