Validation of an Updated Algorithm to Identify Patients With Incident Non-Small Cell Lung Cancer in Administrative Claims Databases.

PURPOSE: Real-world lung cancer data in administrative claims databases often lack staging information and specific diagnostic codes for lung cancer histology subtypes. This study updates and validates Turner's 2017 treatment-based algorithm using more recent claims and electronic health record (EHR) data. METHODS: This study used Optum's deidentified Market Clarity Data of linked medical and pharmacy claims with EHR data. Eligible patients had an incident lung cancer diagnosis (January 2014-December 2020) and ≥one valid histology code for lung cancer 30 days before to 60 days after diagnosis. Histology and stage information from the EHR were used to evaluate the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We evaluated the Turner algorithm using cohort 1 patients diagnosed between June 2014 and October 2015 (step 1) and between November 2015 and December 2020 after approval of immunotherapies (step 2). Next, we evaluated cohort 2 patients diagnosed between November 2015 and December 2020 using an updated algorithm incorporating the latest US treatment guidelines (step 3), and compared the results for cohort 2 (Turner algorithm, step 2 patients). Furthermore, an algorithm to determine early NSCLC (eNSCLC; stage I-III) versus metastatic or advanced/metastatic non-small cell lung cancer (stage IV) was evaluated among patients with available histology and stage information. RESULTS: A total of 5,012 patients were included (cohort 1, step 1: n = 406; cohort 1, step 2: n = 2,573; cohort 2, step 3: n = 2,744). The updated algorithm showed improved performance relative to the previous Turner algorithm for sensitivity (0.920-0.932), specificity (0.865-0.923), PPV (0.976-0.988), and NPV (0.640-0.673). The eNSCLC algorithm showed high specificity (0.874) and relatively low sensitivity (0.539). CONCLUSION: An updated treatment-based algorithm identifying patients with incident NSCLC was validated using EHR data and distinguished lung cancer subtypes in claims databases when EHR data were not available.

Estimating recurrences prevented and costs avoided with atezolizumab in early non-small cell lung cancer in the United States.

BACKGROUND: Recurrence of early-stage non-small cell lung cancer (eNSCLC) is associated with significant mortality and costs. Atezolizumab (ATZ) was recently approved as adjuvant treatment following resection and platinum-based chemotherapy for adults with stage II-IIIA NSCLC with PD-L1 expression ≥1% after demonstrating significant improvement in disease-free survival (DFS) relative to best supportive care (BSC) in the IMpower010 trial (NCT02486718). This study evaluated the population-level impact of ATZ as adjuvant treatment for eNSCLC in the United States by estimating the number and costs of recurrences avoided. METHODS: A Monte Carlo simulation model estimated the cumulative number of recurrences and deaths prevented, along with direct, indirect, and terminal care costs, by treating eNSCLC patients with ATZ compared to BSC. The model included eligible patients treated in any given year and followed over a 5-year period. Recurrence and mortality rates and costs were based on the IMpower010 data and supplemented by estimates from published literature. RESULTS: An estimated 4400 eNSCLC patients in the United States were eligible for adjuvant ATZ in any given year, of whom 2387 would experience recurrence within 5 years with BSC. Following the introduction of ATZ, 1030 (95% confidence interval [CI]: 1023, 1036) recurrences and 369 (95% CI: 362, 376) deaths would be avoided with estimated reductions in cumulative recurrence-related direct, indirect, and terminal care costs of $785 million, $15 million, and $32 million, respectively, over a 5-year time horizon. CONCLUSIONS: Adjuvant ATZ is estimated to prevent a significant number of recurrences and reduce the economic burden of eNSCLC.