Computer assisted clinical decision support tool for management of statins

用于他汀类药物管理的计算机辅助临床决策支持工具

• 批准号: 8715636
• 负责人: Stephen Hutcherson
• 金额: $ 91.09万
• 依托单位: ALGORITHMRX, LLC
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8715636
• 关键词: Adverse effects; Adverse event; Affect; Algorithms; Atherosclerosis; Biological Markers; Calibration; Cardiovascular system; Caring; Cause of Death; Characteristics; Cholesterol; Client; Clinical; Clinical Data; Clinical Decision Support Systems; Code; Cohort Studies; Comorbidity; Computer Assisted; Computer software; Computerized Medical Record; Cost Savings; Data; Data Set; Databases; Development; Disease Outcome; Dose; Economics; Ensure; Epidemiologic Studies; Feedback; Frustration; General Population; Genomics; Goals; Health Care Costs; Healthcare; Hospitals; Human; Individual; Information Centers; Intercept; Java; LDL Cholesterol Lipoproteins; Laboratories; Legal patent; Link; Lipids; Literature; Logistic Regressions; Low-Density Lipoproteins; Massachusetts; Methodology; Metric; Mining; Modeling; Morbidity - disease rate; One-Step dentin bonding system; Patients; Performance; Pharmaceutical Preparations; Phase; Physicians; Population; Probability; Quality Control; Recommendation; Research; Risk; Risk Factors; Sampling; Savings; Site; Software Engineering; Source; Surveys; System; Testing; Time; Treatment Protocols; Validation; Validity and Reliability; Veterans; Visit; Writing; base; cohort; commercialization; computerized; cost; cyber security; data mining; design; dosage; evidence base; experience; health administration; health economics; hypercholesterolemia; improved; innovation; method development; modifiable risk; mortality; next generation; phase 1 study; prototype; public health relevance; research and development; research study; response; satisfaction; success; text searching; tool; treatment adherence; validation studies

DESCRIPTION (provided by applicant): 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.

描述（由申请人提供）：高胆固醇血症（特别是低密度脂蛋白-胆固醇（LDL-c））是动脉粥样硬化性心血管疾病（ASCVD）的主要、可改变的危险因素，ASCVD是美国的主要死亡原因。今天，美国估计有4100万人患有高胆固醇血症ASCVD，其中75%的人服用七种他汀类药物中的一种，这些药物在降低LDL-c升高和心血管发病率方面非常有效。然而，近55%的他汀类药物治疗患者在治疗的第一年没有达到目标LDL-c水平，导致可预防的死亡率和不必要的医疗费用。实现目标LDL-c水平的最重要障碍是缺乏从大型循证数据集合成的实时他汀类药物治疗建议。由于临床医生在他汀类药物选择方面几乎没有指导，他们通常根据不精确的过去经验来选择他汀类药物，从最低剂量开始，并在较长时间内滴定，从而产生本可避免的成本。VA医院环境的初步研究表明，他汀管理器（SM），一种正在申请专利的计算机化电子病历（EMR）算法，可以高精度地预测达到目标LDL-c水平的概率。这些初步结果已经通过国家退伍军人医院106万名患者的样本得到证实。使用基于个体患者特征的多变量逻辑回归模型，包括伴随的临床状况和药物，SM预测特定剂量的特定他汀类药物达到目标LDL-c水平的概率。SM确保正确的他汀类药物，以正确的剂量，为每个病人开处方