Validity of Diagnostic Codes and Laboratory Tests to Identify Cholangiocarcinoma and Its Subtypes.

Background: The absence of validated methods to identify cholangiocarcinoma in real-world data has prevented the conduct of pharmacoepidemiologic studies to evaluate determinants of this malignancy and examine the effectiveness of cholangiocarcinoma treatments.

Objective: To determine the accuracy of International Classification of Diseases for Oncology, Third Edition (ICD-O-3)-based algorithms to identify cholangiocarcinoma and its subtype (intrahepatic or extrahepatic) within US Veterans Health Administration (VA) data.

Methods: We identified patients with cholangiocarcinoma ICD-O-3 diagnosis codes from January 2000-December 2019 in VA data. We developed eight algorithms utilizing ICD-O-3 histology codes for cholangiocarcinoma and further used ICD-O-3 topography codes for location (liver, intrahepatic bile duct, extrahepatic bile duct) plus maximum total bilirubin (≥ 3 mg/dL vs. < 3 mg/dL) within ± 45 days of diagnosis to identify cholangiocarcinoma subtype. Up to 80 patients were randomly selected for each algorithm, and their records were reviewed by two hepatologists. The positive predictive values (PPV) and 95% confidence interval (CI) for each algorithm were estimated.

Results: Among 2934 unique patients who met inclusion criteria, 574 were randomly selected for validation. All eight algorithms had high PPV for definite or probable cholangiocarcinoma, ranging from 83.8% (95% CI, 73.8%-91.1%) to 100.0% (95% CI, 95.5%-100.0%). Among three algorithms to identify intrahepatic cholangiocarcinoma, two had PPV ≥ 80% (range: 88.8% [95% CI, 79.7%-94.7%]-91.3% [95% CI, 82.8%-96.4%]). Among five algorithms to identify extrahepatic cholangiocarcinoma, four had PPV ≥ 80% (range: 80.0% [95% CI, 69.6%-88.1%]-94.0% [83.5%-98.7%]).

Conclusion: These algorithms can be used in future pharmacoepidemiologic studies to evaluate medications associated with intrahepatic or extrahepatic cholangiocarcinoma.