Department of Surgery, University of Ottawa, Ottawa Hospital Research Institute, Canada.
Department of Medicine, University of Ottawa, Canada.
Medicine (Baltimore). 2021 Dec 30;100(52):e28223. doi: 10.1097/MD.0000000000028223.
Acetabular fractures (AFs) are relatively uncommon thereby limiting their study. Analyses using population-based health administrative data can return erroneous results if case identification is inaccurate ('misclassification bias'). This study measured the impact of an AF prediction model based exclusively on administrative data upon misclassification bias.We applied text analytical methods to all radiology reports over 11 years at a large, tertiary care teaching hospital to identify all AFs. Using clinically-based variable selection techniques, a logistic regression model was created.We identified 728 AFs in 438,098 hospitalizations (15.1 cases/10,000 admissions). The International Classification of Disease, 10th revision (ICD-10) code for AF (S32.4) missed almost half of cases and misclassified more than a quarter (sensitivity 51.2%, positive predictive value 73.0%). The AF model was very accurate (optimism adjusted R2 0.618, c-statistic 0.988, calibration slope 1.06). When model-based expected probabilities were used to determine AF status using bootstrap imputation methods, misclassification bias for AF prevalence and its association with other variables was much lower than with International Classification of Disease, 10th revision S32.4 (median [range] relative difference 1.0% [0%-9.0%] vs 18.0% [5.4%-75.0%]).Lone administrative database diagnostic codes are inadequate to create AF cohorts. The probability of AF can be accurately determined using health administrative data. This probability can be used in bootstrap imputation methods to importantly reduce misclassification bias.
髋臼骨折（AFs）相对少见，因此限制了对其的研究。如果病例识别不准确（“分类错误偏倚”），则基于人群的健康管理数据的分析可能会得出错误的结果。本研究通过专门基于管理数据的 AF 预测模型来衡量分类错误偏倚的影响。我们应用文本分析方法对一家大型三级护理教学医院 11 年来的所有放射学报告进行分析，以确定所有 AFs。使用基于临床的变量选择技术，创建了一个逻辑回归模型。我们在 438,098 次住院治疗中发现了 728 例 AF（15.1 例/10,000 例入院）。AF 的国际疾病分类，第 10 版（ICD-10）代码（S32.4）错过了近一半的病例，并且分类错误超过四分之一（敏感性 51.2%，阳性预测值 73.0%）。AF 模型非常准确（乐观调整 R2 为 0.618，C 统计量为 0.988，校准斜率为 1.06）。当使用 bootstrap 插补方法根据基于模型的预期概率确定 AF 状态时，AF 患病率及其与其他变量的关联的分类错误偏差远低于 ICD-10 S32.4（中位数[范围]相对差异 1.0%[0%-9.0%]比 18.0%[5.4%-75.0%]）。单独的管理数据库诊断代码不足以创建 AF 队列。可以使用健康管理数据准确确定 AF 的可能性。可以在 bootstrap 插补方法中使用此概率，以重要降低分类错误偏差。
