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Improving Cervical Precancer Surveillance: Validity of Claims-Based Prediction Models in ICD-9 and ICD-10 Eras

  • Jaimie Z. Shing
  • , Marie R. Griffin
  • , Linh D. Nguyen
  • , James C. Slaughter
  • , Edward F. Mitchel
  • , Manideepthi Pemmaraju
  • , Alyssa B. Rentuza
  • , Pamela C. Hull

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: Human papillomavirus vaccine (HPV) impact on cervical precancer (cervical intraepithelial neoplasia grades 2+ [CIN2+]) is observable sooner than impact on cancer. Biopsy-confirmed CIN2+ is not included in most US cancer registries. Billing codes could provide surrogate metrics; however, the International Classification of Diseases, ninth (ICD-9) to tenth (ICD-10) transition disrupts trends. We built, validated, and compared claims-based models to identify CIN2+ events in both ICD eras. Methods: A database of Davidson County (Nashville), Tennessee, pathology-confirmed CIN2+ from the HPV Vaccine Impact Monitoring Project (HPV-IMPACT) provided gold standard events. Using Tennessee Medicaid 2008-2017, cervical diagnostic procedures (N = 8549) among Davidson County women aged 18-39 years were randomly split into 60% training and 40% testing sets. Relevant diagnosis, procedure, and screening codes were used to build models from CIN2+ tissue diagnosis codes alone, least absolute shrinkage and selection operator (LASSO), and random forest. Model-classified index events were counted to estimate incident events. Results: HPV-IMPACT identified 983 incident CIN2+ events. Models identified 1007 (LASSO), 1245 (CIN2+ tissue diagnosis codes alone), and 957 (random forest) incident events. LASSO performed well in ICD-9 and ICD-10 eras: 77.3% (95% confidence interval [CI] = 72.5% to 81.5%) vs 81.1% (95% CI = 71.5% to 88.6%) sensitivity, 93.0% (95% CI = 91.9% to 94.0%) vs 90.2% (95% CI = 87.2% to 92.7%) specificity, 61.3% (95% CI = 56.6% to 65.8%) vs 60.3% (95% CI = 51.0% to 69.1%) positive predictive value, 96.6% (95% CI = 95.8% to 97.3%) vs 96.3% (95% CI = 94.1% to 97.8%) negative predictive value, 91.0% (95% CI = 89.9% to 92.1%) vs 88.8% (95% CI = 85.9% to 91.2%) accuracy, and 85.1% (95% CI = 82.9% to 87.4%) vs 85.6% (95% CI = 81.4% to 89.9%) C-indices, respectively; performance did not statistically significantly differ between eras (95% confidence intervals all overlapped). Conclusions: Results confirmed model utility with good performance across both ICD eras for CIN2+ surveillance. Validated claims-based models may be used in future CIN2+ trend analyses to estimate HPV vaccine impact where population-based biopsies are unavailable.

Original languageEnglish
Article numberpkaa112
JournalJNCI Cancer Spectrum
Volume5
Issue number1
DOIs
StatePublished - Feb 1 2021

Bibliographical note

Publisher Copyright:
© 2020 The Author(s). Published by Oxford University Press.

Funding

FundersFunder number
National Center for Advancing Translational Sciences (NCATS)TL1TR002244
National Center for Advancing Translational Sciences (NCATS)

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

    ASJC Scopus subject areas

    • Oncology
    • Cancer Research

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