Department of Medicine (C5121), University of Manitoba, 409 Tache Avenue, Winnipeg, Manitoba, R2H 2A6, Canada.
Osteoporos Int. 2011 Jan;22(1):37-46. doi: 10.1007/s00198-010-1225-2. Epub 2010 May 11.
A simple case definition for osteoporosis case diagnosis is feasible based upon administrative health data. This may facilitate implementation of a population-based osteoporosis surveillance program, providing information that could help to inform and guide screening, prevention, and treatment resources.
Our aim was to construct and validate a simplified algorithm for osteoporosis case ascertainment from administrative databases that would be suitable for disease surveillance.
Multiple classification rules were applied to different sets of hospital diagnosis, physician claims diagnosis, and prescription drug variables from Manitoba, Canada. Algorithms were validated against results from a regional bone mineral density testing program that identified bone mineral density (BMD) measurements in 4,015 women age 50 years and older with at least one BMD test between April 1, 2000 and March 31, 2001.
Sensitivity as high as 93.3% was achieved with 3 years of data. Specificity ranged from 50.8% to 91.4% overall, and from 81.2% to 99.1% for discriminating osteoporotic from normal BMD. Sensitivity and overall accuracy were generally lower for algorithms based on diagnosis codes alone than for algorithms that included osteoporosis prescriptions. In the subgroup without prior osteoporotic fractures or chronic corticosteroid use, one simple algorithm (one hospital diagnosis, physician claims diagnosis, or osteoporosis prescription within 1 year) gave accuracy measures exceeding 90% for discriminating osteoporosis from normal BMD across a wide range of disease prevalence.
A relatively simple case definition for osteoporosis surveillance based upon administrative health data can achieve an acceptable level of sensitivity, specificity, and accuracy. Performance is enhanced when the case definition includes osteoporosis medication use in the formulation.
基于管理健康数据，骨质疏松症病例诊断的简单病例定义是可行的。这可能有助于实施基于人群的骨质疏松症监测计划，提供有助于告知和指导筛查、预防和治疗资源的信息。
我们的目的是构建和验证一种适用于疾病监测的从管理数据库中确定骨质疏松症病例的简化算法。
从加拿大马尼托巴省的医院诊断、医生索赔诊断和处方药变量中应用了多种分类规则。该算法针对区域性骨密度测试计划的结果进行了验证，该计划确定了 4015 名年龄在 50 岁及以上的女性的骨密度（BMD）测量值，这些女性在 2000 年 4 月 1 日至 2001 年 3 月 31 日期间至少进行了一次 BMD 测试。
使用 3 年的数据，灵敏度高达 93.3%。总体特异性为 50.8%至 91.4%，区分骨质疏松症和正常 BMD 的特异性为 81.2%至 99.1%。仅基于诊断代码的算法的灵敏度和总体准确性通常低于包含骨质疏松症处方的算法。在没有先前骨质疏松性骨折或慢性皮质类固醇使用史的亚组中，一种简单的算法（一年内一次医院诊断、医生索赔诊断或骨质疏松症处方）在广泛的疾病流行率范围内，用于区分骨质疏松症与正常 BMD 的准确性测量值超过 90%。
基于管理健康数据的骨质疏松症监测的相对简单的病例定义可以达到可接受的灵敏度、特异性和准确性水平。当病例定义在制定中包括骨质疏松症药物使用时，性能会得到提高。
