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: 8838249
• 负责人: Stephen Hutcherson
• 金额: $ 55.88万
• 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 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8838249
• 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; 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; Mining; Modeling; Morbidity - disease rate; One-Step dentin bonding system; Patient risk; 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; personalized medicine; phase 1 study; prototype; 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)是动脉粥样硬化性心血管疾病(ASCVD)的一个主要的、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ASCVD，这4100万人中有75%的人服用七种他汀类药物中的一种，这七种药物在降低升高的低密度脂蛋白和心血管发病率方面非常有效。然而，近55%接受他汀类药物治疗的患者在第一年的治疗中没有达到目标低密度脂蛋白-c水平，导致了可预防的死亡率和不必要的医疗费用。实现目标低密度脂蛋白-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。由于临床医生对他汀类药物的选择几乎没有指导，他们通常根据不精确的过去经验选择他汀类药物，以最低的剂量开始，并在较长的时间内滴定，产生原本可以预防的成本。在退伍军人医院的初步研究表明，他汀类药物管理器(SM)，一种正在申请专利的计算机化的、基于电子病历(EMR)的算法，可以高精度地预测达到目标低密度脂蛋白水平的概率。这些初步结果已经在全国退伍军人医院106万名患者的样本中得到证实。使用基于个体患者特征的多元Logistic回归模型，包括伴随的临床情况和药物，SM预测特定剂量的特定他汀类药物将达到目标低密度脂蛋白水平的概率。SM确保在正确的剂量下，为每个患者开出正确的他汀类药物。 开始治疗方案。他汀类药物管理算法的进一步开发、扩展和商业化将减少达到目标低密度脂蛋白水平所需的高昂成本、延长的时间和频繁的挫折，潜在地减少副作用，改善治疗依从性，并最终降低因低密度脂蛋白升高而导致的ASCVD风险。据估计，仅在美国，与改善医疗保健相关的ASCVD结果节省的经济成本每年就高达数十亿美元。第二阶段的首要目标是完成SM的推出和商业化所需的研究和开发。第二阶段有五个目标：1)SM外部验证和改进，在使用具有代表性的、不同种类的、非VA的国家患者数据库的回溯性队列研究中进行；2)根据第一阶段研究结果和第二阶段目标1的SM算法改进，开发一个健壮的SM原型；3)在临床实用示范项目中评估SM；4)健康经济学研究，以确认使用SM推荐的他汀类药物和剂量治疗的患者直接的医疗成本节省和较低的低密度脂蛋白胆固醇值；以及5)数据挖掘，使用现有软件和生物医学文献来确定与他汀类药物疗效相关的临床变量和基因组标记，以改进SM的模型性能和预测有效性。