A Clinical Prediction Tool to Guide Treatment of Osteoporosis in the Nursing Home

描述：严重的骨质疏松性骨折在长期入住疗养院的居民中是常见的、病态的和昂贵的。尽管这个问题很重要，但目前还没有对疗养院居民进行骨质疏松症筛查的指导方针，也不清楚哪些骨质疏松症药物可以防止长期居住的居民发生骨折。为了解决这些知识差距，我们提出了以下具体目标：1)开发和验证使用跌倒和骨折临床危险因素的预测工具，该工具将估计长期入住养老院居民髋部骨折的2年绝对风险和主要骨质疏松性骨折(髋部、肱骨和手腕合计)的1年绝对风险；2)确定骨质疏松症药物(即双膦酸盐、降钙素、雌激素、雷洛昔芬和特瑞帕特)是否降低长期疗养院居民髋部和主要骨质疏松性骨折的发生率；3)确定长期服用骨质疏松性药物使髋部和主要骨质疏松性骨折的发生率降低20%的临床风险阈值。该项目将利用一个现有数据库，该数据库先前已将Medicare Part A和B中的索赔数据与药房数据(Medicare Part D)、临床特征(最小数据集)和设施级别特征(OSCAR)相关联。使用这个数据库，我们将对2006-2011年间所有参加联邦医疗保险收费服务计划、住院天数为90天的美国疗养院居民进行前瞻性分析(&gt；每年70万居民)。对于具体目标1，预测工具将完全根据所有长期居住在美国的居民可用的临床信息开发，例如认知和功能状况、非骨质疏松症药物使用情况以及最近的跌倒史。这些预测工具将解释疗养院居民的高死亡率。对于特定的目标2a，我们将通过比较骨质疏松症药物的“新使用者”和“非使用者”的骨折发生率，并与倾向评分相匹配，来确定骨质疏松症药物是否降低长期居民的髋部和主要骨质疏松性骨折的发生率。对于特定的目标2b，我们将根据骨折预测工具估计的基线骨折风险，通过检查骨质疏松症药物的疗效来确定药物干预的阈值。研究团队在药物流行病学、老年病学以及分析医疗保险索赔数据和完成该项目所需的最小数据集特征方面具有经验。我们的发现将具有非常重要的意义，因为它们将为养老院环境中骨质疏松症的筛查和治疗提供第一个指导。我们预计，我们的发现将易于提供商实施，因为骨折预测工具将包含所有美国疗养院居民已经可以获得的临床信息。从这项研究中获得的知识最终可能会降低疗养院环境中主要骨质疏松性骨折的发生率，从而降低发病率和医疗费用。

DESCRIPTION: Major osteoporotic fractures are common, morbid, and costly among long-stay nursing home residents. Despite the importance of this problem, there are no guidelines to screen nursing home residents for osteoporosis, and it is unclear which osteoporosis medications prevent fractures in long-stay residents. To address these gaps of knowledge we propose the following specific aims: 1) develop and validate a prediction tool using clinical risk factors for falls and fracture that will estimate the 2 year absolute risk of hip fracture and the year absolute risk of major osteoporotic fracture (hip, humerus, and wrist combined) in long-stay nursing home residents; 2) determine whether osteoporosis medications (i.e., bisphosphonates, calcitonin, estrogen, raloxifene, and teriparatide) reduce the incidence of hip and major osteoporotic fracture in long-stay nursing home residents; and 3) determine the clinical thresholds of risk at which osteoporosis medications reduce the incidence of hip and major osteoporotic fracture by e 20% in long-stay residents. This project will leverage an existing database that has previously linked claims data from Medicare Parts A and B with pharmacy data (Medicare Part D), clinical characteristics (Minimum Data Set), and facility level characteristics (OSCAR). Using this database we will conduct a prospective analysis on all U.S. nursing home residents enrolled in a Medicare fee-for-service plan and with e 90 day length of stay between the years 2006-2011 (>700,000 residents annually). For specific aim 1, the prediction tools will be developed entirely from clinical information that is available for all U.S long-stay residents, such as cognitive and functional status, non-osteoporosis medication use, and recent history of falls. The prediction tools will account for the high mortality in nursing home residents. For specific aim 2a, we will determine whether osteoporosis medications reduce the incidence of hip and major osteoporotic fracture in long-stay residents by comparing the incidence of fracture among "new users" of an osteoporosis medication with the incidence of fracture in "non-users," matched by propensity scores. For specific aim 2b, we will determine a threshold for pharmacologic intervention by examining the efficacy of osteoporosis drugs according to baseline fracture risk, as estimated by the fracture prediction tool. The research team has experience in pharmacoepidemiology, geriatrics, and analysis of Medicare claims data and Minimum Data Set characteristics necessary to complete this project. Our findings will be highly significant as they will provide the first guidance on screening and treatment of osteoporosis in the nursing home setting. We anticipate that our findings will be easy for providers to implement because the fracture prediction tools will be comprised of clinical information that is already available for all U.S. nursing home residents. Knowledge gained from this study could ultimately result in a decreased rate of major osteoporotic fractures in the nursing home setting, with a subsequent reduction in morbidity and health care costs.