prognostic models

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Prognostic models in Infertility

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evidence based prognosis is an important part of EBM. It helps counselling your patients

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Page 1: Prognostic models

Prognostic models in

Infertility

Page 2: Prognostic models

Basic fertility work up

referral gyn

HistoryPhysical examination

Cycle evaluation

Ovulation

Semen analysis

? PCT

Tubalpatency:

CATHSGDLS

FSH, E2AFC

Page 3: Prognostic models

Causes of infertility

• Azoospermia• Anovulation• Double sided tubal occlusion• Sexual dysfunction

Page 4: Prognostic models

Causes of subfertility

• Unexplained subfertility• One-sided tubal pathology• Cervical factor subfertility• Endometriosis• Decreased semen quality • Decreased intercourse frequency

Page 5: Prognostic models

Evers JL, Lancet 2002

Infertility or subfertility?

Page 6: Prognostic models

Clinical problem

• Distinction between couples who need treatment and couples who are likely to conceive spontaneously

Page 7: Prognostic models

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???

Page 8: Prognostic models

Gynaecologists differ widely in estimating pregnancy chances of subfertile couples

Van der Steeg et al.,HR, 2006

Page 9: Prognostic models

Why Models!!

• Prediction models are intended to help gynaecologists in patient communication and decision making about treatment

Page 10: Prognostic models

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

Page 11: Prognostic models

EimersCollinsSnick HunaultFemale age+ + - +Duration subfertility+ + + +F.A. manUrethritis vg. man

+

-

-

-

-

-

-

-prim/ sec subfertility+ + - +Anovulation- - + -Tubal pathology- + + -Semen-analysis + + - +Endometriosis- + --PCTReferral status

+ - + -/+

+

Hunault et al. HR 2004Hunault et al. HR 2004

Prediction models for spontaneous pregnancy

Page 12: Prognostic models

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))

Page 13: Prognostic models

External validation

the agreement between predicted probabilities and the outcome event rates

CalibrationCalibration

Page 14: Prognostic models

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

Obs

erve

d pr

obab

ility

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

Page 15: Prognostic models

http://http://www.amc.nl/prognosticmodelhttp://http://www.amc.nl/prognosticmodel

Page 16: Prognostic models

Clinical consequences

• Couples with prognosis <30% = IVFCouples with prognosis <30% = IVF• Couples with prognosis > 40% = Couples with prognosis > 40% =

expectant management expectant management • Couples with prognosis 30-40% = IUICouples with prognosis 30-40% = IUI

Page 17: Prognostic models

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

Page 18: Prognostic models

Protocols for IVF GnRH AntagonistGnRH AntagonistProtocolsProtocols

GnRH GnRH AgonistAgonistProtocolsProtocols

225 IU per day225 IU per day(150 IU Europe)(150 IU Europe) Individualized Dosing of FSH/HMGIndividualized Dosing of FSH/HMG

250 250 g per day antagonistg per day antagonist

Individualized Dosing of FSH/HMGIndividualized Dosing of FSH/HMG

GnRHa 1.0 mg per day GnRHa 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 6of FSH/HMGof FSH/HMG

DayDay

of of hCGhCG

Day 1 Day 1 of FSH/HMGof FSH/HMG

Day 6Day 6of FSH/HMGof FSH/HMG

DayDayof hCGof hCG

7 – 8 days7 – 8 daysafter estimated ovulationafter estimated ovulation

Down regulationDown regulation

Day 2 or 3Day 2 or 3of mensesof menses

Day 1 Day 1 FSH/HMGFSH/HMG

Page 19: Prognostic models

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.

Page 20: Prognostic models

Ideal model

• the probability of live birth in an IVF cycle prior to start of ovarian stimulation.

Page 21: Prognostic models

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

Page 22: Prognostic models

• The age of the woman is still considered to be the most important predictor of IVF success (Broekmans and Klinkert, 2004).

Page 23: Prognostic models

• 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).

Page 24: Prognostic models

• couples with different infertility diagnoses will likely have different probabilities of achieving a live birth

Page 25: Prognostic models

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).

Page 26: Prognostic models

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

Page 27: Prognostic models

?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)

Page 28: Prognostic models

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).

Page 29: Prognostic models

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

Page 30: Prognostic models

IVF prediction modelsPrediction modelsOutcomeDiscriminationCalibration

Templeton (1996)IVF0.63good

Page 31: Prognostic models
Page 32: Prognostic models

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).

                                        

Page 33: Prognostic models

IVF prediction modelsPrediction modelsOutcomeDiscriminationCalibration

Templeton (1996)IVF0.63good

Page 34: Prognostic models

Lintsen, A.M.E. et al. Hum. Reprod. 2007

Page 35: Prognostic models

• classified for each woman into one of three groups, i.e.,

• (i) predictor of good prognosis• (ii) intermediate prognosis • (iii) predictor of poor prognosis.

Page 36: Prognostic models

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

Page 37: Prognostic models

Prognostic factors of pregnancy in intrauterine insemination

• Women with intermediate prognosis

Page 38: Prognostic models

IUI prediction modelprediction modelsOutcomeDiscriminationCalibration

Steures (2004)IUI0.59good

Page 39: Prognostic models

39

PICO

Patientwoman, 34 years, 2ys 1ry unexplained inf.

InterventionIUI

Comparisonwait

OutcomePregnancy

Page 40: Prognostic models
Page 41: Prognostic models
Page 42: Prognostic models

months to ongoing pregnancy363024181260

Cum

ula

tive

ongoin

g p

regnancy

rat

e

1,0

0,8

0,6

0,4

0,2

0,0

IUI-censoredexp-censoredIUIexp

exp=1, IUI=2

-- delayed treatment

-- early treatment

RR: 1,0 (CI: 0,86-1,2)

N= 90 (71%)N= 90 (71%)

Page 43: Prognostic models

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.

Page 44: Prognostic models

However

• Patient preferences• Private vs medical insurance• Patient values

Page 45: Prognostic models

http://http://www.amc.nl/prognosticmodelhttp://http://www.amc.nl/prognosticmodel

Page 46: Prognostic models

Clinical consequences

• Couples with prognosis <30% = IVFCouples with prognosis <30% = IVF• Couples with prognosis > 40% = Couples with prognosis > 40% =

expectant management expectant management • Couples with prognosis 30-40% = IUICouples with prognosis 30-40% = IUI

Page 47: Prognostic models

Lintsen, A.M.E. et al. Hum. Reprod. 2007

Page 48: Prognostic models

Basics

Clinical Expertise

Prediction Model Patient Preferences

Page 49: Prognostic models

THANK You