Background

We developed and evaluated a prognostic prediction model that estimates osteoarthritis risk for use by patients and practitioners that is designed to be appropriate for integration into primary care health information technology systems. Osteoarthritis, a joint disorder characterized by pain and stiffness, causes significant morbidity among older Canadians. Because our prognostic prediction model for osteoarthritis risk uses data that are readily available in primary care settings, it supports targeting of interventions delivered as part of clinical practice that are aimed at risk reduction.

Methods

We used the CPCSSN (Canadian Primary Sentinel Surveillance Network) database, which contains aggregated electronic health information from a cohort of primary care practices, to develop and evaluate a prognostic prediction model to estimate 5-year osteoarthritis risk, addressing contextual challenges of data availability and missingness. We constructed a retrospective cohort of 383,117 eligible primary care patients who were included in the cohort if they had an encounter with their primary care practitioner between 1 January 2009 and 31 December 2010. Patients were excluded if they had a diagnosis of osteoarthritis prior to their first visit in this time period. Incident cases of osteoarthritis were observed. The model was constructed to predict incident osteoarthritis based on age, sex, BMI, previous leg injury, and osteoporosis. Evaluation of the model used internal 10-fold cross-validation; we argue that internal validation is particularly appropriate for a model that is to be integrated into the same context from which the data were derived.

Results

The resulting prediction model for 5-year risk of osteoarthritis diagnosis demonstrated state-of-the-art discrimination (estimated AUROC 0.84) and good calibration (assessed visually.) The model relies only on information that is readily available in Canadian primary care settings, and hence is appropriate for integration into Canadian primary care health information technology.

Conclusions

If the contextual challenges arising when using primary care electronic medical record data are appropriately addressed, highly discriminative models for osteoarthritis risk may be constructed using only data commonly available in primary care. Because the models are constructed from data in the same setting where the model is to be applied, internal validation provides strong evidence that the resulting model will perform well in its intended application.

中文翻译：

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开发和评估骨关节炎风险模型，以整合到初级保健健康信息技术中。

背景

我们开发并评估了一种预后预测模型，该模型可预测患者和从业人员使用骨关节炎的风险，该模型旨在适合集成到初级保健健康信息技术系统中。骨关节炎是一种以疼痛和僵硬为特征的关节疾病，在年长的加拿大人中引起大量发病。因为我们的骨关节炎风险预后预测模型使用的是在初级保健机构中容易获得的数据，所以它支持针对作为降低风险的临床实践的一部分而提供的干预措施。

方法

我们使用CPCSSN（加拿大初级前哨监视网络）数据库（该数据库包含来自一组初级保健实践的汇总电子健康信息）来开发和评估预后预测模型，以评估5年骨关节炎的风险，从而应对数据可用性和数据访问方面的背景挑战。失踪。我们建立了一项回顾性队列，对383,117名合格的基层医疗患者进行了回顾性研究，如果他们在2009年1月1日至2010年12月31日期间与他们的基层医疗从业者相遇，将被排除在外。在这段时间内造访。观察到骨关节炎的病例。该模型用于根据年龄，性别，BMI，先前的腿部损伤和骨质疏松症来预测骨关节炎的发生。使用内部10倍交叉验证对模型进行评估；我们认为，内部验证特别适合将模型集成到数据所源自的同一上下文中的模型。

结果

由此产生的5年骨关节炎诊断风险预测模型证明了最新的区分度（估计的AUROC 0.84）和良好的校准（通过视觉评估）。该模型仅依赖于加拿大初级保健机构中容易获得的信息，因此适合与加拿大基础医疗保健信息技术集成。

结论

如果适当地解决了在使用基层医疗电子病历数据时出现的情境挑战，则可以仅使用基层医疗中通常可用的数据来构建具有高度判别力的骨关节炎风险模型。因为模型是从与要应用模型的环境相同的数据中构造的，所以内部验证提供了有力的证据，表明所得模型在其预期应用中将表现良好。