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Validation of Molecular Pathology Codes for the Identification of Mutational Testing in Lung and Colon Cancer.

Vachani, Anil MD, MS; Wong, Yu-Ning MD, MSCE; Israelite, Jennifer RN; Mitra, Nandita PhD; Hin, Sakhena MPH; Yang, Lin MS; Smith-McLallen, Aaron PhD; Armstrong, Katrina MD, MSCE; Groeneveld, Peter W. MD, MS; Epstein, Andrew J. PhD
doi: 10.1097/MLR.0000000000000484
Applied Methods: PDF Only

Background: Targeted therapy for patients with lung and colon cancer based on tumor molecular profiles is an important cancer treatment strategy, but the impact of gene mutation tests on cancer treatment and outcomes in large populations is not clear. In this study, we assessed the accuracy of an algorithm to identify tumor mutation testing in administrative claims data during a period before test-specific Current Procedural Terminology codes were available.

Materials and Methods: We used Pennsylvania Cancer Registry data to select patients with lung or colon cancer diagnosed between 2007 and 2011 who were treated at the University of Pennsylvania Health System, and we obtained their administrative claims. A combination of Current Procedural Terminology laboratory codes (stacking codes) was used to identify potential tumor mutation testing in the claims data. Patients' electronic medical records were then searched to determine whether tumor mutation testing actually had been performed. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated.

Results: An algorithm using stacking codes had moderate sensitivity (86% for lung cancer and 81% for colon cancer) and high specificity (98% for lung cancer and 96% for colon cancer). Sensitivity and specificity did not vary significantly during 2007-2011. In patients with lung cancer, PPV was 98% and NPV was 92%. In patients with colon cancer, PPV was 96% and NPV was 83%.

Conclusions: An algorithm using stacking codes can identify tumor mutation testing in administrative claims data among patients with lung and colon cancer with a high degree of accuracy.

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