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

描述：在长期入住疗养院的居民中，严重的骨质疏松性骨折是常见的、病态的、昂贵的。尽管这个问题很重要，但目前还没有指导方针来筛选养老院居民的骨质疏松症，目前还不清楚哪些骨质疏松症药物可以预防长期住院居民的骨折。为了解决这些知识的差距，我们提出了以下具体目标：1）开发和验证一种预测工具，使用福尔斯和骨折的临床风险因素，估计长期入住疗养院居民髋部骨折的2年绝对风险和严重骨质疏松性骨折（髋部、肱骨和手腕合并）的1年绝对风险; 2）确定骨质疏松症药物（即，二膦酸盐、降钙素、雌激素、雷洛昔芬和特立哌酮）降低长期入住疗养院居民的髋部和主要骨质疏松性骨折的发生率;和3）确定骨质疏松药物降低长期入住居民的髋部和主要骨质疏松性骨折发生率20%的临床风险阈值。本项目将利用现有数据库，该数据库先前已将Medicare Part A和B的索赔数据与药房数据（Medicare Part D）、临床特征（最小数据集）和机构级特征（OSCAR）相关联。使用该数据库，我们将对2006年至2011年期间参加医疗保险按服务收费计划且住院时间为90天的所有美国养老院居民（每年超过70万居民）进行前瞻性分析。对于具体目标1，预测工具将完全根据所有美国长期居住居民的临床信息开发，例如认知和功能状态、非骨质疏松症药物使用和近期福尔斯病史。预测工具将解释养老院居民的高死亡率。对于具体目标2a，我们将通过比较骨质疏松症药物“新使用者”与“非使用者”的骨折发生率，确定骨质疏松症药物是否降低长期住院居民髋部和主要骨质疏松性骨折的发生率，并与倾向评分相匹配。对于具体目标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。该研究团队在药物流行病学，老年病学，医疗保险索赔数据和最小数据集特征分析方面具有完成该项目所需的经验。我们的研究结果将是非常重要的，因为他们将提供第一个指导筛选和治疗骨质疏松症的养老院设置。我们预计，我们的研究结果将很容易为供应商实施，因为骨折预测工具将包括临床信息，已经为所有美国养老院居民。从这项研究中获得的知识最终可能会降低疗养院中严重骨质疏松性骨折的发生率，从而降低发病率和医疗保健成本。

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.