Computer assisted clinical decision support tool for management of statins

描述（由申请方提供）：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）的主要可改变风险因素，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，这4100万人中有75%服用七种他汀类药物之一，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，导致可预防的死亡和不必要的医疗保健费用。实现目标LDL-C水平的最重要障碍是无法提供从大型循证数据集合成的优化他汀类药物治疗的实时建议。在没有这种决策支持的情况下，临床医生必须任意选择他汀类药物，并在很长一段时间内滴定剂量，产生可预防的成本。 在VA医院环境中的初步研究表明，他汀类药物管理器（SM），一种正在申请专利的计算机化，基于电子医疗记录（EHR）的算法，可以高准确度预测达到目标LDL-C水平的概率。使用基于个体患者特征（包括伴随临床疾病和药物）的多变量逻辑回归模型，他汀类药物管理器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确保在治疗方案开始时为每位患者开具正确剂量的正确他汀类药物。设想他汀类药物管理算法的进一步开发、扩展和商业化，以降低实现目标LDL-C水平的实验的高成本、延长的时间和频繁的挫折，潜在地降低副作用，改善治疗依从性，并最终降低与升高的LDL-C相关的ASCVD的所得风险。据估计，仅在美国，每年与ASCVD结局的医疗保健改善相关的经济节省就达数千万或数亿美元。 第一阶段研究的第一个目标是使用 一个地区VA医疗保健网络中约201，000例他汀类药物治疗患者的样本，通过选择最有效的他汀类药物和剂量以达到目标LDL-C水平，确认SM在预测达到LCL-C目标方面的精密度（可靠性）。我们还将探索该算法的扩展，以包括可能影响最佳他汀类药物和剂量选择的他汀类药物相关和紧急不良事件。第二个目的是使用VA的国家公司中所有接受他汀类药物治疗的患者（约5，000，000）的数据确定SM的内部（预测）有效性。 数据仓库。我们将比较在广泛的处方他汀类药物和剂量范围内达到的LDL-C水平与SM预测的水平。在第一阶段完成后，SM将在两项大型回顾性EHR研究中得到进一步验证，从而将SM定位为第二阶段的前瞻性外部验证研究。最终，SM将被设计为满足主要集成医疗保健系统的要求，作为嵌入式应用程序纳入其EHR系统范围。

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 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.