Surveillance for hepatocellular carcinoma: development and validation of an algorithm to classify tests in administrative and laboratory data.

Background: The purpose of alpha-fetoprotein (AFP) and abdominal ultrasound (US) cannot be discerned in administrative data.

Aim: We developed an algorithm to identify AFP and US used as surveillance tests for hepatocellular carcinoma (HCC).

Methods: We evaluated 300 AFP and 301 US tests from a VA database. Surveillance predictors in the administrative files (diagnoses, labs) were examined in logistic regression models. We calculated model-based probabilities of HCC surveillance status, and developed classification procedures using single and multiple imputation methods.

Results: The predictors of surveillance intent for AFP were absence of alcoholism, abdominal pain, ascites, diabetes and high AST levels. For US, the predictors of surveillance were prior AFP testing and HIV status and absence of abdominal pain, ascites, or drug dependence. For AFP classification, single imputation compared favorably with multiple imputation, both showing robustness in discrimination and calibration. For US both approaches were less robust in discrimination and calibration which was more moderate in multiple imputation than single imputation.

Conclusions: Predictive algorithms in administrative files can be used to identify AFP performed for HCC surveillance, however, the intent of US is more difficult to identify.