Predicting the chances of a live birth after one or more complete cycles of in vitro fertilisation: Population based study of linked cycle data from 113 873 women

BMJ (Online)

Volumn 355, Issue , 2016, Pages

  McLernon, David J ^a   Steyerberg, Ewout W ^b   Te Velde, Egbert R ^b   Lee, Amanda J ^a   Bhattacharya, Siladitya ^a  

^a UNIVERSITY OF ABERDEEN   (United Kingdom)

^b ERASMUS MEDICAL CENTER   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGE; BIRTH RATE; FEMALE; HUMAN; IN VITRO FERTILIZATION; LIVE BIRTH; PREDICTIVE VALUE; PREGNANCY; PREGNANCY RATE; PROBABILITY; TIME FACTOR; UNITED KINGDOM;

ADULT; AGE FACTORS; BIRTH RATE; FEMALE; FERTILIZATION IN VITRO; HUMANS; LIVE BIRTH; PREDICTIVE VALUE OF TESTS; PREGNANCY; PREGNANCY RATE; PROBABILITY; TIME FACTORS; UNITED KINGDOM;

EID: 84996878259     PISSN: 09598146     EISSN: 17561833     Source Type: Journal    
DOI: 10.1136/bmj.i5735     Document Type: Article

References (42)

1. Oakley L, Doyle P, Maconochie N. Lifetime prevalence of infertility and infertility treatment in the UK: results from a population-based survey of reproduction. Hum Reprod 2008;23:447-50. doi:10. 1093/humrep/dem369.
2. National Collaborating Centre for Women's and Children's Health. Fertility: assessment and treatment for people with fertility problems. 2013. https://www. nice. org. uk/guidance/cg156/evidence/full-guideline-188539453.
3. Adamson GD, Tabangin M, Macaluso M, de Mouzon J. The number of babies born globally after treatment with the assisted reproductive technologies (ART). Fertil Steril 2013;100:S42. doi:10. 1016/j. fertnstert. 2013. 07. 1807.
4. Vrtacnik U, Vrtacnik Bokal E, Devjak R. Cumulative delivery rate after providing full reimbursement in vitro fertilization programme: a 6-years survey. Biomed Res Int 2014;2014:850478. doi:10. 1155/2014/850478.
5. Abuzeid MI, Bolonduro O, La Chance J, et al. Cumulative live birth rate and assisted reproduction: impact of female age and transfer day. Facts Views Vis Obgyn 2014;6:145-9.
6. Maheshwari A, McLernon D, Bhattacharya S. Cumulative live birth rate: time for a consensus?Hum Reprod 2015;30:2703-7.
7. Luke B, Brown MB, Wantman E, et al. Cumulative birth rates with linked assisted reproductive technology cycles. N Engl J Med 2012;366:2483-91. doi:10. 1056/NEJMoa1110238.
8. Macaldowie A, Wang YA, Chambers GM, Sullivan EA. National Perinatal Epidemiology and Statistics Unit, the University of New South Wales, Sydney. Assisted reproduction technology in Australia and New Zealand 2011. 2013. https://npesu. unsw. edu. au/surveillance/assisted-reproductive-technology-australia-new-zealand-2011.
9. Malizia BA, Hacker MR, Penzias AS. Cumulative live-birth rates after in vitro fertilization. N Engl J Med 2009;360:236-43. doi:10. 1056/NEJMoa0803072.
10. McLernon DJ, Maheshwari A, Lee AJ, Bhattacharya S. Cumulative live birth rates after one or more complete cycles of IVF: a populationbased study of linked cycle data from 178, 898 women. Hum Reprod 2016;31:572-81. doi:10. 1093/humrep/dev336.
11. Smith ADAC, Tilling K, Nelson SM, Lawlor DA. Live-birth rate associated with repeat in vitro fertilization treatment cycles. JAMA 2015;314:2654-62. doi:10. 1001/jama. 2015. 17296.
12. van Loendersloot LL, van Wely M, Limpens J, Bossuyt PMM, Repping S, van der Veen F. Predictive factors in in vitro fertilization (IVF): a systematic review and meta-analysis. Hum Reprod Update 2010;16:577-89. doi:10. 1093/humupd/dmq015.
13. Nelson SM, Lawlor DA. Predicting live birth, preterm delivery, and low birth weight in infants born from in vitro fertilisation: a prospective study of 144, 018 treatment cycles. PLoS Med 2011;8:e1000386. doi:10. 1371/journal. pmed. 1000386.
14. Bhattacharya S, Maheshwari A, Mollison J. Factors associated with failed treatment: an analysis of 121, 744 women embarking on their first IVF cycles. PLoS One 2013;8:e82249. doi:10. 1371/journal. pone. 0082249.
15. Human Fertilisation and Embryology Authority. Access anonymised HFEA data. 2013b. http://www. hfea. gov. uk/5874. html.
16. Sunkara SK, Rittenberg V, Raine-Fenning N, Bhattacharya S, Zamora J, Coomarasamy A. Association between the number of eggs and live birth in IVF treatment: an analysis of 400 135 treatment cycles. Hum Reprod 2011;26:1768-74. doi:10. 1093/humrep/der106.
17. Williams CL, Bunch KJ, Stiller CA, et al. Cancer risk among children born after assisted conception. N Engl J Med 2013;369:1819-27. doi:10. 1056/NEJMoa1301675.
18. Steyerberg EW. Clinical prediction models: a practical approach to development, validation and updating. Springer, 2009doi:10. 1007/978-0-387-77244-8.
19. te Velde ER, Nieboer D, Lintsen AM, et al. Comparison of two models predicting IVF success; the effect of time trends on model performance. Hum Reprod 2014;29:57-64. doi:10. 1093/humrep/det393.
20. Bouwmeester W, Twisk JWR, Kappen TH, van Klei WA, Moons KGM, Vergouwe Y. Prediction models for clustered data: comparison of a random intercept and standard regression model. BMC Med Res Methodol 2013;13:19. doi:10. 1186/1471-2288-13-19.
21. Harrell FE. Regression modeling strategies with applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Springer, 2001.
22. Thompson D. Ranking Predictors in logistic regression, SAS Institute: Paper D10-2009. 2009. http://www. mwsug. org/proceedings/2009/stats/MWSUG-2009-D10. pdf.
23. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996;15:361-87. doi:10. 1002/(SICI)1097-0258(19960229)15:43. 0. CO;2-4.
24. Human Fertilisation and Embryology Authority. Fertility treatment in 2013: trends and figures. 2013a. http://www. hfea. gov. uk/docs/HFEA-Fertility-Trends-and-Figures-2013. pdf.
25. Human Fertilisation and Embryology Authority. Facts and figures for researchers and the media. 2015. http://www. hfea. gov. uk/99. html.
26. Joesbury KA, Edirisinghe WR, Phillips MR, Yovich JL. Evidence that male smoking affects the likelihood of a pregnancy following IVF treatment: application of the modified cumulative embryo score. Hum Reprod 1998;13:1506-13. doi:10. 1093/humrep/13. 6. 1506.
27. Soares SR, Melo MA. Cigarette smoking and reproductive function. Curr Opin Obstet Gynecol 2008;20:281-91. doi:10. 1097/GCO. 0b013e3282fc9c1e
28. Rossi BV, Berry KF, Hornstein MD, Cramer DW, Ehrlich S, Missmer SA. Effect of alcohol consumption on in vitro fertilization. Obstet Gynecol 2011;117:136-42. doi:10. 1097/AOG. 0b013e31820090e1.
29. Boniface S, Kneale J, Shelton N. Drinking pattern is more strongly associated with under-reporting of alcohol consumption than socio-demographic factors: evidence from a mixed-methods study. BMC Public Health 2014;14:1297. doi:10. 1186/1471-2458-14-1297.
30. Håkonsen LB, Ernst A, Ramlau-Hansen CH. Maternal cigarette smoking during pregnancy and reproductive health in children: a review of epidemiological studies. Asian J Androl 2014;16:39-49. doi:10. 4103/1008-682X. 122351.
31. Te Velde ER, Koudstaal J, Eimers JM. Assisted conception for infertility. BMJ 1992;305:1097-8. doi:10. 1136/bmj. 305. 6861. 1097-d.
32. Stolwijk AM, Wetzels AMM, Braat DDM. Cumulative probability of achieving an ongoing pregnancy after in-vitro fertilization and intracytoplasmic sperm injection according to a woman's age, subfertility diagnosis and primary or secondary subfertility. Hum Reprod 2000;15:203-9. doi:10. 1093/humrep/15. 1. 203.
33. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med 2015;13:1. doi:10. 1186/s12916-014-0241-z.
34. Dhillon RK, McLernon DJ, Smith PP, et al. Predicting the chance of live birth for women undergoing IVF: a novel pretreatment counselling tool. Hum Reprod 2016;31:84-92. doi:10. 1093/humrep/dev268.
35. Leushuis E, van der Steeg JW, Steures P, et al. Prediction models in reproductive medicine: a critical appraisal. Hum Reprod Update 2009;15:537-52. doi:10. 1093/humupd/dmp013.
36. van Loendersloot LL, van Wely M, Repping S, Bossuyt PMM, van der Veen F. Individualized decision-making in IVF: calculating the chances of pregnancy. Hum Reprod 2013;28:2972-80. doi:10. 1093/humrep/det315.
37. Luke B, Brown MB, Wantman E, et al. A prediction model for live birth and multiple births within the first three cycles of assisted reproductive technology. Fertil Steril 2014;102:744-52. doi:10. 1016/j. fertnstert. 2014. 05. 020.
38. Stern JE, Hickman TN, Kinzer D, Penzias AS, Ball GD, Gibbons WE. Can the Society for Assisted Reproductive Technology Clinic Outcome Reporting System (SART CORS) be used to accurately report clinic total reproductive potential (TRP)?Fertil Steril 2012;97:886-9. doi:10. 1016/j. fertnstert. 2012. 01. 091.
39. Coppus SFPJ, van der Veen F, Opmeer BC, Mol BWJ, Bossuyt PMM. Evaluating prediction models in reproductive medicine. Hum Reprod 2009;24:1774-8. doi:10. 1093/humrep/dep109.
40. Reilly BM, Evans AT. Translating clinical research into clinical practice: impact of using prediction rules to make decisions. Ann Intern Med 2006;144:201-9. doi:10. 7326/0003-4819-144-3-200602070-00009.
41. Moons KGM, Kengne AP, Grobbee DE, et al. Risk prediction models: II. External validation, model updating, and impact assessment. Heart 2012;98:691-8. doi:10. 1136/heartjnl-2011-301247.
42. McLernon DJ, te Velde ER, Steyerberg EW, Mol BWJ, Bhattacharya S. Clinical prediction models to inform individualized decision-making in subfertile couples: a stratified medicine approach. Hum Reprod 2014;29:1851-8. doi:10. 1093/humrep/deu173.