Validation of a Case-Finding Algorithm for Identifying Patients with Non-small Cell Lung Cancer (NSCLC) in Administrative Claims Databases

Ralph M. Turner, Yen-Wen Chen, Ancilla W. Fernandes

Objective: To assess the validity of a treatments- and tests-based Case-Finding Algorithm for identifying patients with non-small cell lung cancer (NSCLC) from claims databases. Data sources: Primary data from the HealthCore Integrated Research Environment (HIRE)-Oncology database and the HealthCore Integrated Research Database (HIRD) were collected between June 1, 2014, and October 31, 2015. Study design: A comparative statistical evaluation using receiver operating characteristic (ROC) curve analysis and other validity measures was used to validate the NSCLC Case-Finding Algorithm vs. a control algorithm. Data collection: Patients with lung cancer were identified based on diagnosis and pathology classifications as NSCLC or small-cell lung cancer. Records from identified patients were linked to claims data from Anthem health plans. Three-month pre-index and post-index data were included. Principal findings: The NSCLC Case-Finding Algorithm had an area under the curve (AUC) of 0.88 compared with 0.53 in the control (p < 0.0001). Promising diagnostic accuracy was observed for the NSCLC Case-Finding Algorithm based on sensitivity (94.8%), specificity (81.1%), positive predictive value (PPV) (95.3%), negative predictive value (NPV) (79.6%), accuracy (92.1%), and diagnostic odds ratio (DOR) (78.8). Conclusions: The NSCLC Case-Finding Algorithm demonstrated strong validity for distinguishing patients with NSCLC from those with SCLC in claims data records and can be used for research into NSCLC populations.