Abstract P074: Visit-to-visit Variability of LDL-C Predicts Statin Non-adherence
Introduction Non-adherence to cardiovascular medications such as statins is a common, important problem. Clinicians have limited means by which to identify medication non-adherence. Visit-to-visit variability (VVV) of LDL-C may represent a novel tool to detect statin non-adherence with greater reliability.
Methods We examined integrated pharmacy claims and clinical data from 782 members of the Boston Medical Center Health Plan, a diverse urban adult population (54.5% female, 38.4% Black, mean age 52.8 years), who were prescribed statins and had at least 3 LDL-C measurements between 2008 and 2011. The predictor variable, LDL-C VVV, was defined by the within-patient standard deviation of LDL-C and was categorized into quintiles. The outcome variable, statin non-adherence, was defined by medication possession ratio less than 80%, based on pharmacy refill data. Multivariable logistic regression models were generated using age, gender, race, mean LDL-C, and LDL-C VVV as covariates.
Results As LDL-C VVV increased across quintiles, the proportion of statin non-adherence increased (64.3%, 71.2%, 89.2%, 92.3%, 91.7%). Higher quintiles of LDL-C VVV had a strong positive association with statin non-adherence with an adjusted odds ratio of 3.4 (95% CI: 1.7-7.1) in the highest versus lowest quintile of LDL-C VVV. The age and gender adjusted model had fair discrimination between adherent and non-adherent patients [C-statistic 0.61 (95% CI: 0.71, 0.79)]; discrimination improved noticeably when incorporating LDL-C VVV in the final adjusted model [C-statistic 0.75 (95% CI: 0.71, 0.79)].
Conclusions VVV of LDL-C demonstrated a strong association with statin non-adherence in a clinical setting. A VVV of LDL-C-based model has good discrimination characteristics for statin non-adherence, potentially enabling clinicians to better detect and address non-adherence using existing data. Research is needed to validate these findings in other populations and investigate similar relationships for other cardiovascular biomarkers.
- © 2013 by American Heart Association, Inc.