The Administrative Data Cystectomy Model and its Impact on Misclassification Bias

Abstract

Abstract Background: Misclassification bias (MB) is the deviation of measured from true values due to incorrect case assignment. This study compared MB when cystectomy status was determined using administrative database codes vs. predicted cystectomy probability. Methods: We identified every primary cystectomy-diversion type at a single hospital 2009-2019. We linked to claims data to measure true association of cystectomy with 30 patient-hospitalization factors. Associations were also measured when cystectomy status was assigned using billing codes and cystectomy probability from multivariate logistic regression model with covariates from administrative data. MB was the difference between measured and true associations. Results: 500 people underwent cystectomy (0.12% of 428 677 hospitalizations). Sensitivity and positive predictive values for cystectomy codes were 97.1% and 58.6% for incontinent diversions and 100.0% and 48.4% for continent diversions, respectively. The model accurately predicted cystectomy-incontinent diversion (c-statistic [C] 0.999, Integrated Calibration Index [ICI] 0.000) and cystectomy-continent diversion (C:1.000, ICI 0.000) probabilities. MB was significantly lower with imputed cystectomy-diversion type status using model-based predictions for both incontinent cystectomy (F=12.75; p<.0001) and continent cystectomy (F=11.25; p<.0001). Conclusions: A model using administrative data accurately returned the probability that cystectomy by diversion type occurred during a hospitalization. Using this model to impute cystectomy status minimized MB. Accuracy of administrative database research can be increased by using probabilistic imputation to determine case status instead of individual codes.