PRM20 - Predictive Modeling to Assess Predictors of Treatment Success and Failure Among Combination Statin Therapy Patients

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Combination statin therapy may help to further lower low-density lipoprotein cholesterol (LDL-C) better than monotherapy alone. The objective of this study was to apply predictive modeling methodology to determine the predictors of success and failure in achieving LDL-C goals after combination statin-fibrate therapy in patients diagnosed with hypertriglyceridemia (HTG). A large claims database was used to identify patients initiating a fibrate between January 2011 and December 2011 (index date). Diagnosis of HTG and the use of statins were confirmed within 6 months before the index date. A total of 622 patients were selected for the current analysis. Patients were categorized into very high risk, high risk, moderate risk, and low risk groups. Logistic regression and two-group discriminant analysis models based on 17 potential predictors for treatment success or failure were constructed. At index, the median triglyceride (TG) level among all patients was 95.5 mg/dL, LDL-C level was 92 mg/dL, and high-density lipoprotein (HDL) was 40 mg/dL. The mean age was 54 years. Two predictors were associated with combination statin-fibrate treatment success or failure and accounted for 5.3% of variance between groups. Low HDL (defined as <40 mg/dL) (OR=0.35; 95% CI, 0.20-0.59) and peripheral arterial disease (OR=0.10; 95% CI, 0.02-0.38) were significantly associated with treatment failure. Low HDL variable was the key discriminator. Analytic insights enabled by predictive models may help researchers gain information on discriminating factors about certain target treatment groups and drug classes. A set of key predictors may suggest opportunities to understand and predict treatment success and failure of targeted groups and/or drug classes. These predictors may be useful in developing treatment strategies that will optimize outcomes.