Risk Stratification and Targeted Therapy for HELP Diseases in Veterans

退伍军人 HELP 疾病的风险分层和靶向治疗

• 批准号: 8396278
• 负责人: Akbar K Waljee
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8396278
• 关键词: Adverse effects; Award; Biological Markers; Calibration; Caring; Case Manager; Cessation of life; Charge; Classification; Clinical; Data; Data Set; Decision Making; Decision Support Systems; Discrimination; Disease; Disease Management; Disease remission; Economics; Flare; Freedom; Goals; Guidelines; Health Personnel; Healthcare; Hybrids; Individual; Inflammatory Bowel Diseases; Intervention; Lead; Low Prevalence; Machine Learning; Maintenance Therapy; Medical; Methods; Modeling; Monitor; Morbidity - disease rate; Multiple Sclerosis; Patient-Centered Care; Patients; Pharmaceutical Preparations; Physicians; Policies; Population; Prevention; Prevention approach; Probability; Process; Research; Rheumatoid Arthritis; Risk; Series; Simulate; Site; Specialist; Specific qualifier value; Stratification; Support System; Symptoms; System; Testing; Veterans; Work; aggressive therapy; base; career; cost; cost effective; disability; disorder risk; economic impact; effective therapy; experience; high risk; implementation trial; improved; mortality; novel; prevent; public health relevance; tool; treatment as usual; treatment planning

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.

描述(由申请人提供)： 目标：我的整个职业目标是使用新的方法开发和实施系统，以支持退伍军人管理局内外严重的、高成本的次专科疾病的有效管理。在这项CDA中，我计划使用炎症性肠病(IBD)作为模型条件，以实现以下具体目标：1)比较传统回归模型和机器学习模型预测IBD恶化的准确性和校准；2)开发和使用微观模拟模型，以比较使用当前指南、传统基于回归的模型和基于机器学习的模型做出患者决策的临床和经济影响；以及3)为患有IBD的退伍军人开发和试点个性化医疗决策支持工具。研究计划：患有类风湿性关节炎、多发性硬化症和炎症性肠病等“高费用、低发病率”(HELP)疾病的退伍军人往往病情恶化，可能导致可预防的死亡或重大发病率。虽然其中一些患者需要终生、昂贵和潜在有害的药物来预防严重的并发症，但许多其他患者的风险较低，可以使用更便宜、危害更小的药物进行更好的治疗，或者在出现耀斑时根据需要进行治疗。此外，已经清楚地表明，医生没有以有效的方式开出药物，事实上，当给予自由选择的时候，医生可能会过度治疗。因此，使用生物标记物驱动的工具，将患有HELP疾病的退伍军人分为风险较高与风险较低的退伍军人，这为显著提高退伍军人护理的质量和效率，并将与更激进的治疗相关的伤害降至最低提供了巨大的希望。开发工具和决策支持系统来指导临床医生为患有辅助性疾病的退伍军人制定个性化的医疗决策对退伍军人管理局具有特殊的应用价值，因为在每个机构都有一名医生专门研究每种辅助性疾病是不可行的。然而，为了实施这种“有针对性的”或“量身定做”的预防方法，对疾病恶化和治疗的个体进行风险分层，临床医生必须知道个体的疾病并发症的基线风险以及个体从治疗中受益(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退伍军人管理局实现有效的以患者为中心的疾病护理的重要第一步。为实现这一目标，CDA建议开发“针对性预防”预测工具和决策支持系统，以促进及时、经济有效地治疗HELP疾病，并将其与目前临床医生使用的“症状驱动”模式进行比较。该提案的重点是将IBD作为HELP疾病的模型条件。方法：我的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)，我将比较回归模型和机器学习方法在预测患有IBD的退伍军人中疾病恶化的准确性，进行区分、校准和重新分类分析。在2-4年级，我会发展 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记物驱动策略与症状驱动的疾病管理(通常护理)策略，以及评估组合方法。然后将比较这两种策略的临床和经济效果(目标2)。最后，从第4年开始，我将使用上述工作开发和试点测试一个个性化的医疗决策支持工具(Aim 3)。还将在3-4年间提交一份IIR，以测试临床干预和在多个地点实施第二类混合干预的实施情况。好了！ 公共卫生相关性： 患有类风湿性关节炎、多发性硬化症和炎症性肠病(IBD)等“高费用、低发病率”(HELP)疾病的退伍军人通常病情恶化，可能导致可预防的死亡或重大发病率。开发工具和决策支持系统来指导临床医生为患有辅助性疾病的退伍军人制定个性化的医疗决策对退伍军人管理局具有特殊的应用价值，因为在每个机构都有一名医生专门研究每种辅助性疾病是不可行的。在退伍军人群体中开发和验证风险分层工具是通过退伍军人管理局实现有效的以患者为中心的疾病护理的重要第一步。为实现这一目标，CDA建议开发“针对性预防”预测工具和决策支持系统，以促进及时、经济有效地治疗HELP疾病，并将其与目前临床医生使用的“症状驱动”模式进行比较。该提案的重点是将IBD作为HELP疾病的模型条件。

项目成果

The increasing importance of quality measures for trainees.

质量措施对学员的重要性日益增加。

• DOI: 10.1053/j.gastro.2014.08.027
• 发表时间: 2014
• 期刊: Gastroenterology
• 影响因子: 29.4
• 作者: Saini,SameerD;Waljee,AkbarK;Schoenfeld,Philip;Kerr,EveA;Vijan,Sandeep
• 通讯作者: Vijan,Sandeep

How Efficacious Are Patient Education Interventions to Improve Bowel Preparation for Colonoscopy? A Systematic Review.

• DOI: 10.1371/journal.pone.0164442
• 发表时间: 2016
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Kurlander JE;Sondhi AR;Waljee AK;Menees SB;Connell CM;Schoenfeld PS;Saini SD
• 通讯作者: Saini SD

Therapeutic delays lead to worse survival among patients with hepatocellular carcinoma.

治疗延迟导致肝细胞癌患者的生存率较差。

• DOI: 10.6004/jnccn.2013.0131
• 发表时间: 2013-09-01
• 期刊: Journal of the National Comprehensive Cancer Network : JNCCN
• 作者: Singal AG;Waljee AK;Patel N;Chen EY;Tiro JA;Marrero JA;Yopp AC
• 通讯作者: Yopp AC

Access to Subspecialty Care And Survival Among Patients With Liver Disease.

• DOI: 10.1038/ajg.2016.96
• 发表时间: 2016-06
• 期刊: The American journal of gastroenterology
• 作者: Mellinger JL;Moser S;Welsh DE;Yosef MT;Van T;McCurdy H;Rakoski MO;Moseley RH;Glass L;Waljee AK;Volk ML;Sales A;Su GL
• 通讯作者: Su GL

Point-counterpoint: Are we overtreating patients with mild ulcerative colitis?

点对点：我们是否过度治疗轻度溃疡性结肠炎患者？

• DOI: 10.1016/j.crohns.2013.07.003
• 发表时间: 2014
• 期刊: Journal of Crohn's & colitis
• 作者: Waljee,AkbarK;Stidham,RyanW;Higgins,PeterDR;Vijan,Sandeep;Saini,SameerD
• 通讯作者: Saini,SameerD