Using Medicare Claims to Advance Our Understanding of Guillain-Barre Syndrome

利用医疗保险索赔来加深我们对格林-巴利综合症的了解

基本信息

批准号：
10303232
负责人：
Rebecca Traub
金额：
$ 42.76万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-27 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10303232
关键词：
Adult Affect Algorithms Autoimmune Diseases Biological Case Study Characteristics Clinical Clinical Data Code Data Decision Making Demographic Factors Diagnosis Diagnostic tests Disabled Persons Drug Prescriptions Elderly Electronic Health Record Face Fee-for-Service Plans Future Guillain Barré Syndrome Health Health Services Research Hospitals Immune system Incidence Individual Infection Inflammatory Institutes Intravenous Immunoglobulins Literature Measurement Measures Mechanical ventilation Medical Medicare Medicare claim Methods Mission Modeling Morbidity - disease rate Muscle Weakness Outcome Paralysed Paresthesia Patients Peripheral Nerves Peripheral Nervous System Diseases Plasma Exchange Plasmapheresis Population Predictive Value Procedures Public Health Records Recovery Regression Analysis Research Research Personnel Respiratory Diaphragm Respiratory Failure Risk Risk Factors Role Sampling Sensitivity and Specificity Severities Stimulus Stroke Symptoms Testing Treatment outcome Variant base care delivery care outcomes clinical care clinical outcome measures clinical risk cohort data warehouse experience high risk high risk population innovation mortality nervous system disorder patient subsets population based population health rare condition tool

项目摘要

PROJECT SUMMARY Guillain-Barré Syndrome (GBS) is a rare autoimmune disorder (annual incidence 0.4 – 3.3 cases per 100,000) in which an infection or other stimulus causes the body's immune system to mistakenly attack the peripheral nerves, resulting in muscle weakness, paresthesias, respiratory failure, and paralysis. With existing treatments, including intravenous immunoglobulin and plasmapheresis, most individuals experience a significant degree of recovery over weeks, months, or years. However, up to 14% of GBS patients remain permanently disabled and the 1-year mortality rate is estimated to range from 3 – 20%. The rarity of GBS makes it hard to study. Most of the literature is based on case reports and small samples, which limit generalizability and yield imprecise estimates. Consequently, much remains unknown about GBS risk factors and disparities in GBS testing, treatment, and outcomes across patient subgroups. In this context, clinicians confronted with an evolving case of GBS must make decisions and develop prognoses in the absence of robust population-based data. Thus, there is considerable value in leveraging large-scale administrative data to further our understanding of GBS. We will use electronic health records matched to Medicare claims data to evaluate the accuracy of different algorithms for identifying GBS in Medicare claims (Aim 1). Then, we will use the preferred algorithm to identify GBS cases in Medicare claims and follow those individuals longitudinally before and after their GBS diagnosis. Next, we will fully characterize GBS risk factors in the Medicare population and compare GBS patients to non- GBS patients using a multivariable regression model to predict the likelihood of GBS onset as a function of patients' clinical and demographic characteristics (Aim 2). Finally, using multivariable regression analysis, we will model GBS diagnostic testing, treatment, and outcomes as a function of patients' clinical and demographic factors, while adjusting for hospital fixed effects to identify disparities across patient subgroups within and between facilities (Aim 3). The proposed study is innovative because it will be the first to validate an algorithm to identify true GBS cases in adults using claims data, will generate the largest and most contemporary GBS cohort in the U.S., and will be the first to use Medicare claims to characterize clinical and demographic risk factors for GBS onset and identify disparities in GBS care delivery and outcomes. This is significant because it will further our understanding of which individuals are at greatest risk of developing GBS, which GBS patients are most likely to face barriers in access to GBS diagnosis and treatment, and which GBS patients are most likely to experience worse GBS outcomes. We also expect to identify clinical conditions previously unknown to be associated with GBS onset. Ultimately, our results will provide an important tool for future population health research focused on GBS, and also have the potential to inform future research into biological mechanisms of GBS onset, guide improvements in clinical care delivery (especially for high-risk populations), and facilitate efforts to find new and better treatments for this rare, but extremely serious condition.

项目概要格林-巴利综合征 (GBS) 是一种罕见的自身免疫性疾病（每年发病率为每 100,000 人中 0.4 – 3.3 例）其中感染或其他刺激导致身体的免疫系统错误地攻击外周神经，导致肌肉无力、感觉异常、呼吸衰竭和瘫痪。包括静脉注射免疫球蛋白和血浆置换术，大多数人都会经历显着程度的然而，高达 14% 的 GBS 患者仍处于永久性残疾状态。 GBS 的 1 年死亡率估计为 3 – 20%。文献基于病例报告和小样本，这限制了普遍性且结果不精确据估计，关于 GBS 风险因素和 GBS 检测差异仍有很多未知之处，在这种情况下，新来者面临着不断变化的病例。 GBS 必须在缺乏可靠的基于人群的数据的情况下做出决策并制定预后。利用大规模行政数据来加深我们对 GBS 的理解具有相当大的价值。我们将使用与医疗保险索赔数据相匹配的电子健康记录来评估不同数据的准确性识别医疗保险索赔中的 GBS 的算法（目标 1）然后，我们将使用首选算法来识别。医疗保险索赔中的 GBS 病例，并在 GBS 诊断前后对这些人进行纵向跟踪。接下来，我们将全面描述医疗保险人群中的 GBS 危险因素，并将 GBS 患者与非 GBS 患者进行比较。 GBS 患者使用多变量回归模型来预测 GBS 发病的可能性，作为以下函数：最后，我们使用多变量回归分析来了解患者的临床和人口特征（目标 2）。将根据患者的临床和人口特征对 GBS 诊断测试、治疗和结果进行建模因素，同时调整医院固定效应，以确定内部和外部患者亚组之间的差异所提出的研究具有创新性，因为它将是第一个验证算法的研究。使用索赔数据识别成人中真正的 GBS 病例，将产生最大、最现代的 GBS 美国的队列，并将是第一个使用医疗保险索赔来描述临床和人口风险的队列 GBS 发病的因素并确定 GBS 护理服务和结果的差异，这一点很重要，因为它。将进一步了解哪些人患 GBS 的风险最大，哪些 GBS 患者最有可能在获得 GBS 诊断和治疗方面面临障碍，以及哪些 GBS 患者最容易面临障碍我们还期望找出以前未知的临床状况。最终，我们的结果将为未来人口健康提供重要工具。研究重点是 GBS，也有可能为未来的生物学机制研究提供信息 GBS 发病，指导临床护理服务的改进（尤其是高危人群），并促进努力寻找新的更好的治疗方法来治疗这种罕见但极其严重的疾病。