Section of Endocrinology, Diabetes, and Metabolism, Department of Medicine, The University of Chicago, 5841 S Maryland Ave, MC 1027, Chicago, IL, 60637, USA.
Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA.
Osteoporos Int. 2024 Dec;35(12):2117-2126. doi: 10.1007/s00198-024-07221-2. Epub 2024 Aug 15.
Information in the electronic health record (EHR), such as diagnoses, vital signs, utilization, medications, and laboratory values, may predict fractures well without the need to verbally ascertain risk factors. In our study, as a proof of concept, we developed and internally validated a fracture risk calculator using only information in the EHR.
Fracture risk calculators, such as the Fracture Risk Assessment Tool, or FRAX, typically lie outside the clinician workflow. Conversely, the electronic health record (EHR) is at the center of the clinical workflow, and many variables in the EHR could predict fractures without having to verbally ascertain FRAX risk factors. We sought to evaluate the utility of EHR variables to predict fractures and, as a proof of concept, to create an EHR-based fracture risk model.
Routine clinical data from 24,189 subjects presenting to primary care from 2010 to 2018 was utilized. Major osteoporotic fractures (MOFs) were captured by physician diagnosis codes. Data was split into training (n = 18,141) and test sets (n = 6048). We fit Cox regression models for candidate risk factors in the training set, and then created a global model using a backward stepwise approach. We then applied the model to the test set and compared the discrimination and calibration to FRAX.
We found variables related to vital signs, utilization, diagnoses, medications, and laboratory values to be associated with incident MOF. Our final model included 19 variables, including age, BMI, Parkinson's disease, chronic kidney disease, and albumin levels. When applied to the test set, we found the discrimination (AUC 0.73 vs. 0.70, p = 0.08) and calibration were comparable to FRAX.
Routinely collected data in EHR systems can generate adequate fracture predictions without the need to verbally ascertain fracture risk factors. In the future, this could allow for automated fracture prediction at the point of care to improve osteoporosis screening and treatment rates.
骨折风险计算器，如骨折风险评估工具（FRAX），通常不在临床医生的工作流程之外。相反，电子健康记录（EHR）是临床工作流程的中心，EHR 中的许多变量无需口头确定 FRAX 风险因素即可预测骨折。我们旨在评估 EHR 变量预测骨折的效用，并作为概念验证，创建基于 EHR 的骨折风险模型。
利用 2010 年至 2018 年期间在初级保健就诊的 24189 名患者的常规临床数据。主要骨质疏松性骨折（MOF）通过医生诊断代码捕获。数据分为训练集（n=18141）和测试集（n=6048）。我们在训练集中拟合候选风险因素的 Cox 回归模型，然后使用向后逐步方法创建一个全局模型。然后将该模型应用于测试集，并将其与 FRAX 进行比较，以评估区分度和校准度。
我们发现与生命体征、使用情况、诊断、药物和实验室值相关的变量与 MOF 事件相关。我们的最终模型包括 19 个变量，包括年龄、BMI、帕金森病、慢性肾脏病和白蛋白水平。当应用于测试集时，我们发现区分度（AUC 为 0.73 比 0.70，p=0.08）和校准与 FRAX 相当。
EHR 系统中常规收集的数据可以在无需口头确定骨折风险因素的情况下提供足够的骨折预测。将来，这可以允许在护理点自动进行骨折预测，以提高骨质疏松症筛查和治疗率。
