A cervical cancer decision model to inform recommendations about preventive services
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A Cervical Cancer Decision Model to Inform Recommendations About Preventive Services. Perspective of the Decision Modeler Shalini Kulasingam, PhD Duke University Durham, NC. The Natural History of Cervical Cancer. Clearance. HPV infected Cervix. Regression. Invasion. Normal Cervix.

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A Cervical Cancer Decision Model to Inform Recommendations About Preventive Services

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A cervical cancer decision model to inform recommendations about preventive services

A Cervical Cancer Decision Model to Inform Recommendations About Preventive Services

Perspective of the Decision ModelerShalini Kulasingam, PhDDuke UniversityDurham, NC


The natural history of cervical cancer

The Natural History of Cervical Cancer

Clearance

HPV

infected

Cervix

Regression

Invasion

Normal

Cervix

Pre-cancer

CIN 3

Cancer

Infection

Progression

Regression

Progression

Self-limited Infection

(CIN 1, CIN 2?)


Why a decision model for cervical cancer

Why a Decision Model for Cervical Cancer?

  • Natural history

  • New screening tests

    • HPV tests

    • Cervical cytology tests

  • Vaccination

  • Guidelines

    • What age to begin screening

    • What age to end screening

    • Screening frequency


  • An rct for every combination is impossible

    An RCT for Every Combination is Impossible

    3 screening tests *

    15 different ages to start screening *

    8 different ages to end screening =

    1 big headache + insufficient funds


    What is a model

    What is a Model?


    State transition model

    Year

    Nml

    HPV

    CIN 1

    CIN 2-3

    CA

    D

    1

    100%

    Nml

    HPV

    CIN 1

    CIN 2-3

    CA

    D

    2

    94%

    5%

    1%

    Nml

    HPV

    CIN 1

    CIN 2-3

    CA

    D

    3

    88%

    8%

    2%

    2%

    State Transition Model

    Screening affects transitions

    for CIN 1, CIN 2-3

    and cancer (Stage I)

    Nml=Normal


    The duke cervical cancer model

    The Duke Cervical Cancer Model

    • Markov state transition model of HPV, cervical pre-cancer and cancer

      • Can account for impact of screening and vaccination

    • Original model developed for 1999 AHRQ evidence report on new cervical cancer screening technologies by Evan Myers, MD, MPH (Professor, Duke University)

    • Validated by comparing outcomes to

      • Reported outcomes (e.g., SEER)

      • Outcomes predicted by other independently developed models

    • Used by a number of different academic groups and by government agencies and pharmaceutical companies

    • Limitations

      • Reflects clinical practice and includes CIN 1 as a state

      • Scientifically moving toward defining CIN 3 as the only true pre-cancer state

      • Data are grouped into age categories that may be blunt to one-year age differences


    How do we use the model to calculate an outcome

    How Do We Use the Model to Calculate an Outcome?

    • Life-years gained

      • With screening and treatment, more women

      • survive for a longer time

      • Model calculates average life-expectancy for the cohort with and without screening and treatment

      • LYG is difference between these two

    • Colposcopies – Task Force measure of burden of screening

    • Cost – traditional measure of resources used


    Current recommendations 2003

    Current Recommendations (2003)

    • Direct evidence to determine the optimal starting and stopping age and interval for screening is limited. Indirect evidence suggests most of the benefit can be obtained by beginning screening within 3 years of onset of sexual activity or age 21 (whichever comes first) and screening at least every 3 years

    • The USPSTF recommends against routinely screening women older than age 65 for cervical cancer if they have had adequate recent screening with normal Pap smears and are not otherwise at high risk for cervical cancer

    • The USPSTF concludes that the evidence is insufficient to recommend for or against the routine use of new technologies to screen for cervical cancer


    Questions posed by uspstf

    Questions posed by USPSTF

    • Age to begin cervical cancer screening

    • Age to end cervical cancer screening

    • Role of HPV tests in primary screening and triage of abnormal cytology results

    • Role of liquid-based cytology


    Communicating with the tf

    Communicating with the TF….


    Issues in answering the tf questions

    Issues in Answering the TF Questions

    • Evidence Report for Screening Tests

      • Oregon EPC

        • Use the data from this report for the model

        • Need to coordinate so that the findings are consistent

    • Short time frame

      • Original time frame of 3 months

    • The “oh you have a model” syndrome

      • Change in model structure

      • Change in questions and output requested

      • Keeping up with an onslaught of HPV and cervical cancer studies


    Results age to begin screening

    Results: Age to Begin Screening


    Results age to end screening

    Results: Age to End Screening


    Results age to end screening1

    Results: Age to End Screening


    Results hpv dna tests

    Results: HPV DNA Tests


    Results liquid vs conventional cytology

    Results: Liquid vs. Conventional Cytology


    Summary of model results

    Summary of Model Results

    • Age 21, screening q3 depends on measure used

    • Little benefit to screening well screened women after age 65

    • HPV testing for women with ASCUS confirmed; role in primary screening remains unclear

    • Preference for screening using conventional or LBC depends on classification of CIN 1


    Shortcomings of the current approach

    Shortcomings of the Current Approach

    • What outcome?

      • Colposcopies similar to colonoscopies?

    • How do current guidelines affect findings?

      • ASCCP guidelines for Age 21

    • How do we compare our results with others?

      • Cost per life-year


    Shortcomings of the current model

    Shortcomings (?) of the Current Model

    • Natural history

      • Role of CIN 1

    • Vaccination

      • Need to change/construct new model(s)


    Acknowledgements

    Acknowledgements

    • Laura Havrilesky, MD, Duke University

    • Evan Myers, MD, Duke University

    • Julian Irvine, Duke University

    • Task Force esp. George Sawaya, MD and Diana Petitti MD, PhD

    • AHRQ: Tracy Wolff, MD, Tess Miller DrPh and Mary Barton, MD; CDC: Mona Saraiya, MD, MPH

    • Funded by the United States Centers for Disease Control and Prevention and the Agency for Healthcare Research and Quality

    • Shalini Kulasingam is supported by NCI grant K07-CA113773


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