Minimizing misclassification bias with a model to identify acetabular fractures using health administrative data: A cohort study

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, 10(th) 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 R(2) 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, 10(th) 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.