Past performance of assisted reproduction technologies as a model to predict future progress: a proposed addendum to Moore’s law

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  • Reproductive BioMedicine Online (2012) 25, 585590

    ceonwww.sc ienwww.rbmARTICLE

    Past performance of assistetechnologies as a model toprogress: a proposed adden

    Jacques Cohen a,b,e,*, Mina Alikani c, Al

    a Althea LLC, United States; b Reprogenetics LLC, United StateShore University Hospital, United States; d Recombine LLC, UniUnited States* Corresponding author. E-mail address: jc@embryos.net (J Cohen).

    Jacques Cohen is one of the founders of RepAlpha Scientists in Reproductive MedicineHe was trained in the 1970s at Erasmus Uspecialized in IVF and cryobiology. He was anhas authored several patents. He is the Seniseveral laboratories involved in IVF and printerests are embryonic viability, cryopreselaboratories.

    Abstract The ultimate goal of IVF is to achieve healthy, single, liveeventuality has never been defined. National implantation rates fromTechnologies (SART) in the USA were evaluated. Regression analysisshowing a linear increase from year to year ranging between 0.3% ationship can be retrospectively applied to earlier SART data reportMoores law, which describes annual improvements in microchip petinue to drive progress, the length of time required to reach 100% imp(AD 2053) for the youngest age group (

  • 586 J Cohen et al.compared with the early data, and reported national suc-cess rates in the USA are on the rise (Marek et al., 1999;Schoolcraft et al., 2010; Shapiro et al., 2011; Toner, 2002).Whilst success rates have increased, specific technologicaladvances that have propelled this increase remain difficultto express with certainty. Another issue is the very defini-tion of success. Live birth rate is commonly regarded asthe ultimate measure of success when comparing assistedreproduction outcomes. However, the notion that this isan all-encompassing and unbiased parameter is hardlydefensible given the fact that it is dependent on the numberof embryos transferred to the uterus. Live birth rates areonly reliable if aetiology, maternal age, attempt number,cycle cancellation rate, number of embryos transferredand other parameters are taken into account. Indeed, livebirth rate from IVF remains elusive and inexact as an out-come measure as long as the incidence of multiple preg-nancy is higher than the natural rate. Transfer of multipleembryos confounds every comparison. Selective reductionof high-order multiple pregnancies also complicate matters.For quality control monitoring purposes, there are manyother suggested endpoints: cycle cancellation rate, numberof follicles, number of eggs, fertilization rate, cryopreser-vation rate, development profile, blastocyst formation rate,frequency of mosaicism and aneuploidy and embryo mor-phology scores to name but a few. Evaluation of theseparameters is helpful for intra-programme quality control,but not necessarily for inter-programme comparisons. Theone parameter that should be adopted as both a measureof quality and success is implantation rate: the frequencywith which in-vitro-generated embryos implant (and leadto a gestational sac or fetal cardiac activity) in the uterus.Although not perfect, this parameter has the advantage ofmeasuring success one embryo at a time whether one ormore embryos are transferred.

    Comparing national databases allows reproduction spe-cialists to assess progress as well as evaluate safety andadverse effects of assisted reproduction technologies. Oneof the first such databases to have been developed is thatof the Society of Assisted Reproductive Technologies (SART)which has provided data reported by IVF clinics in the USA onan annual basis since 1988 and via its own Clinical OutcomeReporting System (CORS) since 1995. A national summary ofthe SART-CORS data is tabulated and presented(https://www.sartcorsonline.com/rptCSR_PublicMultYear.aspx?ClinicPKID = 0). An interactive online SART databasehas been in place since 2003, but annual reports weremade available in printed format starting in 1988. Duringthe first few years of its existence, this system comprisedvoluntary reporting initiated by the American Societyfor Reproductive Medicine (ASRM) in anticipation of theFertility Clinics Success Rate and Certification Act, whichwas passed by Congress in 1992 (Wyden law). The firstreport from the Center for Disease Control (CDC) wasjointly published by SART and CDC on the presentation ofoutcome data of 1995 (Toner, 2002). Since 2003, CDC andSART have separate online reporting systems. Unlike theCDC, the SART database has provided embryo implantationrates.

    Using the publically available data, this study shows thatimplantation rates, corrected for maternal age, have beenlinearly increasing in the USA since the mid-1980s. It is sug-gested that this incline is at least partly technology drivenand that it can therefore be compared with improvementsin the development of microchips, based on which sizeand speed of computers can be accurately predicted usingMoores law (1965).

    Moores law was named after the physicist GordonMoore who is one of the founders of Intel Corporation(Moore, 1965). He predicted, based on just four datapoints, that the ability to pack transistors onto an inte-grated circuit board would double every 24 months. Thiswas later adjusted to 18 months. His prediction has heldfor the past 47 years. The prediction may have beeninspired by an earlier lecture from computer scientist,Douglas C Engelbart, on scalability. The theoretical limitto the relationship between expansion of memory and pro-cessor speed and time is the atom transistor. The firstexperimental atom transistor was described in Februaryof this year allowing transistors to be spaced only one atomapart (Fuechsle et al., 2012). This apparent limit provides apotential endpoint to expansion in Moores law. It has beendetermined from Moores law that computer hard-drivesize and speed doubles every 18 months and that the speedof expansion is about 60% annually. Several other laws inapplied physics have been derived from Moores law.Among these are Nielsens law (Nielsen, 1998), which hascorrectly predicted an annual increase of 50% in internetbandwidth and Metcalfes law (Shapiro and Varian, 1999),which states that the value of a network is proportionalto the square of the number of nodules. This implies thatwhen a network expands, the value of being connected toit grows while costs remain unchanged. Some online busi-ness models such as Facebook are based on this law. Eventhe expansion of pixel quantity on screens and camerasadheres to Moores law.

    Here it is demonstrated that success rate, expressed asimplantation rate, following assisted reproduction increaseslinearly each year when corrected for maternal age.Whereas progress in artificial biological systems can quiteprecisely follow Moores law and expand in log linear fashionas expected, this is possible only if backed by progress inapplied physics such as DNA sequencing speed (Petterssonet al., 2009). By inference, progress in assisted reproductiontechnology is multifactorial and only in part driven by dis-coveries in applied physics. Implantation rates can beexpected to improve at an annual incremental rate of0.31.5% depending on age. This relationship appears tobe linear. The following key question based on these calcu-lations is addressed: How long will it take before 100%implantation rates are achieved routinely? The implicationsof reaching 100% implantation for infertility treatment andhuman reproduction in general are numerous and will bediscussed. The relativity of predicting a perfect outcomewill be addressed as well.

    Materials and methods

    US national data summary reports were transcribed andstudied for the years 20032010 using SART-CORS and indi-vidual clinic data were downloaded and evaluated for theyears 20052010. This information is subject to the copy-right and proprietary use agreement of the ASRM as stated

  • on their website (http://sart.org/policy/detail.aspx?id=424&terms=(+%40Publish_To+Both+Sites+or+%40Publish_To+Patient+Site+Only+)+and+permission). Parameters includedin this evaluation were number of cycles involving transferof fresh embryos, live birth rate, implantation rate andthe average number of embryos transferred. Implantationwas defined by SART as either the fetal hearts reportedon ultrasound or the number of live born plus still born overthe total number of embryos transferred for fresh,non-donor cycles by female age group (www.SART.org).

    For fresh embryo transfers, SART presents data accordingto maternal age groups: 42years. The latter groupwas included in this evaluation in spiteof relatively modest sample sizes. The number of reportedcycles per year ranged from 35,946 to 39,173 for the 42 years). In total 725,747 cycles and1,882,620 transferred embryos were included in this study.

    Linear regression analysis was considered to determinethe slopes of the implantation data over time. For nationaldata, regression analysis was performed on the data and agraph was created using the Excel scatter plot feature (MSOffice 2009). Excel uses several approaches to calculateregression analysis such as LINEST, TREND, FORECAST,SLOPE and STEYX. A linear least-Squares line of best fitwas obtained by calculating the y-intercept (b) and theslope (m) as follows:

    b nP

    yP x2 P xP xynP x2 P x2 1

    m nP

    xy P xP ynP x2 P x2 2

    For individual clinics, data were restricted to clinicswhose annual number of cycles in the

  • SART-CORS data from 6 years (20052010). Clinics wereidentified using an anonymous clinic ID system. Slopes var-ied between 1.65 and 7.20. The mean slope was 1.48 inthis subgroup of clinics. Ten clinics which maximized slopeand average implantation rate, and minimized the standarddeviation of implantation rates were investigated further(Table 2). The linear correlation in these 10 clinics was 0.66(Figure 2). The approximate year of 100% implantation wascalculated for this group of clinics at AD 2027.

    Past implantation rates between 2003 and 2010 reportedfor the other maternal age groups are also presented inTable 1 and Figure 1. For the 3537-year age group,national implantation rates over the past 8 reporting yearsincreased at a slope of 0.93 using regression analysis. Fromthis it can be predicted that the 100% level for this groupwould be reached in 77 years (AD 2087). The slopes were0.64 and 0.32 for the 3840 and 4142-year age groups,respectively. For the 3840-year age group, the 100% levelwould be reached in 129 years (AD 2139). For the 4142-yearage group, the 100% level would be reached in 294 years (AD2294). For the over 42-year age group, a 100% level can onlybe ascertained with a limited confidence level (correlation is0.65), a very low slope of 0.08. The 100% level would bereached in 3213 AD indicating that improvement in successin this group has been modest over the years.

    Discussion

    A careful assessment of the US national SART database usingregression analyses suggests that there has been a linearincrease in implantation of fresh embryos over the courseof 8 years from 2003 to 2010. It appears that this trend ispresent across all maternal ages below 43. There is a cleardecrease in incremental gain with increasing maternal age.The strongest correlation with linear progression was seenin the

  • youngest age group (
  • Wortham, Wright, G., . The program for in vitro fertilization atNorfolk. Fertil. Steril. 38, 1421.

    Leeton, J., Trounson, A., Jessup, D., Wood, C., 1982. Thetechnique for human embryo transfer. Fertil. Steril. 38,156161.

    Leung, C., Lu, Z., Esfandiari, N., Casper, R.F., Sun, Y., 2011.Automated sperm immobilization for intracytoplasmic sperminjection. IEEE Trans. Biomed. Eng. 58, 935942.

    Marek, D., Langley, M., Gardner, D.K., Confer, N., Doody, K.M.,Doody, K.J., 1999. Introduction of blastocyst culture andtransfer for all patients in an in vitro fertilization program.Fertil. Steril. 72, 10351040.

    Moore, G.E., 1965. Cramming more components onto integratedcircuits. Electron. Mag. 38 (8), 4.

    Nagy, Z.P., Kerkis, I., Chang, C.C., 2008. Development of artificialgametes. Reprod. Biomed. Online 16, 539544.

    Nielsen, J., 1998. Nielsens law of internet bandwidth. In: JakobNielsen Alert Box. Available from .

    Pettersson, E., Lundeberg, J., Ahmadian, A., 2009. Generations ofsequencing technologies. Genomics 93, 105111.

    Schoolcraft, W.B., Fragouli, E., Stevens, J., Munne, S., Katz-Jaffe,M.G., Wells, D., 2010. Clinical application of comprehensivechromosomal screening at the blastocyst stage. Fertil. Steril. 94,17001706.

    Shapiro, B.S., Daneshmand, S.T., Garner, F.C., Aguirre, M., Hudson,C., Thomas, S., 2011. Evidence of impaired endometrial recep-tivity after ovarian stimulation for in vitro fertilization: aprospective randomized trial comparing fresh and fro-zen-thawed embryo transfer in normal responders. Fertil. Steril.96, 344348.

    Shapiro, C., Varian, H.R., 1999. Information Rules: A StrategicGuide to the Network Economy. Harvard Business School Press.

    Toner, J.P., 2002. Progress we can be proud of: US trends inassisted reproduction over the first 20 years. Fertil. Steril. 78,943950.

    Declaration: The authors report no financial or commercialconflicts of interest.

    Received 31 May 2012; refereed 31 July 2012; accepted 29 August2012.

    590 J Cohen et al.

    Past performance of assisted reproduction technologies as a model to predict future progress: a proposed addendum to Moores lawIntroductionMaterials and methodsResultsDiscussionReferences

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