Bootstrap imputation with a disease probability model minimized bias from misclassification due to administrative database codes.
OBJECTIVE
Diagnostic codes used in administrative databases cause bias due to misclassification of patient disease status. It is unclear which methods minimize this bias.


METHODS
Serum creatinine measures were used to determine severe renal failure status in 50,074 hospitalized patients. The true prevalence of severe renal failure and its association with covariates were measured. These were compared to results for which renal failure status was determined using surrogate measures including the following: (1) diagnostic codes; (2) categorization of probability estimates of renal failure determined from a previously validated model; or (3) bootstrap methods imputation of disease status using model-derived probability estimates.


RESULTS
Bias in estimates of severe renal failure prevalence and its association with covariates were minimal when bootstrap methods were used to impute renal failure status from model-based probability estimates. In contrast, biases were extensive when renal failure status was determined using codes or methods in which model-based condition probability was categorized.


CONCLUSIONS
Bias due to misclassification from inaccurate diagnostic codes can be minimized using bootstrap methods to impute condition status using multivariable model-derived probability estimates.
Epidemiology & Community Medicine, University of Ottawa; Ottawa Hospital Research Institute, ASB1-003, 1053 Carling Ave., Ottawa, Ontario K1Y 4E9, Canada; Institute for Clinical Evaluative Sciences. Electronic address: carlv@ohri.ca.
Carl
C
van Walraven
D000368:Aged; D015982:Bias; D016208:Databases, Factual / Q000706:statistics & numerical data*; D003951:Diagnostic Errors / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011336:Probability; D051437:Renal Insufficiency / Q000453:epidemiology*; D015203:Reproducibility of Results
Bootstrap; Categorization; Health administrative data; Information bias; Misclassification bias; Observation bias