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Confronting Common Challenges in Managing Laboratory Data

Confronting Common Challenges in Managing Laboratory Data

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Confronting Common Challenges in Managing Laboratory Data

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  1. Confronting Common Challenges in Managing Laboratory Data ~~ May 16, 2008 CONFIDENTIAL

  2. Confronting Common Challenges in Managing Laboratory Data Laurie Callen Senior Technical Manager Synta Pharmaceuticals, Corp Cory Donovan Manager, Database Programming Prometrika Ajay Sadhwani Executive Director – Strategic Product Development Premiere Research Group Boston CONFIDENTIAL

  3. Deal with Lab Data? I’d rather…. CONFIDENTIAL

  4. With just a little planning.. CONFIDENTIAL

  5. This…. CONFIDENTIAL

  6. Not this…. CONFIDENTIAL

  7. Confronting Common Challenges in Managing Laboratory Data What did we have to work with? • One Clinical Program (One Therapeutic Agent) • Multiple Studies, Multiple Indications CONFIDENTIAL

  8. Summary of ‘in-house’ studies CONFIDENTIAL

  9. Confronting Common Challenges in Managing Laboratory Data ~Plus~ Many legacy studies • Multiple vendors • Multiple database systems • With varied data structures CONFIDENTIAL

  10. Tools to Collect the Local Lab Data for ‘in-house studies’ 12 pt NIH sponsored study No CRF – Lab Reports straight from the lab 22 pt RA study Local Lab CRF CONFIDENTIAL

  11. Example Local Lab CRF CONFIDENTIAL

  12. Central Lab Data from Vendor Over 50 variables • Study Information: Id, Protocol Code • Patient Info: Id, Number, Demography Info • Lab Test Name, Lab Test Value, Date, Time, Ranges, Normals, Converted Values, Alert Flags, etc. etc. CONFIDENTIAL

  13. Confronting Common Challenges in Managing Laboratory Data CONFIDENTIAL

  14. Confronting Common Challenges in Managing Laboratory Data Whatdid we have to do? • Utilize information gathered from past studies; • Manage, maintain data for the new studies using Oracle Clinical and Oracle Clinical NormLab2 Module CONFIDENTIAL

  15. Confronting Common Challenges in Managing Laboratory Data Whatdid we have to do? (con’t) • Employ CDISC/SDTM naming conventions and structure within native Oracle Clinical….. ‘as much as possible’ • Minimize pre and post-processing of all lab data CONFIDENTIAL

  16. Confronting Common Challenges in Managing Laboratory Data Whatdid we have to do? (con’t) • Needed to implement uniform Safety Analysis and Reporting requirements • Ultimately create a uniform, consistent dataset for all studies to be used by all team members CONFIDENTIAL

  17. Confronting Common Challenges in Managing Laboratory Data How were we going to do it? 1. We needed to Prioritize needs Statistical Reporting, CDISC/SDTM, Data Cleaning, Lab Loading 2. We needed to Identify the required or mandatory fields for all of our needs Analysis, CDISC/SDTM, OC, NormLab2 CONFIDENTIAL

  18. Confronting Common Challenges in Managing Laboratory Data How were we going to do it (con’t)? 3. Determine the “easy wins” Example: all labs datasets need a lab test name and lab unit field 4. Identify the redundancies and overlaps Remove unnecessary data fields CONFIDENTIAL

  19. Confronting Common Challenges in Managing Laboratory Data How were we going to do it (con’t)? 5. Compromise when necessary CONFIDENTIAL

  20. As a result.. A few major themes I. Develop a laboratory units/ normals management strategy II. Develop a uniform data structure that would be used for analysis, reporting and working with Oracle Clinical/NormLab2 and CDISC/SDTM CONFIDENTIAL

  21. I. Develop a Laboratory Unit/Normals Maintenance and Management Strategy Conversions Local Lab Units Lab Test Names Local Lab Ranges Date of Birth Standard Units Gender CONFIDENTIAL

  22. Key Components of Lab Unit/Normals Maintenance and Management Strategy • Step 1: Create Library of Lab Test Names • Step 2: Plan for Units/Conversions • Step 3: Define Standard Units • Step 4: Create Library of Local Lab Ranges • Step 5: Identify Key Subject Characteristics CONFIDENTIAL

  23. Step 1: Create Library of Lab Test Names • Names will be used across all studies • Document naming convention • Full name/Abbreviation/Other • Source documentation • Important to have library • Critical to standardization • Most efficient for maintenance CONFIDENTIAL

  24. Step 1: Create Library of Lab Test Names (cont.) CONFIDENTIAL

  25. Step 2: Plan for Units/Conversions Create repository of all likely/possible units for each test CONFIDENTIAL

  26. Step 2: Plan for Units/Conversions (cont.) • Create conversions CONFIDENTIAL

  27. Step 3: Define Standard Units • Final results will always be reported in these units • Standard units • Company wide • Program/Project/Study/Client specific • Requires input and approval of all affected parties CONFIDENTIAL

  28. Step 3: Define Standard Units (cont.) Company X Standard Units CONFIDENTIAL

  29. Step 4: Create Library of Local Lab Ranges Lab X Lab Y CONFIDENTIAL

  30. Step 4: Create Library of Local Lab Ranges (cont) • Lab units for Glucose will always be evaluated as • mg/dL for Lab X • mmol/L for Lab Y • With a comprehensive lab management approach, CRF printed/recorded units don’t really matter CONFIDENTIAL

  31. Why Printed/Entered CRF Units Won’t Matter Lab Y x x CONFIDENTIAL

  32. Step 5: Identify Key Subject Characteristics • Key Data Points • Sex • Date of Birth • Used for • Calculating age at time of exam * • Applying correct lab range * Need lab test date in order to calculate age at time of exam CONFIDENTIAL

  33. Ready to Go 1.Get Sex and DOB 2. Calculate age at time of test LAB Y RANGES CONFIDENTIAL

  34. Ready to Go (cont.) Confirm standard unit Perform conversion to Standard Units (in this case from mmol/L to mg/dL) 4.1 X 17.9 = 73.4 mg/dL CONFIDENTIAL

  35. Ready to Go (cont.) CONFIDENTIAL

  36. Importance of This Approach Standard Units Conversions Local Lab Ranges Test names Possible units CONFIDENTIAL

  37. Special Considerations: Character Results • Text Responses • RBC Morphology, Pregnancy • Pos/Neg, Normal/Abnormal, Clear/Cloudy • Assign numeric value to results • Positive =1, Negative= -1 • Normal=1, Abnormal= -1 • Assign numeric range CONFIDENTIAL

  38. Character Results (cont.) 1. Create equivalent numeric results 2. Create numeric ranges CONFIDENTIAL

  39. II. Develop a uniform data structure that would be used for analysis CONFIDENTIAL

  40. Confronting Common Challenges in Managing Laboratory Data CONFIDENTIAL

  41. Confronting Common Challenges in Managing Laboratory Data CONFIDENTIAL

  42. Confronting Common Challenges in Managing Laboratory Data CONFIDENTIAL

  43. Confronting Common Challenges in Managing Laboratory Data CONFIDENTIAL

  44. Confronting Common Challenges in Managing Laboratory Data CONFIDENTIAL

  45. Confronting Common Challenges in Managing Laboratory Data **Raw Data Structure used in OC/NormLab2 **Additional Post-processing necessary to make fully CDISC/SDTM compliant CONFIDENTIAL

  46. Confronting Common Challenges in Managing Laboratory Data Some special considerations • Unpredictability of local lab data reports: (tests performed, test names, units reported, etc.) • Lab CRF utilizing ‘codelist’ values, or free text “Other lab test performed” • Investigator’s ‘Clinical Significant’ vs. Programmed Alert Flags CONFIDENTIAL

  47. Confronting Common Challenges in Managing Laboratory Data Some special considerations, con’t • Local lab units vs. Central lab units • Not Done/ Missing Data • Comment fields CONFIDENTIAL

  48. Confronting Common Challenges in Managing Laboratory Data Some special considerations, con’t CONFIDENTIAL

  49. Confronting Common Challenges in Managing Laboratory Data In Summary • Identify & Understand the needs/requirements (Early and Often) • Plan Ahead – develop a ‘project plan’ • Identify the tools/resources • Key goal from outset should be standardization • Units/Normals Management • CDISC/Data Structure • Develop Expertise CONFIDENTIAL

  50. Confronting Common Challenges in Managing Laboratory Data Thank you! lcallen@syntapharma.com cdonovan@prometrika.com CONFIDENTIAL