Abstract 4398: Validation of an updated algorithm to identify non-small cell lung cancer (NSCLC) patients in administrative claims databases

Abstract

Abstract Purpose: Administrative claims data using the ICD-9-CM or ICD-10-CM coding systems are not detailed enough to distinguish between lung cancer subtypes, which presents challenges for real-world research. This study updated and validated a previous treatment-based algorithm that uses claims data to identify NSCLC vs small-cell lung cancer (SCLC) (Turner RM, et al. Front Pharmacol 2017;8:883). Methods: This study used Optum’s de-identified Market Clarity Data (2007-2021) which deterministically links medical and pharmacy claims with electronic health record (EHR) data from providers across the continuum of care. Included patients had lung cancer diagnosis and histology information available in the EHR, which is the gold standard for validation. Patients were required to have ≥3 months of continuous health plan enrollment before and after the diagnosis date. First, to replicate the Turner algorithm, patients were identified between Jun 2014 and Oct 2015 with any first-line NSCLC treatment and no SCLC treatment (Step 1). Next, the Turner algorithm was applied from Nov 2015 to Dec 2020 to evaluate if performance decreased given the approval of immunotherapies since 2015 (Step 2). Finally, the Turner algorithm was updated with NSCLC treatments approved since 2015 in concordance with the latest US treatment guidelines. Algorithm performance at each step was measured using sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Results: Sample sizes and performance statistics for each step are presented in the Table. From Step 1 to Step 2, specificity and PPV decreased while sensitivity and NPV increased. Algorithm performance improved slightly for all measures from Step 2 to Step 3. Conclusions: This updated treatment-based algorithm improves validity and accuracy in identifying NSCLC patients in claims databases and supports its use in real-world research using claims databases when histology data is unavailable. Step 1: Turner replication Step 2: Turner 2015-2020 Step 3: Updated Turner 2015-2020 Date range Jun 2014-Oct 2015 Nov 2015-Dec 2020 Nov 2015-Dec 2020 Sample size 406 2573 2744 Sensitivity 0.873 0.920 0.932 Specificity 0.933 0.865 0.923 PPV 0.987 0.976 0.988 NPV 0.560 0.640 0.673 Citation Format: Sandip Pravin Patel, Rongrong Wang, Summera Qiheng Zhou, Daniel Sheinson, Ann Johnson, Janet Lee. Validation of an updated algorithm to identify non-small cell lung cancer (NSCLC) patients in administrative claims databases. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4398.