SBIR-STTR Award

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 hypercholesterolemia 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 inability to deliver real-time recommendations for optimized statin treatment synthesized from large, evidence-based datasets. In the absence of such decision support, clinicians must choose statins arbitrarily and titrate doses over a prolonged period, generating preventable costs. Preliminary research in a VA hospital setting indicates that Statin Manager (SM), a patent-pending computerized, electronic health care record (EHR)-based algorithm can predict with high accuracy the probability of achieving target LDL-C levels. Using multivariate logistic regression models based on individual patient characteristics, including concomitant clinical conditions and medications, Statin Manager predicts the probability that target LDL-C levels will be achieved by specific statins at specific 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 is envisioned to 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 tens or hundreds of millions of dollars annually in the US alone. The first aim of this Phase I study uses a sample of ~201,000 statin-treated patients in a regional VA healthcare network to confirm the precision (reliability) of SM in predicting achievement of LCL-C goal by selecting the most efficacious statin and dose to achieve targeted LDL-C levels. We will also explore extension of the algorithm to include statin-related and emergent adverse events potentially impacting optimal statin and dose selection. The second aim is to determine the internal (predictive) validity of SM using data from all statin- treated patients (~5,000,000) in VA's national Corporate Data Warehouse. We will compare LDL-C levels achieved over a broad range of prescribed statins and doses with those predicted by SM. Upon completion of Phase I, SM will have been further validated in two large retrospective EHR studies, thus positioning SM for a prospective, external validation study in Phase II. Ultimately, SM will be designed to meet the requirements of major integrated healthcare systems for inclusion as an embedded application in their EHR system-wide.

Public Health Relevance Statement:


Public Health Relevance:
This research will further develop Statin Manager, a real-time, computer-assisted, EHR-based clinical decision support tool to accurately identify the best statin and starting dose for personalized treatment of high cholesterol for the millions of patients who do not achieve target levels of LDL-C in the first year of treatment.

Project Terms:
Achievement; Address; Adverse effects; Adverse event; Affect; Algorithms; analytical tool; Atherosclerosis; base; Cardiovascular Diseases; cardiovascular disorder risk; Cardiovascular system; Cause of Death; Cessation of life; Characteristics; Cholesterol; Clinical; commercialization; Computer Assisted; computerized; Coronary heart disease; cost; Data; Data Analyses; Data Set; Databases; design; Development; diabetic; Disease Outcome; dosage; Dose; Drug Interactions; Economics; Effectiveness; Electronic Health Record; Electronics; Ensure; evidence base; experience; Frustration; Goals; Health Care Costs; Health system; Healthcare; Healthcare Systems; Hepatotoxicity; Hospitals; hypercholesterolemia; improved; Individual; Informatics; innovation; LDL Cholesterol Lipoproteins; Legal patent; Logistic Regressions; Low-Density Lipoproteins; medical schools; Medicine; meetings; Modeling; modifiable risk; Morbidity - disease rate; Mortality Vital Statistics; New Mexico; Patients; Pharmaceutical Preparations; Phase; phase 1 study; phase 2 study; Positioning Attribute; Primary Care Physician; Probability; professor; prospective; Public Health Informatics; public health relevance; Recommendation; Research; response; Rhabdomyolysis; Risk; Risk Factors; Sampling; Savings; Series; Services; System; Time; tool; treatment adherence; Treatment Protocols; United States; Universities; Validation; validation studies; Validity and Reliability

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 thebeginning 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.

Thesaurus Terms:
Adverse Effects;Adverse Event;Affect;Algorithms;Atherosclerosis;Base;Biological Markers;Calibration;Cardiovascular System;Caring;Cause Of Death;Characteristics;Cholesterol;Client;Clinical;Clinical Data;Clinical Decision Support Systems;Code;Cohort;Cohort Studies;Commercialization;Comorbidity;Computer Assisted;Computer Software;Computerized;Computerized Medical Record;Cost;Cost Savings;Cyber Security;Data;Data Mining;Data Set;Databases;Design;Development;Disease Outcome;Dosage;Dose;Economics;Ensure;Epidemiologic Studies;Evidence Base;Experience;Feedback;Frustration;General Population;Genomics;Goals;Health Administration;Health Care Costs;Health Economics;Healthcare;Hospitals;Human;Hypercholesterolemia;Improved;Individual;Information Centers;Innovation;Intercept;Java;Laboratories;Ldl Cholesterol Lipoproteins;Legal Patent;Link;Lipids;Literature;Logistic Regressions;Low-Density Lipoproteins;Massachusetts;Method Development;Methodology;Metric;Mining;Modeling;Modifiable Risk;Morbidity - Disease Rate;Mortality Vital Statistics;Next Generation;One-Step Dentin Bonding System;Patients;Performance;Pharmaceutical Preparations;Phase;Phase 1 Study;Physicians;Population;Probability;Prototype;Public Health Relevance;Quality Control;Recommendation;Research;Research And Development;Research Study;Response;Risk;Risk Factors;Sampling;Satisfaction;Savings;Site;Software Engineering;Source;Success;Surveys;System;Testing;Text Searching;Time;Tool;Treatment Adherence;Treatment Protocols;Validation;Validation Studies;Validity And Reliability;Veterans;Visit;Writing;