Novel nomogram-based integrated gonadotropin therapy individualization in in vitro fertilization/intracytoplasmic sperm injection: A modeling approach

다국어 초록 (Multilingual Abstract)

Objective: This study aimed to characterize a validated model for predicting oocyte retrieval in controlled ovarian stimulation (COS) and to construct model-based nomograms for assistance in clinical decision-making regarding the gonadotropin protocol and dose.Methods: This observational, retrospective, cohort study included 636 women with primary unexplained infertility and a normal menstrual cycle who were attempting assisted reproductive therapy for the first time. The enrolled women were split into an index group (n=497) for model building and a validation group (n=139). The primary outcome was absolute oocyte count. The dose-response relationship was tested using modified Poisson, negative binomial, hybrid Poisson-Emax, and linear models. The validation group was similarly analyzed, and its results were compared to that of the index group. Results: The Poisson model with the log-link function demonstrated superior predictive performance and precision (Akaike information criterion, 2,704; λ=8.27; relative standard error (λ)=2.02%). The covariate analysis included women’s age (p<0.001), antral follicle count (p<0.001), basal follicle-stimulating hormone level (p<0.001), gonadotropin dose (p=0.042), and protocol type (p=0.002 and p<0.001 for short and antagonist protocols, respectively). The estimates from 500 bootstrap samples were close to those of the original model. The validation group (n=139) showed model assessment metrics comparable to the index model. Based on the fitted model, a static nomogram was built to improve visualization. In addition, a dynamic electronic tool was created for convenience of use.Conclusion: Based on our validated model, nomograms were constructed to help clinicians individualize the stimulation protocol and gonadotropin doses in COS cycles.

참고문헌 (Reference)

1 Van Belle V, "Visualizing risk prediction models" 10 : e0132614-, 2015

2 Lukaszuk K, "Use of ovarian reserve parameters for predicting live births in women undergoing in vitro fertilization" 168 : 173-177, 2013

3 Islam Y, "The value of different ovarian reserve tests in the prediction of ovarian response in patients with unexplained infertility" 21 : 69-74, 2016

4 Drakopoulos P, "The effect of dose adjustments in a subsequent cycle of women with suboptimal response following conventional ovarian stimulation" 9 : 361-, 2018

5 Altman DG, "The cost of dichotomising continuous variables" 332 : 1080-, 2006

6 Magnusson A, "The addition of anti-Müllerian hormone in an algorithm for individualized hormone dosage did not improve the prediction of ovarian response-a randomized, controlled trial" 32 : 811-819, 2017

7 Ou J, "Short versus long gonadotropin-releasing hormone analogue suppression protocols in IVF/ICSI cycles in patients of various age ranges" 10 : e0133887-, 2015

8 Toftager M, "Risk of severe ovarian hyperstimulation syndrome in GnRH antagonist versus GnRH agonist protocol : RCT including 1050 first IVF/ICSI cycles" 31 : 1253-1264, 2016

9 Tsakos E, "Predictive value of anti-müllerian hormone, follicle-stimulating hormone and antral follicle count on the outcome of ovarian stimulation in women following GnRH-antagonist protocol for IVF/ET" 290 : 1249-1253, 2014

10 Vuong TN, "Predictive value of AMH, FSH and AFC for determining ovarian response in Vietnamese women undergoing assisted reproductive technologies : a prospective study" 3 : 151-, 2015

11 Oehninger S, "Predictive factors for ovarian response in a corifollitropin alfa/GnRH antagonist protocol for controlled ovarian stimulation in IVF/ICSI cycles" 13 : 117-, 2015

12 Arce JC, "Ovarian response to recombinant human follicle-stimulating hormone : a randomized, antimüllerian hormone-stratified, dose-response trial in women undergoing in vitro fertilization/intracytoplasmic sperm injection" 102 : 1633-1640, 2014

13 Moon KY, "Nomogram to predict the number of oocytes retrieved in controlled ovarian stimulation" 43 : 112-118, 2016

14 Al-Azemi M, "Multi-marker assessment of ovarian reserve predicts oocyte yield after ovulation induction" 26 : 414-422, 2011

15 Plan EL, "Modeling and simulation of count data" 3 : e129-, 2014

16 Siristatidis C, "Mild versus conventional ovarian stimulation for poor responders undergoing IVF/ICSI" 31 : 231-237, 2017

17 Farmer KC, "Methods for measuring and monitoring medication regimen adherence in clinical trials and clinical practice" 21 : 1074-1090, 1999

18 Felmlee MA, "Mechanism-based pharmacodynamic modeling" 929 : 583-600, 2012

19 Nyboe Andersen A, "Individualized versus conventional ovarian stimulation for in vitro fertilization: a multicenter, randomized, controlled, assessor-blinded, phase 3 noninferiority trial" 107 : 387-396, 2017

20 La Marca A, "Individualization of the FSH starting dose in IVF/ICSI cycles using the antral follicle count" 6 : 11-, 2013

21 La Marca A, "Individualization of controlled ovarian stimulation in IVF using ovarian reserve markers : from theory to practice" 20 : 124-140, 2014

22 Broekmans FJ, "Individualization of FSH doses in assisted reproduction: facts and fiction" 10 : 181-, 2019

23 Lensen SF, "Individualised gonadotropin dose selection using markers of ovarian reserve for women undergoing in vitro fertilisation plus intracytoplasmic sperm injection(IVF/ICSI)" 2 : CD012693-, 2018

24 Pinto F, "Impact of GnRH ovarian stimulation protocols on intracytoplasmic sperm injection outcomes" 7 : 5-, 2009

25 Rustamov O, "How much variation in oocyte yield after controlled ovarian stimulation can be explained? A multilevel modelling study" 2017 : hox018-, 2017

26 Al-Inany HG, "Gonadotrophin-releasing hormone antagonists for assisted reproductive technology" 4 : CD001750-, 2016

27 "Gonadotrophin therapy individualization tool"

28 Lambalk CB, "GnRH antagonist versus long agonist protocols in IVF : a systematic review and meta-analysis accounting for patient type" 23 : 560-579, 2017

29 Borges E Jr, "FSH dose to stimulate different patient' ages : when less is more" 21 : 336-342, 2017

30 Xiao JS, "Comparisons of GnRH antagonist versus GnRH agonist protocol in supposed normal ovarian responders undergoing IVF : a systematic review and meta-analysis" 9 : e106854-, 2014

31 Lazer T, "Comparison of IVF outcomes between minimal stimulation and high-dose stimulation for patients with poor ovarian reserve" 2014 : 581451-, 2014

32 Papaleo E, "Clinical application of a nomogram based on age, serum FSH and AMH to select the FSH starting dose in IVF/ICSI cycles : a retrospective two-centres study" 207 : 94-99, 2016

33 Hashish NM, "Choosing the optimal dose of human menopausal gonadotropins for ovarian stimulation in ICSI cycles" 19 : 124-128, 2014

34 Zhou J, "Association between the number of oocytes retrieved and cumulative live birth rate in women aged 35-40 years undergoing long GnRH agonist IVF/ICSI cycles" 296 : 1005-1012, 2017

35 Farquhar C, "Assisted reproductive technology : an overview of Cochrane Reviews" 8 : CD010537-, 2018

36 Salahudeen MS, "An overview of pharmacodynamic modelling, ligand-binding approach and its application in clinical practice" 25 : 165-175, 2017

37 Allegra A, "A randomized controlled trial investigating the use of a predictive nomogram for the selection of the FSH starting dose in IVF/ICSI cycles" 34 : 429-438, 2017

38 Zhu M, "A predictive formula for selecting individual FSH starting dose based on ovarian reserve markers in IVF/ICSI cycles" 300 : 441-446, 2019

39 Oliveira JB, "A new ovarian response prediction index(ORPI) : implications for individualised controlled ovarian stimulation" 10 : 94-, 2012