Novel Approach Using Administrative Claims to Evaluate Trends in Oncology Multigene Panel Testing for Patients Enrolled in Medicare Advantage Health Plans

Development of an algorithm to identify small cell lung cancer patients in claims databases

Introduction

The treatment landscape of small cell lung cancer (SCLC) is evolving. Evidence generated from administrative claims is needed to characterize real-world SCLC patients. However, the current ICD-10 coding system cannot distinguish SCLC from non-small cell lung cancer (NSCLC). We developed and estimated the accuracy of an algorithm to identify SCLC in claims-only databases.

Methods

We performed a cross-sectional study of lung cancer patients diagnosed from 2016-2017 using the Surveillance, Epidemiology and End Results (SEER), linked with Medicare database. The analysis included two phases - data exploration (utilizing a 25% random sample) and data validation (remaining 75% sample). The SEER definition of SCLC and NSCLC were used as the gold standard. Claims-based algorithms were identified and evaluated for their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Results

The eligible cohort included 31,912 lung cancer patients. The mean age was 76.3 years, 44.6% were male, with 9.4% having SCLC and 90.6% identified as NSCLC using SEER. The exploration analysis identified potential algorithms based on treatment data. In the validation analysis of 7,438 lung cancer patients who received systemic treatment in the outpatient setting, an etoposide-based algorithm (etoposide use in 180 days following lung cancer diagnosis) to identify SCLC showed: sensitivity 95%, specificity 95%, PPV 82% and NPV 99%.

Discussion

An etoposide treatment-based algorithm showed good accuracy in identifying SCLC patients. Such algorithms can facilitate analyses of treatment patterns, outcomes, healthcare resource and costs among treated SCLC patients, thereby bolstering the evidence-base for best patient care.

Real-world Trends, Rural-urban Differences, and Socioeconomic Disparities in Utilization of Narrow versus Broad Next-generation Sequencing Panels

Advances in genetic technology have led to the increasing use of genomic panels in precision oncology practice, with panels ranging from a couple to hundreds of genes. However, the clinical utilization and utility of oncology genomic panels, especially among vulnerable populations, is unclear. We examined the association of panel size with socioeconomic status and clinical trial matching. We retrospectively identified 9,886 eligible adult subjects in the Mayo Clinic Health System who underwent genomic testing between January 1, 2016 and June 30, 2020. Patient data were retrieved from structured and unstructured data sources of institutional collections, including cancer registries, clinical data warehouses, and clinical notes. Socioeconomic surrogates were approximated using the Area Deprivation Index (ADI) corresponding to primary residence addresses. Logistic regression was performed to analyze relationships between ADI or rural/urban status and (i) use of genomic test by panel size; (ii) clinical trial matching status. Compared with patients from the most affluent areas, patients had a lower odds of receiving a panel test (vs. a single-gene test) if from areas of higher socioeconomic deprivation [OR (95% confidence interval (CI): 0.71 (0.61-0.83), P < 0.01] or a rural area [OR (95% CI): 0.85 (0.76-0.96), P < 0.01]. Patients in areas of higher socioeconomic deprivation were less likely to be matched to clinical trials if receiving medium panel tests [(OR) (95% CI): 0.69 (0.49-0.97), P = 0.03]; however, there was no difference among patients receiving large panel tests (P > 0.05) and rural patients were almost 2x greater odds of being matched if receiving a large panel test [(OR) (95% CI): 1.76 (1.21-2.55), P < 0.01].

SIGNIFICANCE

We identified socioeconomic and rurality disparities in the use of genomic tests and trial matching by panel size, which may have implications for equal access to targeted therapies. The lack of association between large panel tests and clinical trial matching by socioeconomic status, suggests a potential health equity impact, while removing barriers in access to large panels for rural patients may improve access to trials. However, further research is needed.

Trends and Clinical Characteristics of Next-Generation Sequencing-Based Genetic Panel Tests: An Analysis of Korean Nationwide Claims Data

PURPOSE

In the modern era of precision medicine, next-generation sequencing (NGS) is employed for a variety of clinical purposes. The aim of this study was to investigate the trends and clinical characteristics of NGS testing in South Korea.

MATERIALS AND METHODS

This nationwide, population-based, retrospective cohort study examined National Health Insurance Service claims data from 2017 to 2021 for NGS and from 2008 to 2021 for gene-targeted anticancer drugs.

RESULTS

Among the total 98,748 claims, there were 51,407 (52.1%) solid cancer panels, 30,173 (30.5%) hereditary disease panels, and 17,168 (17.4%) hematolymphoid cancer panels. The number of annual claims showed a persistent upward trend, exhibiting a 5.4-fold increase, from 5,436 in 2017 to 29,557 in 2021. In the solid cancer panel, colorectal cancer was the most common (19.2%), followed by lung cancer (18.8%). The annual claims for targeted cancer drugs have increased 25.7-fold, from 3,932 in 2008 to 101,211 in 2020. Drugs for the treatment of lung cancer accounted for 488,819 (71.9%) claims. The number of patients who received non-hereditary NGS testing has substantially increased, and among them, the count of patients prescribed targeted anticancer drugs consistently rose from 508 (13.9%) in 2017 to 2,245 (12.3%) in 2020.

CONCLUSION

This study highlights the rising nationwide demand for comprehensive genetic testing for disease diagnosis and treatment following NGS reimbursement by the National Health Insurance in South Korea, in addition to the need for greater utilization of targeted anticancer drugs.

Analyzing Precision Medicine Utilization with Real-World Data: A Scoping Review

Precision medicine (PM), specifically genetic-based testing, is currently used in over 140,000 individual tests to inform the clinical management of disease. Though several databases (e.g., the NIH Genetic Testing Registry) demonstrate the availability of these sequencing-based tests, we do not currently understand the extent to which these tests are used. There exists a need to synthesize the body of real-world data (RWD) describing the use of sequencing-based tests to inform their appropriate use. To accomplish this, we performed a scoping review to examine what RWD sources have been used in studies of PM utilization between January 2015 and August 2021 to characterize the use of genome sequencing (GS), exome sequencing (ES), tumor sequencing (TS), next-generation sequencing-based panels (NGS), gene expression profiling (GEP), and pharmacogenomics (PGx) panels. We abstracted variables describing the use of these types of tests and performed a descriptive statistical analysis. We identified 440 articles in our search and included 72 articles in our study. Publications based on registry databases were the most common, followed by studies based on private insurer administrative claims. Slightly more than one-third (38%) used integrated datasets. Two thirds (67%) of the studies focused on the use of tests for oncological clinical applications. We summarize the RWD sources used in peer-reviewed literature on the use of PM. Our findings will help improve future study design by encouraging the use of centralized databases and registries to track the implementation and use of PM.