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Journal of Assisted Reproduction and Genetics

, Volume 28, Issue 2, pp 137–144 | Cite as

Receiver operating characteristic (ROC) analysis of day 5 morphology grading and metabolomic Viability Score on predicting implantation outcome

  • Emre Seli
  • Can Bruce
  • Lucy Botros
  • Mark Henson
  • Pieter Roos
  • Kevin Judge
  • Thorir Hardarson
  • Aishling Ahlström
  • Paul Harrison
  • Michael Henman
  • Kathryn Go
  • Nicole Acevedo
  • Jeannette Siques
  • Michael Tucker
  • Denny Sakkas
Assisted Reproduction Technologies

Abstract

Purpose

Assessment of embryo viability is a key component of in vitro fertilization (IVF) and currently relies largely on embryo morphology and cleavage rate. In this study, we used receiver operating characteristic (ROC) analysis to compare the Viability Score (generated by metabolomic profiling of spent embryo culture media using near infrared (NIR) spectroscopy) to morphologic grading for predicting pregnancy in women undergoing single embryo transfer (SET) on day 5.

Methods

A total of 198 spent embryo culture media samples were collected in four IVF centers located in the USA, Europe and Australia. First, 137 samples (training set) were analyzed by NIR to develop an algorithm that generates a Viability Score predictive of pregnancy for each sample. Next, 61 samples (validation set) were analyzed by observers blinded to embryo morphology and IVF outcome, using the Day 5 algorithm generated with the training set. Pregnancy was defined as fetal cardiac activity (FCA) at 12 weeks of gestation.

Results

The Area Under the Curve (AUC) was greater for the metabolomic Viability Score compared to Morphology [Training set: 0.75 versus 0.55, p = 0.0011; Validation set: 0.68 versus 0.50, P = 0.021], and for a Composite score (obtained using a model combining Viability Score with morphologic grading), compared to morphology alone [0.74 versus 0.50, p = 0.004].

Conclusions

Our findings suggest that Viability Score alone or in combination with morphologic grading has the potential to be a better classifier for pregnancy outcome than morphology alone in women undergoing SET on day 5.

Keywords

ROC analysis Assisted reproductive technologies ART In vitro fertilization IVF Morphologic grade Metabolomics Viability Score 

References

  1. 1.
    SART. Assisted reproductive technology success rates. National summary and fertility clinic reports. Centers for disease control, USA; 2007.Google Scholar
  2. 2.
    Ata B, Seli E. Economics of assisted reproductive technologies. Curr Opin Obstet Gynecol. 2010; epub Feb 1.Google Scholar
  3. 3.
    Steptoe PC, Edwards RG. Birth after the reimplantation of a human embryo. Lancet. 1978;2:366.PubMedCrossRefGoogle Scholar
  4. 4.
    Trounson A, Leeton J, Wood C, Webb J, Wood J. Pregnancies in humans by fertilization in vitro and embryo transfer in the controlled ovulatory cycle. Science. 1981;216:681–2.CrossRefGoogle Scholar
  5. 5.
    Edwards R, Fishel S, Cohen J. Factors influencing the success of in vitro fertilization for alleviating human infertility. J In Vitro Fertil Embryo Transf 1984:3–23.Google Scholar
  6. 6.
    Gardner DK, Schoolcraft WB. In vitro culture of human blastocysts. In: MD JR, editor. Towards reproductive certainty: fertility and genetics beyond. Carnforth: Parthenon; 1999. p. 378–88.Google Scholar
  7. 7.
    Gerris J, De Neubourg D, Mangelschots K, et al. Prevention of twin pregnancy after in-vitro fertilization or intracytoplasmic sperm injection based on strict embryo criteria: a prospective randomized clinical trial. Hum Reprod. 1999;14:2581–7.PubMedCrossRefGoogle Scholar
  8. 8.
    Scott LA, Smith S. The successful use of pronuclear embryo transfer the day following oocyte retrieval. Hum Reprod. 1998;13:1003–13.PubMedCrossRefGoogle Scholar
  9. 9.
    Tesarik J, Greco E. The probability of abnormal preimplantation development can be predicted by a single static observation on pronuclear state morphology. Hum Reprod. 1999;14:1318–23.PubMedCrossRefGoogle Scholar
  10. 10.
    VanRoyan E, Mangelschots K, De Neubourg D, Valkenburg M, Van de Meerssche M, Ryckaert G, et al. Characterization of a top quality embryo, a step towards single-embryo transfer. Hum Reprod. 1999;14:2345–9.CrossRefGoogle Scholar
  11. 11.
    Veeck L. An atlas of human gametes and conceptuses: an illustrated reference for assisted reproductive technology. New York: Parthenon; 1999.Google Scholar
  12. 12.
    Sakkas D, Percival G, D’Arcy Y, Sharif K, Afnan M. Assessment of early cleaving in vitro fertilized human embryos at the 2-cell stage before transfer improves embryo selection. Fertil Steril. 2001;76:1150–6.PubMedCrossRefGoogle Scholar
  13. 13.
    Toner JP. Progress we can be proud of: U.S. trends in assisted reproduction over the first 20 years. Fertil Steril. 2002;78:943–50.PubMedCrossRefGoogle Scholar
  14. 14.
    Bromer JG, Seli E. Assessment of embryo viability in assisted reproductive technologies: shortcomings of current approaches and the emerging role of metabolomics. Curr Opin Obstet Gynecol. 2008;20:234–41.PubMedCrossRefGoogle Scholar
  15. 15.
    Sakkas D, Gardner DK. Noninvasive methods to assess embryo quality. Curr Opin Obstet Gynecol. 2005;17:283–8.PubMedCrossRefGoogle Scholar
  16. 16.
    Botros L, Sakkas D, Seli E. Metabolomics and its application for non-invasive embryo assessment in IVF. Mol Hum Reprod. 2008;14:679–90.PubMedCrossRefGoogle Scholar
  17. 17.
    Katz-Jaffe MG, Schoolcraft WB, Gardner DK. Analysis of protein expression (secretome) by human and mouse preimplantation embryos. Fertil Steril. 2006;86:678–85.PubMedCrossRefGoogle Scholar
  18. 18.
    Fuzzi B, Rizzo R, Criscuoli L, Noci I, Melchiorri L, Scarselli B, et al. HLA-G expression in early embryos is a fundamental prerequisite for the obtainment of pregnancy. Eur J Immunol. 2002;32:311–5.PubMedCrossRefGoogle Scholar
  19. 19.
    Vercammen MJ, Verloes A, Van de Velde H, Haentjens P. Accuracy of soluble human leukocyte antigen-G for predicting pregnancy among women undergoing infertility treatment: meta-analysis. Hum Reprod Update. 2008;14:209–18.PubMedCrossRefGoogle Scholar
  20. 20.
    Oliver SG, Winson MK, Kell DB, Baganz F. Systematic functional analysis of the yeast genome. Trends Biotechnol. 1998;16:373–8.PubMedCrossRefGoogle Scholar
  21. 21.
    Seli E, Sakkas D, Scott R, Kwok JS, Rosendahl S, Burns DH. Non-invasive metabolomic profiling of human embryo culture media using Raman and near infrared spectroscopy correlates with reproductive potential of embryos in women undergoing in vitro fertilization. Fertil Steril. 2007;88:1350–7.PubMedCrossRefGoogle Scholar
  22. 22.
    Scott RT, Seli E, Miller K, Sakkas D, Scott K, Burns DH. Non-invasive metabolomic profiling of human embryo culture media using Raman spectroscopy predicts embryonic reproductive potential: a prospective blinded pilot study. Fertil Steril. 2008;90:77–83.PubMedCrossRefGoogle Scholar
  23. 23.
    Vergouw CG, Botros LL, Roos P, Lens JW, Schats R, Hompes PGA, et al. Metabolomic profiling by near-infrared spectroscopy as a tool to assess embryo viability: a novel, non-invasive method for embryo selection. Hum Reprod. 2008;23:1499–504.PubMedCrossRefGoogle Scholar
  24. 24.
    Seli E, Vergouw CG, Morita H, Botros L, Roos P, Lambalk CB, et al. Non-invasive metabolomic profiling as an adjunct to morphology for non-invasive embryo assessment in women undergoing single embryo transfer. Fertil Steril. 2010;94:535–42.PubMedCrossRefGoogle Scholar
  25. 25.
    Seli E, Botros L, Henson M, Roos P, Sakkas D, group. Ms. Viability scores determined by metabolomic assessment of embryo culture media correlate with IVF outcome in women undergoing single embryo transfer on day 2: a prospective multi-center trial. In: Annual Meeting of American Society of Reproductive Medicine; 2009; Atlanta, GA.; 2009.Google Scholar
  26. 26.
    Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: visualizing classifier performance in R. Bioinformatics. 2005;21:3940–1.PubMedCrossRefGoogle Scholar
  27. 27.
    R Development Core Team. R: A language and environment for statistical computing. In: R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL: http://www.R-project.org; 2005.
  28. 28.
    Hosmer DW, Lemeshow S. Applied logistic regression. 2nd ed. Wiley; 2000.Google Scholar
  29. 29.
    Vergara IA, Norambuena T, Ferrada E, Slater AW, Melo F. StAR: a simple tool for the statistical comparison of ROC curves. BMC Bioinform. 2008;9:265.CrossRefGoogle Scholar
  30. 30.
    He X, Frey E, ROC, LROC, FROC, AFROC. An alphabet soup. J Am Coll Radiol. 2009;6:652–5.PubMedCrossRefGoogle Scholar
  31. 31.
    Gardner DK. Dissection of culture media for embryos: the most important and less important components and characteristics. Reprod Fertil Dev. 2008;20:9–18.PubMedCrossRefGoogle Scholar
  32. 32.
    Biggers JD, Summers MC. Choosing a culture medium: making informed choices. Fertil Steril. 2008;90:473–83.PubMedCrossRefGoogle Scholar
  33. 33.
    Gardner DK, Schoolcraft WB, Wagley L, Schlenker T, Stevens J, Hesla J. A prospective randomized trial of blastocyst culture and transfer in in-vitro fertilization. Hum Reprod. 1998;13:3434–40.PubMedCrossRefGoogle Scholar
  34. 34.
    Behr B, Pool TB, Milki AA, Moore D, Gebhardt J, Dasig D. Preliminary clinical experience with human blastocyst development in vitro without co-culture. Hum Reprod. 1999;14:454–7.PubMedCrossRefGoogle Scholar
  35. 35.
    Van der Auwera I, Debrock S, Spiessens C, Afschrift H, Bakelants E, Meuleman C, et al. A prospective randomized study: day 2 versus day 5 embryo transfer. Hum Reprod. 2002;17:1507–12.PubMedCrossRefGoogle Scholar
  36. 36.
    Gardner DK, Surrey E, Minjarez D, Leitz A, Stevens J, Schoolcraft WB. Single blastocyst transfer: a prospective randomized trial. Fertil Steril. 2004;81:551–5.PubMedCrossRefGoogle Scholar
  37. 37.
    Blake DA, Farquhar CM, Johnson N, Proctor M. Cleavage stage versus blastocyst stage embryo transfer in assisted reproduction. Cochrane Database Syst Rev. 2007;4:CD002118.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Emre Seli
    • 1
  • Can Bruce
    • 2
  • Lucy Botros
    • 3
  • Mark Henson
    • 3
  • Pieter Roos
    • 3
  • Kevin Judge
    • 3
  • Thorir Hardarson
    • 4
  • Aishling Ahlström
    • 4
  • Paul Harrison
    • 5
  • Michael Henman
    • 5
  • Kathryn Go
    • 6
  • Nicole Acevedo
    • 6
  • Jeannette Siques
    • 7
  • Michael Tucker
    • 7
  • Denny Sakkas
    • 1
    • 3
  1. 1.Department of Obstetrics, Gynecology and Reproductive SciencesYale University School of MedicineNew HavenUSA
  2. 2.Department of Molecular Biochemistry and Biophysics, and W.M. Keck Foundation, Biotechnology Resource LaboratoryYale UniversityNew HavenUSA
  3. 3.Molecular Biometrics,® IncNew HavenUSA
  4. 4.FertilitetsCentrumGothenburgSweden
  5. 5.Sydney IVF ClinicSydneyAustralia
  6. 6.Reproductive Sciences CenterBostonUSA
  7. 7.Shady Grove Fertility ClinicRockvilleUSA

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