Risk Stratification and Targeted Therapy for HELP Diseases in Veterans

描述（由申请人提供）： 目的：我的总体职业目标是使用新的方法来开发和实施系统，支持VA内外严重，高成本的亚专业条件的有效管理。在本CDA中，我计划使用炎症性肠病（IBD）作为模型条件来实现以下特定目标：1）比较传统回归与机器学习模型预测IBD恶化的准确性和校准; 2）开发和使用微观模拟模型来比较使用当前指南做出患者决策的临床和经济影响，一个传统的基于回归的模型和一个基于机器学习的模型;以及，3）为患有IBD的退伍军人开发和试点个性化的医疗决策支持工具。研究计划：患有“高风险、低患病率”（HELP）疾病（如类风湿性关节炎、多发性硬化症和炎症性肠病）的退伍军人通常会加重，导致可预防的死亡或严重发病。虽然这些患者中的一些需要终身，昂贵和潜在有害的药物来预防严重并发症，但许多其他患者的风险较低，并且可以更好地使用更便宜和危害更小的药物进行治疗，或者在发作时使用“按需”治疗。此外，已经清楚地表明，医生没有以有效的方式开药，事实上，当有自由选择的时候，可能会过度治疗。因此，使用生物标志物驱动的工具将患有HELP疾病的退伍军人分为高风险和低风险的退伍军人，这为显着提高退伍军人护理的质量和效率，并最大限度地减少与更积极的治疗相关的伤害提供了巨大的希望。 开发工具和决策支持系统来指导临床医生为患有帮助疾病的退伍军人制定个性化的医疗决策，这对退伍军人事务部有特别的应用，因为在每一个设施都有一名医生对每一种帮助疾病进行亚专业化是不可行的。然而，为了实施这种“有针对性的”或“定制的”预防方法以针对疾病恶化和治疗对个体进行风险分层，临床医生必须知道个体的疾病并发症的基线风险和个体将从治疗中受益（或遭受伤害）的概率。在退伍军人群体中开发和验证风险分层工具是通过VA实现有效的以患者为中心的HELP疾病护理的重要第一步。为了实现这一目标，本CDA建议开发“有针对性的预防”预测工具和决策支持系统，以促进及时和成本效益的治疗HELP疾病的交付，并将其与临床医生目前使用的“疾病驱动”模型进行比较。该提案侧重于IBD作为HELP疾病的模型条件。方法：本研究计划包括一系列的序贯研究。在目标1（获奖的第1年和第2年）中，我将比较回归模型和机器学习方法预测IBD退伍军人疾病恶化的准确性，进行区分，校准和重新分类分析。在2-4年，我将开发 并使用微观模拟模型来比较基于目标1中开发的风险预测模型的生物标志物驱动策略与生物标志物驱动的疾病管理（常规护理）策略，以及评估组合方法。然后将比较两种策略的临床和经济效果（目的2）。最后，从第四年开始，我将使用上述工作开发和试点测试个性化医疗决策支持工具（目标3）。在第3-4年期间还将提交IIR，以在多个研究中心的混合II型实施试验中测试临床干预和实施干预。! 公共卫生相关性： 患有“高风险、低患病率”（HELP）疾病（如类风湿性关节炎、多发性硬化症和炎症性肠病（IBD））的退伍军人通常会加重，导致可预防的死亡或严重发病。开发工具和决策支持系统来指导临床医生为患有HELP疾病的退伍军人进行个性化医疗决策，这对VA具有特殊的应用，因为在每种HELP疾病的每个设施都有一名医生是不可行的。在退伍军人群体中开发和验证风险分层工具是通过VA实现有效的以患者为中心的HELP疾病护理的重要第一步。为了实现这一目标，本CDA建议开发“有针对性的预防”预测工具和决策支持系统，以促进对HELP疾病提供及时和具有成本效益的治疗，并将其与临床医生目前使用的“症状驱动”模型进行比较。该提案侧重于IBD作为HELP疾病的模型条件。

DESCRIPTION (provided by applicant): Objectives: My overall career goal is to use novel methods to develop and implement systems that support the effective management of serious, high-cost subspecialty conditions within and outside of VA. In this CDA I plan to use inflammatory bowel disease (IBD) as a model condition to accomplish the following specific aims: 1) to compare the accuracy and calibration of traditional regression vs. machine-learning models for predicting IBD exacerbations; 2) to develop and use a microsimulation model to compare the clinical and economic impact of making patient decisions using current guidelines, a traditional regression-based model and a machine learning-based model; and, 3) to develop and pilot a personalized medical decision support tool for veterans with IBD. Research Plan: Veterans with "High Expense, Low Prevalence" (HELP) diseases such as rheumatoid arthritis, multiple sclerosis, and inflammatory bowel disease often have exacerbations that can result in preventable mortality or major morbidity. Although some of these patients require lifelong, expensive, and potentially harmful medications to prevent serious complications, many others are at lower risk and are better treated with less expensive and less harmful medications, or by using "as-needed" therapy as flares occur. In addition, it has been clearly demonstrated that physicians do not prescribe medications in an efficient manner, and in fact may over-treat, when given the freedom to make choices. Therefore, stratifying veterans with HELP diseases, using biomarker-driven tools, into those at higher vs. lower risk offers great promise to significantly improve both the quality and efficiency of veteran care, and to minimize harm associated with more aggressive therapies. Developing tools and decision support systems to guide clinicians in personalizing medical decision- making for veterans with HELP diseases has particular application for the VA, because having a physician at every facility that subspecializes in each HELP disease is not feasible. However, to implement this "targeted" or "tailored" prevention approach to risk stratifying individuals for disease exacerbation and treatment, a clinician must know both the individual's baseline risk of disease complications and the probability that the individual would benefit (or suffer harm) from therapy. Having risk stratification tools developed and validated within the veteran population is an important first step towards realizing efficient patient-centered care for HELP diseases through the VA. Towards this goal, this CDA proposes to develop "targeted-prevention" prediction tools and decision support systems to facilitate the delivery of timely and cost-effective therapy for HELP diseases and to compare it to the current "symptom-driven" model used by clinicians. The proposal focuses on IBD as a model condition for HELP diseases. Methods: My research plan involves a series of sequential studies. In Aim 1 (year 1 and 2 of the award), I will compare the accuracy of regression models and machine learning approaches for predicting exacerbations of disease among veterans with IBD, conducting discrimination, calibration and re-classification analyses. In years 2-4, I will develop and use a microsimulation model to compare a biomarker-driven strategy based on the risk prediction model developed in Aim 1 to a symptom-driven disease management (usual care) strategy, as well as assessing a combination approach. The clinical and economic effects of the two strategies will then be compared (Aim 2). Finally, starting early in year 4, I will use the above work to develop and pilot test a personalized medical decision support tool (Aim 3). An IIR will also be submitted during year 3-4, to test the clinical intervention and the implementatio intervention, at multiple sites, in a Hybrid Type II implementation trial. ! PUBLIC HEALTH RELEVANCE: Veterans with "High Expense, Low Prevalence" (HELP) diseases such as rheumatoid arthritis, multiple sclerosis, and inflammatory bowel disease (IBD) often have exacerbations that can result in preventable mortality or major morbidity. Developing tools and decision support systems to guide clinicians in personalizing medical decision-making for veterans with HELP diseases has particular application for the VA, because having a physician at every facility that subspecializes in each HELP disease is not feasible. Having risk stratification tools developed and validated within the veteran population is an important first step towards realizing efficient patient-centered care for HELP diseases through the VA. Towards this goal, this CDA proposes to develop "targeted-prevention" prediction tools and decision support systems to facilitate the delivery of timely and cost-effective therapy for HELP diseases and to compare it to the current "symptom- driven" model used by clinicians. The proposal focuses on IBD as a model condition for HELP diseases.