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Using CDC Edits Metafile in the Registry to Support Clinical Trials Recruitment

Utilize CDC Edits Metafile to support clinical trials recruitment effectively by matching patient characteristics with trial criteria. Explore real-world examples and filtering processes for selecting trials.

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Using CDC Edits Metafile in the Registry to Support Clinical Trials Recruitment

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  1. Using CDC Edits Metafile in the Registry to Support Clinical Trials Recruitment Alan R. Houser, MA, MPH C/NET Solutions Dennis Deapen, DrPH Los Angeles Cancer Surveillance Program

  2. Finding a Clinical Trial • NCI web site, ClinicalTrials.gov, has 5933* open trials for cancer patients. • Focused search tool to filter on disease, age, location, and treatment. * Checked on 2/21/2007

  3. ClinicalTrials.gov • Optimal for searching for available trials for a single patient or patients with similar characteristics. • Not suited for screening for trials for a large number of patients with dissimilar characteristics at one time.

  4. Another Approach:CDC Edits Tools (1) • Descriptive cancer terminology built in • Excellent at complex pattern matching • Readily customizable – each trial is translated into a single edit – edits can be removed from Metafile when closed to recruitment

  5. Another Approach: CDC Edits Tools (2) • Match multiple patients against multiple trials • Match hundreds of trials against large numbers of cases at one time • Portable – can distribute metafile widely

  6. ClinicalTrials.gov For each listed trial, three elements of eligibility criteria: • Disease characteristics • Patient characteristics • Prior concurrent therapy EDITS language can test each of these elements

  7. How to Write an Edit to Select a Trial • Identify inclusion and exclusion requirements for trial • Match requirements to data fields in registry data set • “Failing” an edit means “matching” a trial’s requirements – require failure to display message • “Missing data” – write edits to exclude cases that don’t match requirements, leaving cases that are still potential matches

  8. Demonstration Project (1) • Write metafile edits for a selection of actual clinical trials selected from ClinicalTrials.gov • Select trials that require a diagnosis of cancer – no prophylactic studies • Select trials (five for each site) that use data fields available in registry data

  9. Demonstration Project (2) • Match metafile edits against a sample of real cancer case reports from central registry (California Cancer Registry) • Select cases from 2004 forward to take advantage of Collaborative Staging

  10. Methodology • Datafile selected from California Cancer Registry Eureka database: NAACCR 11.1 format • Clinical Trials Metafile created with EditWriter 3.0: five trials for each site • Edits Metafile run against Eureka datafile with GenEdits Plus (beta)

  11. Selecting Trials fromClinicalTrials.Gov (1) Select by Primary Site and Location (California) • Breast Cancer • Total Trials = 789* • California Trials = 131* • Translated into Metafile Edits = 5 * Checked on 2/21/2007

  12. Selecting Trials fromClinicalTrials.Gov (2) Select by Primary Site and Location (California) • Prostate Cancer • Total Trials = 366* • California Trials = 70* • Translated into Metafile Edits = 5 * Checked on 2/21/2007

  13. Selecting Trials fromClinicalTrials.Gov (3) Select by Primary Site and Location (California) • Lung Cancer • Total Trials = 575* • California Trials = 107* • Translated into Metafile Edits = 5 * Checked on 2/21/2007

  14. Case Data File (1) Extract test file from California Cancer Registry’s Eureka database: • All patients diagnosed 2004-2006 (about 2.5 years) • Three sites: breast, prostate, lung • Vital status alive • Los Angeles County residents at diagnosis • 31,007 cases identified

  15. Case Data File (2) • Selected from California Cancer Registry’s Eureka database • 3 sites, patients alive at last contact: • Breast (C500-C509), 15,708 cases • Prostate (C619), 11,197 cases • Lung (C340-349), 4102 cases

  16. Tracing the Filtering Process (1) Breast Clinical Trial NCT00382070 • Start:31,007 cases • Exclude if not female, not alive • Step 1:19,588 cases • Exclude if not breast or if bilateral • Step 2:15,613 cases • Exclude if not invasive, not microscopically confirmed • Step 3:12,688 cases

  17. Tracing the Filtering Process (2) Breast Clinical Trial NCT00382070 • Exclude if not Stage I, II, IIIA • Step 4:10,870 cases • Exclude if ERA, PRA are within normal limits • Step 5:8430 cases • Exclude if hormone therapy not given • Step 6:867 cases • Exclude if not lumpectomy or simple mastectomy with lymph node staging • Final:756 cases

  18. Results: Five Breast Cancer Trials Total breast cases: 15,708 • NCT00074152: 11,302 (72%) • NCT00127205: 573 ( 3.6%) • NCT00382070: 756 ( 4.8%) • NCT00388726: 341 ( 2.2%) • NCT00390455: 1075 ( 6.8%)

  19. Results: Five Prostate Cancer Trials Total prostate cases: 11,197 • NCT00004124: 97 ( 0.9%) • NCT00063882: 9 ( 0.1%) • NCT00110214: 247 ( 2.2%) • NCT00123838: 2814 (25%) • NCT00402285: 12 ( 0.1%)

  20. Results: Five Lung Cancer Trials Total lung cases: 4102 • NCT00008385: 222 ( 5.4%) • NCT00268489: 948 (23%) • NCT00293332: 1682 (41%) • NCT00368992: 183 ( 4.5%) • NCT00409188 : 154 ( 3.8%)

  21. Summary • Metafile technology can be used to screen large data sets for potential clinical participants • Matching criteria is limited by registry data set • Additional criteria not available to registry may exclude patients identified by metafile matching

  22. Limitations of Metafile Scanningof Registry Data (1) • Data not collected • Her2/neu (except in California) • Date treatment ended • Clinical factors (lab tests, fitness)

  23. Limitations of Metafile Scanningof Registry Data (2) • Incomplete treatment data • Cases may be reported before treatment is completed • No identification of specific agents • No identification of multiple courses

  24. Limitations of Metafile Scanningof Registry Data (3) • Incomplete recurrence data • May not be available to central registry unless reported from hospital registries

  25. Extending the Technology:Hospital Registry • More timely identification of eligible cases • Monitor changes in patient status that could trigger eligibility (e.g., recurrence, additional treatment) • Notification of managing physician when patient become eligible • Interactive or batch processing • Data set not limited to state requirements

  26. Extending the Technology:Physician Reporting • Immediate notification of potential eligibility • Passive, not active, screening by physician or staff

  27. Thank You • Dennis Deapen (LA CSP), for guiding the direction of this work with his helpful suggestions • Mark Allen (CCR), for providing the data extract from the Eureka database • Winny Roshala (CCR), for help in translating clinical trial requirements into ICD-O-3 codes • Tom Rawson (CDC), for making available GenEdit Plus (beta) for running the edits

  28. For more information: Alan R. Houser C/NET Solutions 1936 University Ave, Suite 112 Berkeley CA 94704-1024 (510) 549-8914 alanh@askcnet.org

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