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Prognostic models
in
Infertility
Hesham Al-Inany, M.D, PhD
kaainih@yahoo.com
Basic fertility work up
referral gyn
History
Physical examination
Cycle evaluation
Ovulation
Semen analysis
? PCT
Tubal
patency:
CAT
HSG
DLS
FSH, E2
AFC
Causes of infertility
• Azoospermia
• Anovulation
• Double sided tubal occlusion
• Sexual dysfunction
Causes of subfertility
• Unexplained subfertility
• One-sided tubal pathology
• Cervical factor subfertility
• Endometriosis
• Decreased semen quality
• Decreased intercourse frequency
Evers JL, Lancet 2002
Infertility or subfertility?
Clinical problem
• Distinction between couples who need
treatment and couples who are likely to
conceive spontaneously
Clinical Problem II
• You scheduled a couple to do ICSI and the
woman asked you : What is my chance to get
a baby after doing ICSI???
Gynaecologists differ widely in estimating
pregnancy chances of subfertile couples
Van der Steeg et al.,HR, 2006
Why Models!!
• Prediction models are intended to help
gynaecologists in patient communication and
decision making about treatment
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
Eimers Collins Snick Hunault
Female age + + - +
Duration subfertility + + + +
F.A. man
Urethritis vg. man
+
-
-
-
-
-
-
-
prim/ sec subfertility + + - +
Anovulation - - + -
Tubal pathology - + + -
Semen-analysis + + - +
Endometriosis - + - -
PCT
Referral status
+ - + -/+
+
Hunault et al. HR 2004Hunault et al. HR 2004
Prediction models for spontaneous pregnancy
Calculation Prognosis
P = 1-0,0166P = 1-0,0166EXP(-0,053*EXP(-0,053*ageage-0,152*-0,152*durationduration-0,447*-0,447*prim/secprim/sec+0.0035*+0.0035*prog.motprog.mot-0,949*-0,949*PCTPCT-0,321*-0,321*referralreferral))
External validation
the agreement between predicted
probabilities and the outcome event rates
CalibrationCalibration
Calibration plot for unexplained subfertility
Synthesis model without PCT
Predicted probability
0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0
Observedprobability
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
10 groups of N~260
Calibration Synthesis model
Van der Steeg HR 2007
http://http://www.amc.nl/prognosticmodelhttp://http://www.amc.nl/prognosticmodel
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
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
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
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.
Ideal model
• the probability of live birth in an IVF cycle
prior to start of ovarian stimulation.
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
• The age of the woman is still considered to be
the most important predictor of IVF success
(Broekmans and Klinkert, 2004).
• 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).
• couples with different infertility diagnoses will
likely have different probabilities of achieving
a live birth
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).
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
?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)
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).
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
IVF prediction models
Prediction models Outcome Discrimination Calibration
Templeton (1996( IVF 0.63 good
Prognostic models in infertility
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).
IVF prediction models
Prediction models Outcome Discrimination Calibration
Templeton (1996( IVF 0.63 good
Lintsen, A.M.E. et al. Hum. Reprod. 2007
• classified for each woman into one of three
groups, i.e.,
• (i) predictor of good prognosis
• (ii) intermediate prognosis
• (iii) predictor of poor prognosis.
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
Prognostic factors of pregnancy in
intrauterine insemination
• Women with intermediate prognosis
IUI prediction model
prediction models Outcome Discrimination Calibration
Steures (2004( IUI 0.59 good
39
PICO
Patient
woman, 34 years, 2ys 1ry
unexplained inf.
Intervention IUI
Comparison wait
Outcome Pregnancy
Prognostic models in infertility
Prognostic models in infertility
months to ongoing pregnancy
363024181260
Cumulativeongoingpregnancyrate
1,0
0,8
0,6
0,4
0,2
0,0
IUI-censored
exp-censored
IUI
exp
exp=1, IUI=2
-- delayed treatment
-- early treatment
RR: 1,0 (CI: 0,86-1,2(
N= 90 (71%)
N= 90 (71%)
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.
However
• Patient preferences
• Private vs medical insurance
• Patient values
http://http://www.amc.nl/prognosticmodelhttp://http://www.amc.nl/prognosticmodel
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
Lintsen, A.M.E. et al. Hum. Reprod. 2007
THANK You

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Prognostic models in infertility

  • 2. Basic fertility work up referral gyn History Physical examination Cycle evaluation Ovulation Semen analysis ? PCT Tubal patency: CAT HSG DLS FSH, E2 AFC
  • 3. Causes of infertility • Azoospermia • Anovulation • Double sided tubal occlusion • Sexual dysfunction
  • 4. Causes of subfertility • Unexplained subfertility • One-sided tubal pathology • Cervical factor subfertility • Endometriosis • Decreased semen quality • Decreased intercourse frequency
  • 5. Evers JL, Lancet 2002 Infertility or subfertility?
  • 6. Clinical problem • Distinction between couples who need treatment and couples who are likely to conceive spontaneously
  • 7. Clinical Problem II • You scheduled a couple to do ICSI and the woman asked you : What is my chance to get a baby after doing ICSI???
  • 8. Gynaecologists differ widely in estimating pregnancy chances of subfertile couples Van der Steeg et al.,HR, 2006
  • 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
  • 11. Eimers Collins Snick Hunault Female age + + - + Duration subfertility + + + + F.A. man Urethritis vg. man + - - - - - - - prim/ sec subfertility + + - + Anovulation - - + - Tubal pathology - + + - Semen-analysis + + - + Endometriosis - + - - PCT Referral status + - + -/+ + Hunault et al. HR 2004Hunault et al. HR 2004 Prediction models for spontaneous pregnancy
  • 12. Calculation Prognosis P = 1-0,0166P = 1-0,0166EXP(-0,053*EXP(-0,053*ageage-0,152*-0,152*durationduration-0,447*-0,447*prim/secprim/sec+0.0035*+0.0035*prog.motprog.mot-0,949*-0,949*PCTPCT-0,321*-0,321*referralreferral))
  • 13. External validation the agreement between predicted probabilities and the outcome event rates CalibrationCalibration
  • 14. Calibration plot for unexplained subfertility Synthesis model without PCT Predicted probability 0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0 Observedprobability 0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0 10 groups of N~260 Calibration Synthesis model Van der Steeg HR 2007
  • 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
  • 30. IVF prediction models Prediction models Outcome Discrimination Calibration Templeton (1996( IVF 0.63 good
  • 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).
  • 33. IVF prediction models Prediction models Outcome Discrimination Calibration Templeton (1996( IVF 0.63 good
  • 34. Lintsen, A.M.E. et al. Hum. Reprod. 2007
  • 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
  • 38. IUI prediction model prediction models Outcome Discrimination Calibration Steures (2004( IUI 0.59 good
  • 39. 39 PICO Patient woman, 34 years, 2ys 1ry unexplained inf. Intervention IUI Comparison wait Outcome Pregnancy
  • 42. months to ongoing pregnancy 363024181260 Cumulativeongoingpregnancyrate 1,0 0,8 0,6 0,4 0,2 0,0 IUI-censored exp-censored IUI exp exp=1, IUI=2 -- delayed treatment -- early treatment RR: 1,0 (CI: 0,86-1,2( N= 90 (71%) N= 90 (71%)
  • 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.
  • 44. However • Patient preferences • Private vs medical insurance • Patient values
  • 46. 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
  • 47. Lintsen, A.M.E. et al. Hum. Reprod. 2007