Abstract B102: NSCLC observations combining medical charts and administrative claims data
Authors: Schwab Phil , MacLean Elizabeth , Chioda Marc , Moll Keran , Pasquale Margaret , Mardekian Jack , Futch Tracy 
Published: May 2016

Background: We previously examined diagnostic testing and treatment patterns for NSCLC using administrative claims data which relied on ICD-9-CM diagnosis codes. With no specific code for NSCLC, an algorithm was developed to exclude cases with SCLC. Subsequently, medical record data were added to verify the claims-based NSCLC algorithm and to supplement observations of NSCLC diagnostic testing and treatment patterns.

Objective: To evaluate a claims-based algorithm for identifying NSCLC based on diagnosis in the medical record and to re-examine diagnostic testing and treatment patterns after combining claims data with medical chart data.

Methods: The previous study attempted to identify patients with metastatic NSCLC by requiring a diagnosis for lung cancer (162.xx) and diagnosis of metastatic disease (196.xx, 197.xx,198.xx), with no other malignancy. Patients were excluded if a medication typically used to treat SCLC was dispensed. In total, 2,623 patients with suspected NSCLC met these criteria. For the follow-up study, medical chart records were requested from providers who were identified from claims where metastatic disease was first listed. From the chart data, abstractors identified data for 356 patients that could be matched to claims data from the prior study.

Results: Of the 356 suspected NSCLC patients with charts and claims data, 204 did not specify type. Of the other 132, 79% had NSCLC and 21% had SCLC. Patients with NSCLC were 85% white, 10% black, 5% other, mostly male (62%), mean age 71 years, residing across 31 states. Stage at diagnosis was noted for all patients (n = 120) with 34% Stage 1 or 2, 17% Stage 3, and 47% Stage 4. Fewer than half (44%) had molecular testing within 45 days of the date of metastasis in the claims data. Most (93%) had evidence of histological testing via claims data with charts indicating 37% with adenocarcinoma, 21% squamous cell, &lt; 10% large cell or mixed, and 39% had no mention of subtype. 53% of patients with adenocarcinoma, 32% of squamous cell, and 47% where subtype was not mentioned had molecular testing within 45 days of the date of metastasis. Some NSCLC patients (11%) received a targeted therapy (erlotinib), but not all had evidence of molecular testing prior to treatment. All with targeted therapy had adenocarcinoma if subtype was mentioned. Most NSCLC patients were current smokers (28%) or prior smokers (68%) based on charts with smoking status (n = 102). Molecular testing within 45 days of metastasis was evident for 48% of current smokers and 48% of prior smokers.

Conclusions: Based on analysis of chart data, the NSCLC algorithm for claims data did not effectively exclude SCLC. NSCLC would be expected in 85% of cases and SCLC in 10-15% of all lung cancers, yet the algorithm developed to select NSCLC yielded a sample with about 21% SCLC. Claims data allow for a fairly comprehensive view of a large number of patients, but important information is not available, such as a distinction between NSCLC and SCLC. Medical record data enable greater depth of patient and tumor characteristics, but missing data may be common due to less restrictive reporting standards and because reporting is not tied to reimbursements as is the case with coding and claims. Combining medical record data with claims data for an observational study facilitates more detailed and comprehensive views of diagnostic testing and treatments, but researchers must compromise on sample size.

Citation Format: Phil Schwab, Elizabeth MacLean, Marc Chioda, Keran Moll, Margaret Pasquale, Jack Mardekian, Tracy Futch. NSCLC observations combining medical charts and administrative claims data. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B102.