Abstract 104: Statin Advisor: A Model to Predict Low-Density Lipoprotein Responsiveness

Abstract

Background: For patients with ASCVD, reduction of LDL-C levels to a goal of <100mg/dl ranges from 36-74%. This broad success rate reflects inconsistency in clinical practice and biologic variability in response to statins, and suggests that a significant proportion of ASCVD patients may not obtain the full therapeutic benefit of statin therapy. Contributing to this problem is the fact that there are no specific guidelines for managing ASCVD patients who do not have an adequate LDL-C response to statin therapy. Methods: All ASCVD patients who had ever been prescribed a statin in the Veterans Affairs Corporate Data Warehouse were analyzed. Of 268 possible statin up-titrations or within intensity-transitions for patients not at LDL-C goal, 41 statin transitions with at last 2000 patients were included, representing a total of 1.16 million patients. The transitions were randomly divided into a derivation sample (60%) and a validation sample (40%). Logistic regression models were developed to predict the likelihood of specific statin transitions achieving a goal LDL-C level of <100mg/dl. Predictor variables included demographics, BMI, medication possession ratio, prior LDL-C, and if the patient was on medications with statin interactions. These models were then validated, and predicted probability of achieving LDL-C <100mg/dl were categorized as low (<30%), intermediate (30-70%), or high (>70%). Results: Table below. Conclusion: These models were able to accurately identify groups of patients who would have a low (19.4%; 19,548 / 100,730), intermediate (49.8%; 165,356 / 331,801), or high (72.6%; 21,301 / 29,350) likelihood of achieving goal LDL-C, by a specific statin transition. Any statin transition had only a 44.6% (206,205 / 461,881) probability of achieving LDL-C <100 mg/dl. Use of these models may aide practitioners at the frontline to determine which statin transition would most likely achieve LDL-C <100 mg/dl, based on specific patient characteristics. Based on these data, there is a potential to eliminate over 20% (100,730 / 461,881) of transitions which would be unlikely to result in achieving LDL-C goal, thus potentially reducing time to goal and wasteful utilization resources.