Hormone Replacement Therapy: Friend or Foe?

1. Hormone Replacement Therapy: Friend or Foe? Jillian GauldResearch Alliance in Math and ScienceComputational Sciences and Engineering Mentor: Kara L. Kruse August 13, 2008 Oak Ridge, Tennessee

2. Outline • Purpose of project • Introduction to atherosclerosis, femoropopliteal (fem-pop) bypass, intimal hyperplasia, and the role of hormone replacement therapy • Project goals • Explanation of data used for study • Statistical analyses utilized • Results from analyses • Interpretation of results and conclusions • Wrap-up

3. Purpose • Conduct retrospective study • Support Dr. Timaran’s paper with statisticical analyses • Provide parameters for future prospective study

4. Introduction • Atherosclerosis • Femoropopliteal Bypass • Intimal Hyperplasia • Hormone Replacement Therapy’s Role

5. What is atherosclerosis? • Build-up of fatty plaques within an artery, leading to its hardening • High cholesterol, high blood pressure, diabetes, and smoking contribute • Causes strokes, heart attack, congestive heart failure, and death • Statin drugs, diet changes, angioplasty, and femoropopliteal bypass http://www.Wikipedia.org

6. Femoropopliteal Bypass • Femoropopliteal bypass surgery used to redirect blood flow to avoid diseased vessels • Surgeon uses either different leg vein, or man-made graft material http://www.reshealth.org

7. Intimal Hyperplasia • Universal response of artery to injury, such as fem-pop bypass • Influx of smooth muscle cells from media to intima • Restenosis, or post-surgical narrowing of artery, occurs in about 1/3 of cases http://www.nature.com

8. Intimal Hyperplasia • I = Intima • M = Media • A = Adventitia

9. Hormone Replacement Therapy’s Effect • Estrogen commonly thought to have vascular protective effects • UT Medical Center doctors observed contradicting evidence • Patients on HRT fare worse after fem-pop bypass

10. Project Goals • Use statistic analysis to support theory that HRT is not beneficial for fem-pop bypass patients • Analyze results to help design future prospective study by identifying significant variables, and eliminating the insignificant

11. Materials and Methods • 68 patient records • JMP software • Tests included Kaplan-Meier survival analysis, Chi-square analysis, student’s t-test, mosaic plots, scatterplot matrices • Kaplan-Meier and chi-square analyses most conclusive

12. Terminology • Patients classified according to patency (degree artery remains open) • Primary Patency – no interventions needed • Primary Assisted Patency – intervention needed • Secondary Patency – thrombolytic occurrence (blood clot), intervention necessary • Failure – no flow detected in graft, intervention was not available or limb was removed due to failure

13. Kaplan-Meier Surival Analysis • Plots graft survival percentage over time • Censored data (patients who dropped out of study) • Log-rank test and Wilcoxon test • Allows comparison of two different populations Primary Patency Rates by Graft Type Log rank = 0.1699

14. Chi-square Test for Independence • Analyzes relationship between sets of categorical data • Low p-value indicates strong correlation between variables • Helps identify and separate significant variables • Doesn’t represent type of correlation P-value = 0.0343 Graft type by patency

15. Mosaic Plot • Visually represents categorical data. • Used along with chi-square • Shows what correlation the p-value expresses Mental/Mood Disorders vs. Graft Patency P-value= .0343

16. Results • Inconclusive results: • (Not enough data, obvious lurking variables) • Heart problems • Estrogen vs. estrogen plus progesterone Significant variables (some correlation found-should be included in future study) • Mental/mood disorder medication • Graft type (vein or synthetic) • High cholesterol • Arthritis • Hysterectomy Insignificant variables: (little correlation found- should not be put in a future study) • Above knee versus below knee bypass • Infections during surgery • Myocardial infarctions • Allergies • Stomach problems

17. Hormone Replacement Therapy • Within first three years, patients tend to do better on HRT • After 3 years, HRT patients have lower primary patency rates (worse outcome) • Does not necessarily support theory that HRT is harmful Primary patency of patients divided by use of HRT Log-rank = 0.6322 P-value= 0.5770

18. Mental/Mood Disorder Medication • Chi-square analysis for patency rates by mental/mood disorder medications • P-value = 0.0343 • Patients on medications for mental or mood disorders and their primary patency rates • Log-Rank = 0.1041

19. Graft Type • Analyses comparing use of goretex graft versus vein graft • P-value = 0.0817 • Primary patency rates of patients with goretex graft versus vein graft • Log-rank = 0.1699

20. High Cholesterol • Patency rates divided by patients’ cholesterol levels • P-value = 0.1034 • Primary patency rates over time • Log-rank = 0.0153

21. Arthritis • Patency rates divided by arthritis • P-value = 0.3673 • Primary patency rates over time • Log-rank = 0.1030

22. Conclusions • Found statistically significant data on certain variables that should be included in a future prospective study • Eliminated insignificant variables • Helped set parameters for future prospective study • Prospective study will help advance knowledge of HRT’s role

23. Acknowledgments The Research Alliance in Math and Science program is sponsored by the Office of Advanced Scientific Computing Research, U.S. Department of Energy. The work was performed at the Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC under Contract No. De-AC05-00OR22725. This work has been authored by a contractor of the U.S. Government, accordingly, the U.S. Government retains a non-exclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purposes. Acknowledgement of contributions to research: Oak Ridge National Laboratory: Sara Wezensky University of Tennessee Medical Center: Oscar Grandas, MD, Fernando Aycinena, MD, Melinda Klar, RN.

24. Questions? Comments?Clarifications? Questions 24 Managed by UT-Battellefor the Department of Energy UTBOG_Computing_0801