Biostatistics Unit, Institut d'Investigació Biomèdica de Bellvitge, L'Hospitalet de Llobregat, Spain; Department of Clinical Sciences, Universitat de Barcelona.
IDIAP Jordi Gol Primary Care Research Institute; Ambit Barcelona, Primary Care Department, Institut Catala de la Salut; GREMPAL Research Group.
Bone. 2022 Sep;162:116469. doi: 10.1016/j.bone.2022.116469. Epub 2022 Jun 9.
We aimed to develop and validate a fracture risk algorithm for the automatic identification of subjects at high risk of imminent and long-term fracture risk.
RESEARCH, DESIGN, AND METHODS: A cohort of subjects aged 50-85, between 2007 and 2017, was extracted from the Catalan information system for the development of research in primary care database (SIDIAP). Participants were followed until the earliest of death, transfer out, fracture, or 12/31/2017. Potential risk factors were obtained based on the existing literature. Cox regression was used to model 1 and 5-year risk of hip and major fracture. The original cohort was randomly split in 80:20 for development and internal validation purposes respectively. External validation was explored in a cohort extracted from the Spanish database for pharmaco-epidemiological research in primary care.
A total of 1.76 million people were included from SIDIAP (50.7 % women with mean age of 65.4 years). Hip and major fracture incidence rates were 3.57 [95%CI 3.53 to 3.60] and 11.61 [95%CI 11.54 to 11.68] per 1000 person-years, respectively. The derived model included 19 risk factors. Internal validity showed good results on calibration and discrimination. The 1-year C-statistic for hip and major fracture were 0.851 (95%CI 0.853 to 0.864), and 0.717 (95%CI 0.742 to 0.749) respectively. The 5-year C-statistic for hip and major fracture were 0.849 (95%CI 0.847 to 0.852) and 0.724 (95%CI 0.721 to 0.727) respectively. External validation showed good performance for hip and major fracture risk prediction.
We have developed and validated a clinical prediction tool for 1- and 5-year hip and major osteoporotic fracture risks using electronic primary care data. The proposed algorithm can be automatically estimated at the population level using the available primary care records. Future work is needed on the cost-effectiveness of its use for population-based screening and targeted prevention of osteoporotic fractures.
我们旨在开发和验证一种骨折风险算法，以自动识别即将发生和长期骨折风险高的患者。
从 2007 年至 2017 年期间的加泰罗尼亚初级保健研究开发信息系统（SIDIAP）中提取了一组年龄在 50-85 岁之间的患者队列。参与者随访至最早死亡、转出、骨折或 2017 年 12 月 31 日。根据现有文献获得潜在风险因素。使用 Cox 回归对髋部和主要骨折 1 年和 5 年的风险进行建模。原始队列分别以 80:20 的比例随机分为开发和内部验证组。在从西班牙初级保健药物流行病学研究数据库中提取的队列中进行了外部验证。
SIDIAP 共纳入 176 万人（50.7%为女性，平均年龄为 65.4 岁）。髋部和主要骨折的发生率分别为 3.57[95%CI 3.53-3.60]和 11.61[95%CI 11.54-11.68]每 1000 人年。所推导的模型包含 19 个风险因素。内部验证显示校准和区分效果良好。髋部和主要骨折的 1 年 C 统计量分别为 0.851（95%CI 0.853-0.864）和 0.717（95%CI 0.742-0.749）。髋部和主要骨折的 5 年 C 统计量分别为 0.849（95%CI 0.847-0.852）和 0.724（95%CI 0.721-0.727）。外部验证显示髋部和主要骨折风险预测性能良好。
我们使用电子初级保健数据开发并验证了一种用于预测 1 年和 5 年髋部和主要骨质疏松性骨折风险的临床预测工具。该算法可以使用现有的初级保健记录自动估算人群水平的骨折风险。未来需要进一步研究其在人群筛查和骨质疏松性骨折针对性预防方面的成本效益。
