PATIENT OUTCOME IN RA WITH EARLY ANTIRHEUMATIC THERAPY

早期抗风湿治疗的 RA 患者结局

• 批准号: 6583185
• 负责人: James F Fries
• 金额: $ 21.52万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-04-08 至 2006-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6583185
• 关键词: antiarthritic agent; antirheumatic agents; arthritis therapy; clinical research; combination chemotherapy; cost effectiveness; drug administration rate /duration; drug adverse effect; drug screening /evaluation; functional ability; human data; human subject; human therapy evaluation; longitudinal human study; nonsteroidal antiinflammatory agent; outcomes research; patient care planning; patient oriented research; quality of life; questionnaires; rheumatoid arthritis; skeletal pharmacology; statistics /biometry

The medical management of rheumatoid arthritis (RA) depends on a number of medications, applied sequentially either alone or in combination. As a result, future improvement in management will come not only by use of the most effective drugs but also the moist effective sequences. This project will identify optimal therapeutic strategies for patients with RA, strategies which use the best drugs in the best order, by breaking the disease course into multiple discrete, specific treatment courses called therapeutic segments. A therapeutic segment starts with the date of onset of treatment with a drug or drug combination and ends on the date of a change in the drug treatment whether due to discontinuation, deletion of an agent or addition of an agent. A typical RA patient may have 5 to 15 therapeutic segments over the disease course. Optimal treatment will be evaluated in terms of area under the curve (AUC) disability, pain, global health, cumulative toxicity, and costs. We will complete therapeutic segment outcomes for each of the five outcomes, for each major DMARD and DMARD combination, and will identify patient and group variables which affect value of these outcomes. We will first compare alternative therapeutic strategies employing all DMARDs, without regard to sequence, for long-term outcomes using multivariate regression models. We will then evaluate the impact of RA patient characteristics on outcomes using regression analyses and classification trees and consider the effects of therapeutic segments and of the sequence of these segments. Characteristics to be evaluated include: sociodemographic variables, disease duration, prior response to treatment, prior DMARD therapy, and prior toxicity. Our next task will be to evaluate the sensitivity of the sequences and the outcomes to variation in individual patient characteristics. This work will result in statistical models for the individual outcomes (AUC disability, pain, global, health toxicity, and costs). Finally, we will construct cost- effectiveness and effectiveness/toxicity ratios for each segment and for each sequence. Results will encourage clinical decision to be made on the basis of effectiveness, cost/effectiveness, and effectiveness/toxicity which may be individualized for the particular patient. This project builds upon large, long-term, high quality data sets, and techniques for estimation of effectiveness, toxicity, and costs developed by this team, in order to provide unique and important data to guide therapeutic choices toward improved outcomes for RA patients. This research will provide knowledge of the importance and magnitude of cumulative (AUC) outcomes, the drug sequences best employed for outcome improvement, the necessity of considering individual patient variability, and will provide clinicians with better information to assist them in decision-making regarding treatment choices for their RA patients.

类风湿性关节炎（RA）的医疗管理取决于许多药物，单独或联合顺序应用。因此，未来的管理改进将不仅通过使用最有效的药物，而且还通过潮湿的有效顺序。该项目将为RA患者确定最佳治疗策略，通过将疾病过程分解为多个离散的特定治疗过程（称为治疗部分），以最佳顺序使用最佳药物的策略。治疗阶段从药物或药物组合治疗开始的日期开始，到药物治疗改变的日期结束，无论是由于停药、删除药物还是添加药物。典型的RA患者在病程中可能有5至15个治疗段。最佳治疗将根据曲线下面积（AUC）残疾、疼痛、总体健康、累积毒性和成本进行评价。我们将完成五种结局中每一种结局的治疗部分结局，以及每种主要DMARD和DMARD组合，并将确定影响这些结局价值的患者和组变量。我们将首先使用多变量回归模型比较采用所有DMARD的替代治疗策略（不考虑序列）的长期结局。然后，我们将使用回归分析和分类树评估RA患者特征对结局的影响，并考虑治疗段和这些段的顺序的影响。待评价的特征包括：社会人口统计学变量、疾病持续时间、既往治疗反应、既往DMARD治疗和既往毒性。我们的下一个任务将是评估序列的敏感性和个体患者特征变化的结果。这项工作将产生个体结局的统计模型（AUC残疾、疼痛、总体、健康毒性和成本）。最后，我们将为每个部分和每个序列构建成本效益和有效性/毒性比。结果将鼓励根据有效性、成本/有效性和有效性/毒性做出临床决策，这些决策可能针对特定患者进行个体化。该项目建立在大型，长期，高质量的数据集和该团队开发的有效性，毒性和成本估计技术的基础上，以提供独特而重要的数据来指导治疗选择，以改善RA患者的结局。这项研究将提供累积（AUC）结果的重要性和幅度的知识，最好用于改善结果的药物序列，考虑个体患者变异性的必要性，并将为临床医生提供更好的信息，以帮助他们做出关于RA患者治疗选择的决策。