Division of Orthopaedic Surgery, University of Arizona College of Medicine - Phoenix, Phoenix, AZ, USA.
Department of Surgery, University of Toronto, Toronto, ON, Canada.
Medicine (Baltimore). 2024 May 31;103(22):e38238. doi: 10.1097/MD.0000000000038238.
Analyses using population-based health administrative data can return erroneous results if case identification is inaccurate ("misclassification bias"). An acetabular fracture (AF) prediction model using administrative data decreased misclassification bias compared to identifying AFs using diagnostic codes. This study measured the accuracy of this AF prediction model in another hospital. We calculated AF probability in all hospitalizations in the validation hospital between 2015 and 2020. A random sample of 1000 patients stratified by expected AF probability was selected. Patient imaging studies were reviewed to determine true AF status. The validation population included 1000 people. The AF prediction model was very discriminative (c-statistic 0.90, 95% CI: 0.87-0.92) and very well calibrated (integrated calibration index 0.056, 95% CI: 0.039-0.074). AF probability can be accurately determined using routinely collected health administrative data. This observation supports using the AF prediction model to minimize misclassification bias when studying AF using health administrative data.
如果病例识别不准确（“分类错误偏倚”），基于人群的健康管理数据的分析可能会得出错误的结果。与使用诊断代码识别髋臼骨折 (AF) 相比，使用管理数据的 AF 预测模型可减少分类错误偏倚。本研究在另一家医院测量了该 AF 预测模型的准确性。我们计算了验证医院 2015 年至 2020 年期间所有住院患者的 AF 概率。按预期 AF 概率分层选择了 1000 名患者的随机样本。回顾患者的影像学研究以确定真实的 AF 状态。验证人群包括 1000 人。AF 预测模型具有很好的区分度（c 统计量 0.90，95%CI：0.87-0.92）和很好的校准度（综合校准指数 0.056，95%CI：0.039-0.074）。使用常规收集的健康管理数据可以准确确定 AF 概率。这一观察结果支持在使用健康管理数据研究 AF 时使用 AF 预测模型来最小化分类错误偏倚。
