Hypercholesterolemia (particularly low-density lipoprotein-cholesterol (LDL-c)) is a major, modifiable risk factor for atherosclerotic cardiovascular disease (ASCVD), the primary cause of death in the US. Today, an estimated 41 million people in the US are hypercholesterolemic with ASCVD and 75% of these 41 million people take one of seven statin drugs that are remarkably effective in reducing elevated LDL-c and cardiovascular morbidity. However, nearly 55% of statin-treated patients do not achieve target LDL-c levels during the first year of treatment, resulting in preventable mortality and unnecessary health care costs. The most important barrier to achieving target LDL-c levels is the lack of real-time statin treatment recommendations synthesized from large, evidence-based datasets. Since clinicians have little guidance in statin selection, they instead typically select statins based on imprecise past experience, start at the lowest dosage and titrate over a prolonged period, generating otherwise preventable costs. Preliminary research in a VA hospital setting indicates that Statin Manager" (SM), a patent-pending computerized, electronic medical record (EMR)-based algorithm can predict with high accuracy the probability of achieving target LDL-c levels. These preliminary results have been confirmed using a national VA Hospital sample of 1.06 million patients. Using multivariate logistic regression models based on individual patient characteristics, including concomitant clinical conditions and medications, SM predicts the probability that target LDL-c levels will be achieved by specific statins at specifc doses. SM ensures that the right statin, in the right dosage, is prescribed for each patient at the beginning of the treatment regimen. Further development, extension, and commercialization of the statin management algorithm will reduce the high cost, extended time and frequent frustration of experimentation to achieve target LDL-c levels, potentially reduce side effects, improve treatment adherence and ultimately reduce the resultant risk of ASCVD associated with elevated LDL- c. The economic savings associated with improved healthcare for ASCVD outcomes is estimated in the billions of dollars annually in the US alone. The overarching goal of Phase II is to complete the research and development necessary to begin roll- out and commercialization of SM. There are five Aims in Phase II: 1) SM external validation and refinement in a retrospective cohort study using a representative, heterogeneous, non-VA, national patient database;2) develop a robust SM prototype based on phase I study results and SM algorithm enhancements from Phase II Aim 1;3) evaluate SM in a Clinical Utility Demonstration Project;4) health economics research to confirm direct health cost savings and lower LDL-c values for those treated with SM's recommended statin and dose;and, 5) Data-Mining using existing software and biomedical literature to identify clinical variables and genomic markers linked to statin efficacy to improve SM's model performance and predictive validity.

Public Health Relevance

This research will further develop Statin Manager (SM), a real-time, computer-assisted, clinical decision support system based on an individual patient's Electronic Medical Record to accurately identify the best statin and starting dose for personalized treatment of high cholesterol. SM will benefit millions of patients, more than half of who do not achieve target levels of LDL-C in the first year of treatment.

National Institute of Health (NIH)
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
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Special Emphasis Panel (ZRG1)
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Hasan, Ahmed AK
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Algorithmrx, LLC
North Chesterfield
United States
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