how to determine your patient chance of having spontaneous pregnancy? how to evaluate chance of success of IVF ? it should be based on objective way. this talk may help to illustrate this
9. Why Models!!
• Prediction models are intended to help
gynaecologists in patient communication and
decision making about treatment
10. How to Choose: Expectant
management or intervention
• Prediction models for Chance to concieve
naturally (home conception) (treatment
independent pregnancy)
• Prediction models for pregnancy after IVF
• Prediction models for pregnancy after IUI
16. Clinical consequences
• Couples with prognosis <30% = IVFCouples with prognosis <30% = IVF
• Couples with prognosis > 40% =Couples with prognosis > 40% =
expectant managementexpectant management
• Couples with prognosis 30-40% = IUICouples with prognosis 30-40% = IUI
17. Expectant management or
intervention
• Prediction models for Chance to concieve
naturally (home conception) (treatment
independent pregnancy)
• Prediction models for pregnancy after IVF
• Prediction models for pregnancy after IUI
18. Protocols for IVF
GnRH AntagonistGnRH Antagonist
ProtocolsProtocols
GnRHGnRH AgonistAgonist
ProtocolsProtocols
225 IU per day225 IU per day
(150 IU Europe)(150 IU Europe) Individualized Dosing of FSH/HMGIndividualized Dosing of FSH/HMG
250250 µµg per day antagonistg per day antagonist
Individualized Dosing of FSH/HMGIndividualized Dosing of FSH/HMG
GnRHa 1.0 mg per dayGnRHa 1.0 mg per day
up to 21 daysup to 21 days
0.5 mg per day of GnRHa0.5 mg per day of GnRHa
225 IU per day225 IU per day
(150 IU Europe)(150 IU Europe)
Day 6Day 6
of FSH/HMGof FSH/HMG
DayDay
ofof hCGhCG
Day 1Day 1
of FSH/HMGof FSH/HMG
Day 6Day 6
of FSH/HMGof FSH/HMG
DayDay
of hCGof hCG
7 – 8 days7 – 8 days
after estimated ovulationafter estimated ovulation
Down regulationDown regulation
Day 2 or 3Day 2 or 3
of mensesof menses
Day 1Day 1
FSH/HMGFSH/HMG
19. Which day!!!
• Day of start of cycle
• Day of start of stimulation
• Day of OPU
• Day of ET
• the time of embryo transfer will be more
accurate
• but limited since the couple has already gone
through the whole process of IVF.
20. Ideal model
• the probability of live birth in an IVF cycle
prior to start of ovarian stimulation.
21. Day of start: Baseline factors
• female age,
• duration of infertility,
• primary cause of infertility,
• duration of GnRH agonist use,
• Hormonal level
• the number of previous IVF cycle
22. • The age of the woman is still considered to be
the most important predictor of IVF success
(Broekmans and Klinkert, 2004).
23. • increasing duration of infertility has also been
shown to be negative impact , even after
adjustment for age, whereas previous
pregnancy increases the likelihood of success
(Collins et al., 1995; Templeton et al,1996).
24. • couples with different infertility diagnoses will
likely have different probabilities of achieving
a live birth
25. Ovarian reserve tests
• Basal FSH, inhibin B, and anti-Müllerian
hormone concentrations, as well as antral
follicles count can be used to measure the
ovarian reserve (Broekmans et al., 2006; Kwee
et al., 2008).
26. AMH
• If kits are available, AMH measurement could
be the most useful in the prediction of ovarian
response in anovulatory women.
• It is done at any day of cycle
• It is too expensive
• Exact normal levels not yet well agreed upon
27. ?Pregnancy
• correlation with the degree of response to
COH, but identifying poor responders by
means of these tests has low prognostic value
in relation to the chance of live birth after IVF
Broekmans et al. (2006)
28. How to build a model!
• Multivariate logistic regression analysis for
previous prognostic variables to create
prediction models of ovarian response and/or
ongoing pregnancy has been used to a lesser
extent (e.g., Bancsi et al., 2002).
29. Existing Models
• Most statistical models for prediction of IVF
outcome use both prestimulation parameters
and data obtained during the treatment, such
as data on embryos
32. Calculation
• The predicted probability (P) of achieving a live birth
after IVF was calculated using the Templeton the
model:
• Where y was defined as y = –2.028 + [0.00551x(age – 16)2] –
[0.00028x(age – 16)3] + [i – (0.0690x no. of unsuccessful IVF attempts)] –
(0.0711xtubal subfertility) + (0.7587xlive birth after IVF) + (0.2986 x
previous pregnancy after IVF which did not result in a live birth) +
(0.2277x live birth which was not a result of IVF) + (0.1117x previous
pregnancy, not after IVF and which did not result in a live birth).
35. • classified for each woman into one of three
groups, i.e.,
• (i) predictor of good prognosis
• (ii) intermediate prognosis
• (iii) predictor of poor prognosis.
36. Expectant management or
intervention
• Prediction models for Chance to concieve
naturally (home conception) (treatment
independent pregnancy)
• Prediction models for pregnancy after IUI
• Prediction models for pregnancy after IVF
37. Prognostic factors of pregnancy in
intrauterine insemination
• Women with intermediate prognosis
43. Take Home Message
• Prediction models are now available and
ready for use
• Female age is the overwhelming factor
affecting prediction models
• The prognosis should be discussed clearly with
the patients based on scientific evidence and
existing models.