Adherence to statin treatment in patients with familial hypercholesterolemia: A dynamic prediction model

Background: Statins are the primary therapy in patient with heterozygous familial hypercholesterolemia (HeFH). Non-adherence to statin therapy is associated with increased cardiovascular risk. Objective: We constructed a dynamic prediction model to predict statin adherence for an individual HeFH patient for each upcoming statin prescription. Methods: All patients with HeFH, identified by the Dutch Familial Hypercholesterolemia screening program between 1994 and 2014, were eligible. National pharmacy records dated between 1995 and 2015 were linked. We developed a dynamic prediction model that estimates the probability of statin adherence (defined as proportion of days covered >80%) for an upcoming prescription using a mixed effect logistic regression model. Static and dynamic patient-specific predictors, as well as data on a patient's adherence to past prescriptions were included. The model with the lowest AIC (Akaike Information Criterion) value was selected. Results: We included 1094 patients for whom 21,171 times a statin was prescribed. Based on the model with the lowest AIC, age at HeFH diagnosis, history of cardiovascular event, time since HeFH diagnosis and duration of the next statin prescription contributed to an increased adherence, while adherence decreased with higher untreated LDL-C levels and higher intensity of statin therapy. The dynamic prediction model showed an area under the curve of 0.63 at HeFH diagnosis, which increased to 0.85 after six years of treatment. Conclusion: This dynamic prediction model enables clinicians to identify HeFH patients at risk for non-adherence during statin treatment. These patients can be offered timely interventions to improve adherence and further reduce cardiovascular risk.