Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec, QC, Canada.
CHU de Québec - Université Laval Research Centre, Quebec, QC, Canada.
J Bone Miner Res. 2021 Dec;36(12):2329-2342. doi: 10.1002/jbmr.4438. Epub 2021 Sep 22.
In Canada and other countries, osteoporosis is monitored as part of chronic disease population surveillance programs. Although fractures are the principal manifestation of osteoporosis, very few algorithms are available to identify individuals at high risk of osteoporotic fractures in current surveillance systems. The objective of this study was to derive and validate predictive models to accurately identify individuals at high risk of osteoporotic fracture using information available in healthcare administrative data. More than 270,000 men and women aged ≥66 years were randomly selected from the Quebec Integrated Chronic Disease Surveillance System. Selected individuals were followed between fiscal years 2006-2007 and 2015-2016. Models were constructed for prediction of hip/femur and major osteoporotic fractures for follow-up periods of 5 and 10 years. A total of 62 potential predictors measurable in healthcare administrative databases were identified. Predictor selection was performed using a manual backward algorithm. The predictive performance of the final models was assessed using measures of discrimination, calibration, and overall performance. Between 20 and 25 predictors were retained in the final prediction models (eg, age, sex, social deprivation index, most of the major and minor risk factors for osteoporosis, diabetes, Parkinson's disease, cognitive impairment, anemia, anxio-depressive disorders). Discrimination of the final models was higher for the prediction of hip/femur fracture than major osteoporotic fracture and higher for prediction for a 5-year than a 10-year period (hip/femur fracture for 5 years: c-index = 0.77; major osteoporotic fracture for 5 years: c-index = 0.71; hip/femur fracture for 10 years: c-index = 0.73; major osteoporotic fracture for 10 years: c-index = 0.68). The predicted probabilities globally agreed with the observed probabilities. In conclusion, the derived models had adequate predictive performance in internal validation. As a final step, these models should be validated in an external cohort and used to develop indicators for surveillance of osteoporosis. © 2021 American Society for Bone and Mineral Research (ASBMR).
在加拿大和其他国家，骨质疏松症作为慢性病人群监测计划的一部分进行监测。虽然骨折是骨质疏松症的主要表现，但目前的监测系统中很少有算法可以识别骨质疏松性骨折高危个体。本研究旨在利用医疗保健管理数据中可用的信息，开发和验证能够准确识别骨质疏松性骨折高危个体的预测模型。从魁北克综合慢性病监测系统中随机选择了超过 270,000 名年龄≥66 岁的男性和女性。在 2006-2007 财年至 2015-2016 财年期间对选定个体进行随访。为预测 5 年和 10 年的髋部/股骨和主要骨质疏松性骨折，构建了模型。确定了 62 个可在医疗保健管理数据库中测量的潜在预测因子。使用手动向后算法进行预测因子选择。使用区分度、校准和整体性能评估最终模型的预测性能。最终预测模型中保留了 20-25 个预测因子（例如，年龄、性别、社会剥夺指数、大多数骨质疏松症的主要和次要危险因素、糖尿病、帕金森病、认知障碍、贫血、焦虑抑郁障碍）。最终模型对髋部/股骨骨折的预测区分度高于对主要骨质疏松性骨折的预测，对 5 年预测的区分度高于对 10 年预测的区分度（5 年髋部/股骨骨折：c 指数=0.77；5 年主要骨质疏松性骨折：c 指数=0.71；10 年髋部/股骨骨折：c 指数=0.73；10 年主要骨质疏松性骨折：c 指数=0.68）。预测概率与观察概率总体一致。总之，在内部验证中，所开发的模型具有足够的预测性能。作为最后一步，这些模型应在外部队列中进行验证，并用于开发骨质疏松症监测指标。© 2021 美国骨骼与矿物质研究协会（ASBMR）。
