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GnRH-antagonist treatment protocols: myths and facts

GnRH-antagonist treatment protocols: myths and facts. Georg Griesinger UK-SH, Campus Luebeck, Germany. Ideal control of the endogenous LH . German IVF registry 2006. u pdate of Griesinger et al., Hum Reprod 2005. GnRH-antagonists: 2nd choice?.

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GnRH-antagonist treatment protocols: myths and facts

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  1. GnRH-antagonist treatment protocols: myths and facts Georg Griesinger UK-SH, Campus Luebeck, Germany

  2. Ideal controloftheendogenous LH

  3. German IVF registry 2006 updateofGriesinger et al., Hum Reprod 2005

  4. GnRH-antagonists: 2nd choice? ………..moreoftenused in olderpatients Griesinger et al., Hum Reprod 2005 (n = 272.862 stimulation cycles)

  5. GnRH-antagonists: 2nd choice? ………moreoftenused in highercycleranks Griesinger et al., Hum Reprod 2005

  6. Myth No.1 • Antagonists have a lower efficacy

  7. No difference in clinical pregnancy rate Lower clinical pregnancy rate No difference in clinical pregnancy rate

  8. Unit of analysis:  the unit ofanalysis will differbetweenstudies

  9. theunitofanalysis will differbetween meta-analyses • denominator: patientsstartingtreatment • numerator: patientswithclinicalpregnancy rate differencebetweenagonistandantagonist: -5.0%, p<0.05Cochrane, 2002 • denominator: randomisedpatients • numerator: patientswith live birth rate differencebetweenagonistandantagonist: -3.8%, p= 0.08 Kolibianakis et al., 2006

  10. Whatis a clinically relevant difference? -10.0 -7.5 -5.0 -2.5 0 +2.5 +5.0 +7.5 +10.0 Δ in Live birth rate (%) Antagonist Agonist

  11. Graphicaldepictionofeffectindicator & confidenceinterval Point estimation Intervalestimation (e.g. 95% CI)

  12. Example - statement: „Because GnRH-agonist long protocol increases risks and burden for the patients it can only be considered useful if it increases the incidence of live birth by a minimum of 3% (at a baseline live birth rate of 20%)“ P-value <0.05 <0.05 <0.05 >0.05 Meta- Analysis: 1 2 3 4 +5 +7.5 +10 +12.5 +15 +17.5 1 +2.5 Live birth rate difference with agonist

  13. Statistical significanceclinical relevance rather than focusing on the p-value, one should check effect size and precision of estimate

  14. Sample size calculation Sample size Δ in live birth rate

  15. 3% difference 5% difference Sample size 8% difference

  16. 3% difference Meta-analysis 5% difference Sample size 8% difference

  17. Probabilityof live birth Odds ratio:0.859 p=0.085 Rate difference -2.7%

  18. * Update 2007: Live birth depending on year of publication ODDS RATIO= 0.88 95% CI : 0.76 to 1.03 RD= 2% 95% CI -5% to+ 0% Kolibianakis and Griesinger unpublished

  19. Myth No.2 • There are two meta-analyses, and the results are contradictory

  20. Abstract: the probability of live birth does not depend on the type of analogue used for pituitary suppression OR = 0.86 95% CI 0.72–1.02 P = 0.08 Abstract: ongoing pregnancy/live-birth rate were significantly lower in the antagonistgroup OR = 0.82 95% CI 0.68–0.97 P = 0.02

  21. Deriving clinically useful estimates from the results of a meta-analysis:Number needed to treat The number needed to treat (NNT) is an measure that indicates how many patients would require treatment to increase the expected number of cases of a defined endpoint by one. It is defined as the inverse of the absolute risk difference, and needs to be adjusted to the baseline event rates

  22. EXPECTED LIVE BIRTH RATE: 20% RR = 0.89, 95% CI 0.72–1.02, P = 0.08 NNTB = 41 (18 - ∞ ) OR = 0.82, 95% CI 0.68–0.97, P = 0.02 NNTB = ~36 (~17-250) (estimated) Although the p-value statistics differ, the two meta-analyses show very similar NNTB

  23. “Until recently, it was acceptable to look for (P values) in the results and the difference is considered significant if P <0.05. The magnitude of the effect of the intervention is of utmost clinical importance to evaluate the implications of this significance……. ……….we should remember that what is statistically significant might not always be clinically relevant” Prof. Aboulghar, Cairo University, Fertility Forum Masterclass Vienna, April 4th, 2008

  24. Secondaryoutcomes: • Duration of GnRH-analogue exposure - 19.5 days with antagonist • No. of COCs + 1.2 with agonist • Duration of gonadotropin exposure - 1.2 days with antagonist • OHSS (hospital admission) lower with antagonist (OR 0.46) • Incidence of LH rise higher with antagonist (OR 8.3) • Incidence of LH and progesterone rise higher with antagonist (OR 4.0) • (Gonadotropin consumption - 3 amp with antagonist) Kolibianakis et al., 2006

  25. Finding the balance NNT to harm EXPECTED OHSS III° INCIDENCE: 3.5% RR = 0.47, 95% CI 0.27–0.82, P = 0.01 NNT TO HARM with agonist long protocol = 53 (39 - 159)

  26. Myth No.3 • GnRH-antagonists work better in poor responders & PCOS

  27. Poor response & PCOS Update of Griesinger et al., RBMonline 2006

  28.  Current evidence does not favor any one pituitary suppression regimen…..  Insufficient evidence to support the routine use of any particular pituitary suppression regimen…..

  29. Summary • GnRH-agonist and GnRH-antagonist have equivalent efficacy • the differences observed in the published meta-analyses do not warrant to reject the notion of equivalence • There is no convincing evidence in the literature that GnRH-antagonists perform better than GnRH-agonist in poor response & PCOS patients

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