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  11. Development Of An Algorithm To Identify Small Cell Lung Cancer Patients In Claims Databases.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Development Of An Algorithm To Identify Small Cell Lung Cancer Patients In Claims Databases.

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

Mark D Danese1, Akhila Balasubramanian2, D Gwyn Bebb2

  • 1Outcomes Insights, Inc., United States, Calabasas, CA, United States.

Frontiers in Oncology
|August 30, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

An etoposide-based algorithm accurately identifies small cell lung cancer (SCLC) in claims data. This method aids in studying real-world SCLC patient treatment and outcomes.

Area of Science:

  • Oncology
  • Health Informatics
  • Epidemiology

Background:

  • The current ICD-10 coding system lacks specificity to differentiate small cell lung cancer (SCLC) from non-small cell lung cancer (NSCLC) in administrative claims.
  • Characterizing real-world SCLC patient populations requires accurate identification methods for claims-only databases.
  • The evolving treatment landscape of SCLC necessitates robust data for evidence-based patient care.

Purpose of the Study:

  • To develop and validate an algorithm for identifying SCLC patients using administrative claims data.
  • To assess the accuracy of the developed algorithm in distinguishing SCLC from NSCLC.
  • To enable future research on SCLC treatment patterns, outcomes, and healthcare resource utilization.

Main Methods:

  • A cross-sectional study utilized linked Surveillance, Epidemiology and End Results (SEER) and Medicare data (2016-2017).
Keywords:
Medicarealgorithmantineoplastic therapyclaimssmall cell lung cancer

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  • An etoposide treatment-based algorithm was developed and validated in distinct patient cohorts.
  • Algorithm performance was evaluated using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) against SEER definitions.

    • Main Results:

    • The validation cohort included 7,438 patients receiving systemic treatment.
    • An etoposide-based algorithm demonstrated high accuracy: 95% sensitivity, 95% specificity, 82% PPV, and 99% NPV for SCLC identification.
    • The algorithm specifically identified SCLC based on etoposide use within 180 days of diagnosis.

    Conclusions:

    • An etoposide treatment-based algorithm is a reliable tool for identifying SCLC patients in claims databases.
    • This validated algorithm can facilitate comprehensive real-world evidence studies for SCLC.
    • Improved characterization of SCLC patients will support enhanced treatment strategies and patient care.