Keywords
early pregnancy loss, occult pregnancy, embryo mortality, human chorionic gonadotrophin, Hertig, pre-implantation embryo loss
early pregnancy loss, occult pregnancy, embryo mortality, human chorionic gonadotrophin, Hertig, pre-implantation embryo loss
It is widely accepted that under natural circumstances, human embryo mortality is high, particularly immediately after fertilisation. Quantitative estimates of embryo loss are found in diverse media including television documentaries (“You made it through the first round” presented by Michael Mosley: video at http://www.bbc.co.uk/timelines/z84tsg8; transcript at http://a.files.bbci.co.uk/bam/live/content/z3b87hv/transcript: accessed on 22nd October, 2016), online educational videos (“Bill Nye: Can We Stop Telling Women What to Do With Their Bodies?” presented by Bill Nye, the Science Guy: video at https://www.youtube.com/watch?v=4IPrw0NYkMg: accessed on 22nd October, 2016), online museum exhibits (“Who Am I? What happens in week 1?” presented by The Science Museum; available at http://www.sciencemuseum.org.uk/WhoAmI/FindOutMore/Yourbody/Wheredidyoucomefrom/Howdoyougrowinthewomb/Whathappensinweek1: accessed on 22nd October, 2016), news reports (“Scientists get ‘gene-editing’ go-ahead” by James Gallagher: article at http://www.bbc.co.uk/news/health-35459054: accessed on 22nd October, 2016), as well as academic philosophical articles1 and legal judgements2. Among reputable scientific publications, including medical and reproductive biology text books, scientific reviews and primary research articles, reported mortality estimates include: 30–70% before and during implantation3; >50%4, 73%5 and 80%6 before the 6th week; 75% before the 8th week7; 70% in the first trimester8; 40–50% in the first 20 weeks9; and 49%10, >50%11,12, 53%13, 54%14, 60%15, >60%16, 63%17,18, 70%19–23, 50–75%24, 76%5,25, 78%26, 80–85%27, >85%28, and 90%29 total loss from fertilisation to term. The variance in these estimates is striking. 90% intrauterine mortality implies a maximal live birth fecundability of 10%, and only then if all other stages of the reproductive process are 100% efficient. Observed human fecundability is low compared to other animals13, but at approximately 20–30%4,30 it is still higher than implied by such a high embryo mortality rate.
Early human embryo mortality is of interest not only to reproductive biologists and fertility doctors, but also to ethicists31, theologians32 and lawyers2. Nevertheless, becoming pregnant and having children is of primary and personal importance to many women and their families. As with all biological processes, nothing works perfectly all the time33, and failure to conceive and pregnancy loss are common problems. However, inconsistent estimates of early pregnancy loss are not reassuring, nor do they provide a sound basis for either a quantitative understanding of natural human reproductive biology or an unbiased appraisal of artificial reproductive technologies. The divergent and excessive values noted above therefore invite scrutiny of the evidence that supports them. In this article, I identify and re-evaluate published data that contribute to claims regarding natural human embryo mortality.
A quantitative framework has been proposed to facilitate the calculation and comparison of embryo mortalities from fecundability and pregnancy loss data34. The model comprises conditional probabilities (π) of the following biological processes: (1) reproductive behaviours resulting in sperm-ovum-co-localisation per cycle = πSOC; (2) successful fertilisation given sperm-ovum-co-localisation = πFERT; (3) implantation of a fertilised ovum as indicated by increased levels of human chorionic gonadotrophin (hCG) = πHCG; (4) progression of an implanted embryo to a clinically recognised pregnancy = πCLIN; (5) survival of a clinical pregnancy to live birth = πLB.
Fecundability is the probability of reproductive success per cycle, but may take different values depending on the definition of success. The following four fecundabilities broadly follow Leridon30:
1. Total (all fertilisations): FECTOT = πSOC × πFERT
2. Detectable (implantation): FECHCG = πSOC × πFERT × πHCG
3. Apparent (clinical): FECCLIN = πSOC × πFERT × πHCG × πCLIN
4. Effective (live birth): FECLB = πSOC × πFERT × πHCG × πCLIN × πLB
Hence, the probability that a fertilised egg will perish prior to implantation is [1 - πHCG], and prior to clinical recognition is [1 - (πHCG × πCLIN)]. In theory, embryonic mortality may be estimated at different stages; however, in practice, this depends on available data. Clinical and live birth fecundabilities are most easily quantified and most frequently reported. Total and detectable fecundabilities are less frequently reported, although of direct relevance.
Publications containing data relevant to early human embryo mortality were identified primarily by tracing citations found in articles, reviews and textbooks. Systematic online searches did not capture all of these studies. Some are particularly old, many were not conducted to address the specific question, and others are in books or publications that are not adequately indexed. If not entirely complete, nevertheless the data presented form a substantial proportion of relevant, available scientific information on natural early human embryo mortality.
Studies that contribute analysis and data relevant to the quantification of natural human embryo mortality fall into the following four categories and will be considered in turn.
1. A speculative hypothesis published in The Lancet.
2. Life tables of intra-uterine mortality.
3. Studies of early pregnancy by biochemical detection of hCG.
4. Anatomical studies of Dr Arthur Hertig and Dr John Rock.
In 1975, a short hypothesis published in The Lancet entitled “Where Have All The Conceptions Gone?” concluded that 78% of all conceptions were lost before birth26. It has been widely cited by both scientists4,17,19,20,35 and non-scientists36,37 alike. Conceptions among married women aged 20–29 in England and Wales in 1971 were estimated and compared to infants born in the same period. In this analysis (Table 1) there are reliable values, e.g., census data, and simple arithmetical calculations. However, speculative values are necessary to perform the calculations. Three are biological: (1) fertilisation rate following unprotected coitus during the fertile period was estimated as 50% and supported by reference to Hertig38 (although his estimate was 84%33); (2) the length of a menstrual cycle (28 days); and (3) the duration of the fertile period (2 days). These latter values are plausible, but also variable. No justification is provided for three behavioural variables: (1) coital frequency estimated at twice per week; (2) proportion of unprotected coital acts estimated at 25%; and (3) either a random or regular distribution of coital acts during menstrual cycles such that 1/14 of all coital acts fall within a fertile period.
The validity of Roberts & Lowe’s conclusion depends largely on the accuracy and precision of these speculative values. The following two simple analyses illustrate the sensitivity of their conclusion on the speculative values.
1. When four of the speculative values are reduced by 25% (e.g., coital frequency reduced to 1.5/week) and cycle length increased by 10% (from 28 days to 31 days30), the estimate for embryo loss drops to 22%. The opposite operation (e.g., coital frequency increased to 2.5/week) results in an estimate of 92% (Table 1). Embryo loss of 22% is barely sufficient to account for observed clinical losses, and 92% indicates a maximum FECLB of 8%. Neither scenario is biologically plausible.
2. A non-zero variance was applied to each speculative value reflecting their uncertain nature. Using the random number generator in Microsoft® Excel (Office 2010) simulated values were obtained by random sampling from normal distributions with means equal to Roberts & Lowe’s speculative values with coefficients of variation equal to 20%. For simplicity, it was assumed that there was no covariance between the different speculative values. Table 1 shows the expected range within which 95% of these simulated values fall (e.g., coital frequency is 1.2–2.8/week). For each simulated record, a new estimate of embryo loss was calculated and from 10,000 of these, the mean, median and 2.5th and 97.5th percentiles of embryo loss were determined. This was repeated 1,000 times: the mean value of the simulated means was 73.3% and of the simulated medians was 76.5%. The mean values of the 2.5th and 97.5th percentile boundaries for embryo loss were 37% and 90% (Table 1). The same simulation was also performed using NONMEM 7.3.0® (Icon PLC, Dublin, Eire) and generated 100,000 data records. The outcome of this is shown in Figure 1. The code and simulated data values are in Dataset 1.
The sole purpose of these simple sensitivity analyses is to illustrate that modest adjustments to Roberts & Lowe’s original speculative values can result in any biologically plausible estimate for embryo loss. The output from the calculation is therefore substantially dependent on the subjectively selected input. Such an analysis has no practical quantitative value.
Other sources of bias in their model include the failure to account for intentionally terminated pregnancies and the reduced fecundability of already pregnant women and nursing mothers. Despite this, it was described as “persuasive”39 and it has been claimed that “it is still difficult to better the original calculations of Roberts and Lowe (1975)”19. By contrast, others have noted that “their calculations can be criticized”4 and are “tenuous”40. Considering its quantitative limitations, it has been cited surprisingly often8,20,41.
Constructing a life table of intrauterine mortality is challenging since embryonic death may occur even before the presence of an embryo is recognised. Nevertheless, in 1977 Henri Leridon published a complete life table of intrauterine mortality18. Leridon highlighted the consequences of inappropriate analysis and the quantitative biases produced by alternative numerical methods. Overall, he discussed sixteen studies, and provided detailed commentary on six42–47. These data are summarised in Figure 2 and suggest that 12–24% embryos alive at 4 weeks’ gestation (i.e., approx. 2 weeks’ post-fertilisation) will perish before birth.
Leridon described the Kauai Pregnancy Study42 in particular detail. In this study, an attempt was made to identify every pregnancy on Kauai from 1953–56. Women were encouraged to enrol as soon as they missed a period. Early pregnancy loss may therefore have been overestimated, since not all amenorrhoea is caused by conception, although other studies that relied upon medically-identified pregnancies probably underestimated early pregnancy loss by not capturing all cases48. Whatever the truth, it is clear that, among the studies reviewed by Leridon, the Kauai Pregnancy Study revealed the highest levels of pregnancy loss (Figure 2).
All recorded pregnancies in the Kauai study were categorised by date of enrolment in four week intervals, beginning with 4–7 weeks’ gestation. This time-staggered approach enabled risk of miscarriage to be associated with stage of gestation. However, despite considerable efforts, only 19% of the 3,197 recorded Kauai pregnancies were enrolled between 4–7 weeks’ gestation, thereby reducing the precision of pregnancy loss estimates for this earliest of time intervals. Although pregnancies were grouped in four week periods, Leridon suggested that early mortality may change week by week, resulting in underestimation of pregnancy loss. He re-allocated the 592 study entries and 32 pregnancy losses for weeks 4–7 (Table 2) generating an overall probability of pregnancy loss during this period of 15.0%, higher than 10.8% originally reported42. Leridon’s own description of this interpolation as “risky” can be illustrated by adjusting his re-allocation18. Transferring just two of the pregnancy losses out of or into the first week results in estimates of the 4–7 week pregnancy loss of 10.9% and 19.1% respectively (Table 2). The validity of adjusting Leridon’s re-allocation may be questioned. However, pregnancy loss in week 4–5 of the Kauai Study would manifest as a menstrual period delayed by up to one week. This is far from being a robust pregnancy diagnosis and in different study46, exclusion of pregnancy losses reported within one week of study entry resulted in substantially different loss probabilities (Figure 2) suggesting a confounding correlation between entry and loss18. Nevertheless, the re-allocation does reinforce a concern highlighted by Leridon, namely the uncertainty that affects the first probability. Clearly, these estimates of early loss should be treated with caution.
A more fundamental problem is that these data offer no insight into the fate of embryos prior to the earliest possible point of clinical pregnancy detection. Leridon completed his life table with values from Hertig’s analysis33. He concluded that among 100 ova exposed to the risk of fertilisation, 16 are not fertilised, 15 die in week one (before implantation), and 27 die in week two (before the menstrual period). After two weeks his life table follows the Kauai probabilities closely ending with 31 live births. Leridon’s table therefore indicates an embryo mortality of 50% (42/84) within the first two weeks after fertilisation and a total mortality of 63% (53/84) from fertilisation to birth.
Leridon’s account of intrauterine mortality has been widely cited. However, its accuracy depends entirely on the quality and interpretation of the data from Hertig33 and French & Bierman42. French & Bierman’s approach probably resulted in an overestimate of total pregnancy loss and is certainly imprecise in its estimate of embryo loss in the four weeks following the first missed menstrual period. The reliability of Hertig’s estimates of embryo loss in the two weeks following fertilisation is considered below.
Quantification of pregnancy loss requires pregnancy diagnosis. The earliest outward sign of pregnancy is a missed menstrual period, approximately 2 weeks after fertilisation, although amenorrhoea in women of reproductive age is not exclusively associated with fertilisation49,50. Several potentially diagnostic pregnancy-associated proteins have been identified51 of which only one, Early Pregnancy Factor (EPF)52, has been claimed to be produced by embryos within one day of fertilisation. However, there is doubt about the utility of EPF for diagnosing early pregnancy53 and little has been published on it in the past five years.
Modern pregnancy tests detect human chorionic gonadotrophin (hCG), a highly glycosylated 37 kDa protein hormone produced by embryonic trophoblast cells54. Mid-cycle elevation of hCG is associated with embryo implantation19,20,55. Early assays for the detection of hCG were probably confounded by antibody cross-reactivity with luteinizing hormone56 but modern tests are more specific and a positive result is a reliable indicator of early pregnancy. Highly sensitive assays have revealed low levels of hCG in non-pregnant women and healthy men57; hence, quantitative criteria are required to distinguish between non-pregnant women and those harbouring early embryos55.
Figure 3 and Table 3 summarise findings from thirteen studies that used hCG to identify so-called early, occult or biochemical pregnancy loss, i.e., pregnancy loss between the initiation of implantation and clinical recognition58–70. Notwithstanding design and subject differences, estimates for clinical pregnancy loss, ranging from 8.3% - 21.2% (Figure 3), are similar to previous estimates (Figure 2). Estimates for early/occult loss ranged from 0% to 58.3% in studies58–62 prior to Wilcox in 198863. This high variance was probably due to reduced specificity and sensitivity of the hCG assays and sub-optimal study design48,51,71–74. Studies from 198863 onwards have produced more consistent data indicating early/occult loss of approximately 20% (Figure 3). In the three largest studies63,66,70 pregnancies were clinically recognised only if they lasted ≥6 weeks after the onset of the last menstrual period66,75. Hence, early pregnancy losses in these studies included those lost up to approximately two weeks after a missed menstrual period: this may influence comparison of study results34,73. An overview of the thirteen studies suggests that overall pregnancy loss from first detection of hCG through to live birth is approximately one third (Table 3). This is consistent with another recent study which found that 98 out of 301 (32.6%) singleton pregnancies diagnosed by an early positive hCG test and followed-up to either birth or miscarriage were lost76.
The much cited Wilcox study63 is the earliest of several large well-designed studies that made use of a specific and sensitive hCG assay and led to numerous further publications75,77–83. Two other studies (Zinaman65 and Wang66) were similar in purpose, design and execution. These studies provide some of the best available data to calculate pregnancy loss between implantation and birth34. In each study, women intending to become pregnant and with no known fertility problems were recruited and hCG levels monitored cycle by cycle in daily urine samples until they became pregnant. Most women were followed through to late pregnancy or birth. Although these studies provide evidence regarding the outcome of both clinical and hCG pregnancies, determining the fate of embryos prior to implantation is more difficult. To relate the study results to pre-implantation embryo loss, it is necessary to determine fecundability. In each study FECCLIN declined in successive cycles as the proportion of sub-fertile women increased. Hence, reported FECHCG values of 30%65 and 40%66, and FECCLIN values of 25%63 and 30%66 are biased underestimates of the fecundability of normal fertile women. A recent re-analysis of these data provides statistical evidence for discrete fertile and sub-fertile sub-cohorts within the study populations34. The proportions of sub-fertile women (mean [95% CI]) were estimated as 28.1% [20.6, 36.9] (Wilcox); 22.8% [12.9, 37.2] (Zinaman); and 6.0% [2.8, 12.3] (Wang). For normally fertile women, FECHCG was, respectively: 43.2% [35.6, 51.1]; 38.1% [32.7, 43.7]; and 46.2% [42.8, 49.6]. FECCLIN was: 33.9% [29.4, 38.6]; 33.3% [27.6, 39.6]; and 34.9% [33.0, 36.8]. There was no apparent difference in πCLIN between fertile and sub-fertile sub-cohorts, which was estimated as: 78.3% [69.2, 85.3]; 87.5% [76.0, 93.9]; and 75.4% [71.5, 79.0]34.
Why do a proportion of menstrual cycles in women attempting to conceive fail to show any increase in hCG? Since FECHCG = πSOC × πFERT × πHCG, there can be various causes for this failure including mistimed coitus, anovulation, failure of fertilisation or pre-implantation embryo death. Although FECHCG puts limits on the extent of pre-implantation embryo loss, uncertainty in the estimates of πSOC, πFERT and πHCG translates into uncertainty in estimates of pre-implantation embryo mortality. In the Wang study, for normally fertile women, FECHCG = 46.2%; hence, the absolute maximum value for pre-implantation embryo loss must be 53.8%, although only if πSOC = πFERT = 1, conditions both extreme and unlikely34. Studies of the relationship between coital frequency and conception indicate that fecundability is greater with daily compared to alternate day intercourse34,84,85. Hence, when coital frequency is less than once per day a proportion of reproductive failure will be due to mistimed coitus, i.e., πSOC < 1. In the Wilcox study, coitus occurred on only 40% of the six pre-ovulatory days34,79, and in the Zinaman study participants were advised that alternate day intercourse was optimal65. Based on the difference in fecundability between daily and alternate day intercourse as modelled by Schwartz85, a value of πSOC = 0.80 was used to calculate pre-implantation embryo mortality34. However, this is a speculative estimate, and in reality the value may be higher, or lower.
A further critical missing piece of the equation is knowledge of the efficiencies of fertilisation and implantation under normal, natural, propitious circumstances. Assuming that either of these processes may be up to 90% efficient, and based on data from the three hCG studies63,65,66, a plausible range for pre-implantation embryo loss in normally fertile women is 10–40% and for loss from fertilisation to birth, 40–60%34. Even with these wide ranges of mathematically possible outcomes, it is clear that estimates for total embryonic loss of 90%29, 85%28, 83%31, 80–85%6,27, 78%26, 76%5,25 and 70%19–23 are excessive.
A previous review concluded that “at least 73% of natural single conceptions have no real chance of surviving 6 weeks of gestation”5,86. Live birth fecundability was estimated as “not over 15%”, substantially lower than Leridon’s 31%. Despite this discrepancy, Boklage’s conclusions were derived from a review of data including several hCG studies55,58–61,63 and Leridon’s analysis18. He derived a model describing the survival probability of human embryos comprising the sum of two exponential functions:
in which t is the time in days post-fertilization. This is the source of the 73% in the conclusion.
There are, however, serious problems with this analysis. Firstly, data presented as embryo survival probabilities at different times post-fertilization55,58,59,61,63 are fecundabilities, i.e., successes per cycle, not per fertilised embryo. Secondly, for reasons that are unclear, data from Whittaker60 and Leridon18 were excluded from the modelling analysis and the data from an earlier Wilcox report55 were included twice since this preliminary data had been incorporated into the later report63. Thirdly, the modelled data were normalised to a survival probability of 0.287 at 21 days post-fertilization. This value was derived from data published by Barrett & Marshall on the relationship between coital frequency and conception84. Barrett & Marshall had concluded that coitus during a single day alone, 2 days before ovulation resulted in a conception probability of 0.30. Boklage’s value of 0.287 is his calculated equivalent. However, conception in this study was “identified by the absence of menstruation, after ovulation”84. Hence, 0.30 (and similarly, 0.287) is a clinical fecundability and not a measure of embryo survival. Furthermore, 0.30 is a non-maximal fecundability, since it was an estimate based on coitus on a single day (2 days before ovulation) within the cycle. Barrett & Marshall clearly report that as coital frequency increased so did the fecundability, up to a maximum of 0.68 associated with daily coitus84.
Boklage’s analysis can only make biological sense if it is assumed that every cycle in the Barrett & Marshall study resulted in fertilisation. Under these circumstances, failure to detect conception in 71.3% (1 – 0.287) of cycles would be due entirely to embryo mortality. However, this is highly implausible and explicitly contradicted by the higher estimate of fecundability reported84. Boklage’s implicit assumption also contradicts his further conclusion that “only 60–70% of all oocytes are successfully fertilized given optimum timing of natural insemination”5. The vertical normalisation of the hCG study data to a value of 0.287 at 21 days is the principal determinant of the parameters that define the two exponential model. Any change in this value would commensurately alter the balance between the two implied sub-populations of embryos. Since it is evident that the value of 0.287 is neither an embryo survival rate nor even a maximal fecundability, it follows that quantitative conclusions from this analysis in relation to the survival of naturally conceived human embryos are of doubtful validity.
However, Boklage is right about two things: firstly, the difficulty of calculating pre-clinical losses, because “In the place of the necessary numbers for the first few weeks of pregnancy we find editorially acceptable estimates which, while perhaps not far wrong, are difficult to defend with any precision”, and secondly, that the source of some of the only directly relevant data (even though he excluded it from his modelling analysis), namely, “Hertig’s sample is, and will probably remain, unique”.
At the start of the 1930s, no-one had ever seen a newly fertilised human embryo. It was barely 60 years since Oscar Hertwig had first observed fertilisation in sea urchins87, and just 40 years before the birth of the first test tube baby88,89. In Boston, Dr Arthur Hertig and Dr John Rock’s search to find early human embryos generated an irreplaceable collection which has left an indelible mark on our understanding of human embryology.
Hertig and Rock recruited 210 married women of proven fertility who presented for gynaecological surgery38. (In most of their publications, the number is given as 21033,90,91 although 211 subjects are mentioned elsewhere38.) Of these, 107 were considered optimal for finding an embryo because they apparently: (i) demonstrated ovulation; (ii) had at least one recorded coital date within 24 hours before or after the estimated time of ovulation; (iii) lacked pathologic conditions that would interfere with conception. Hertig examined the excised uteri and fallopian tubes, and over fifteen years found 34 human embryos aged up to 17 days33,38,90–97. Of these, 24 were normal and 10 abnormal33,90. (There is some confusion over this: in three publications38,91,97, 21 embryos are described as normal and 13 as abnormal. It appears that the three alternatively described embryos (C-8299; C-8000; C-8290) were originally defined as abnormal based on their position or depth of implantation38.) Table 4 provides information about the 34 embryos found in these 107 women. Although the study was primarily intended to find and describe early human embryos, Hertig subsequently used the data to derive estimates of reproductive efficiency including early embryo wastage33,90.
Hertig’s analysis33,90 relies heavily on the 15 normal and 6 abnormal implanted embryos found in 36 women from cycle day 25 onwards. He assumed the 6 abnormal embryos would perish around the time of the first period concluding that fertility (% pregnant) at this stage = 42% (15/36). Of the 8 pre-implantation embryos identified (7 in the uterus and 1 in the fallopian tubes), 4 were abnormal. Hertig assumed the 4 normal embryos would implant successfully but that some of the abnormal ones would not, such that the proportion of normal embryos would increase from 50% (4/8) before implantation to 71% (15/21) after implantation as observed. Hence, among the 36 post-cycle day 25 cases, in addition to the 15 normal embryos, there must have been 15 abnormal pre-implantation embryos of which 60% (9/15) failed to implant and were not observed, and 40% (6/15) did implant and were observed, although these 6 would have perished shortly afterwards. This left 6/36 eggs that must have been unfertilised. The ratio of ‘unfertilised’ : ‘fertilised abnormal’ : ‘fertilised normal’ was therefore 6:15:15, matching the 16% infertility (no fertilisation), 42% sterility (post-fertilisation death) and 42% fertility (reproductive success) reported in Figure 9 of Hertig’s article, “The Overall Problem in Man”33. This is the source of Hertig’s 84% fertilisation rate and 50% embryo loss before and during implantation, and is reproduced in Leridon’s life table18 as 84/100 eggs surviving at time zero (ovulation and fertilisation) and 42 surviving to 2 weeks (time of first missed period).
Hertig provides almost the entire body of evidence used to quantify natural human embryo loss in the first week post-fertilisation. Most claims regarding early human embryo mortality find their source here. Before considering how reliable the figures are, it is worth repeating Hertig’s own caveat, namely, the lack of data on the efficiency of natural fertilisation33. All estimates of embryo mortality from fertilisation onwards are subject to commensurate inaccuracy in the absence of reliable fertilisation probabilities (i.e., πFERT), which are “surprisingly difficult to estimate”13.
There are several problems with Hertig’s analysis. As noted by others, the observations are cross-sectional, but the inferences are longitudinal48. Hertig detected 21 embryos from 36 cases (58.3%) from cycle day 25 onwards. If this detection rate were representative, then on average, prior to day 25, the detection rate should either be the same or higher; however, they are all lower, and substantially so (Table 4). Hertig suggested that this was due to the technical difficulty of finding newly fertilised embryos. However, the detection rate for cycle days 18–19 was good (46.7%) and embryos one or two days younger would not have been much smaller, at which stage the detection rate was poor (11.1%). An alternative explanation for this discrepancy might simply be random variation. Furthermore, from cycle day 25 onwards, embryos would probably have produced hCG and therefore FECHCG would have been at least 58%. This is approximately double the equivalent values observed in more recent and robust hCG studies (Table 3) further suggesting that this subset of the data is not representative.
Despite having proven fertility, these women presented with gynaecological problems, suggesting sub-optimal reproductive function. Furthermore, Hertig’s reproductively ‘optimal’ coital pattern does not include 2 days pre-ovulation and does include one day post-ovulation, conditions which are known not to maximise fertilisation34,79,84,85,98. Hence, detection rates before cycle day 25 may be more representative than those after. Given the numerical discrepancies, they cannot both be.
Hertig does not provide error estimates with his conclusions. In order to estimate the precision of his derived proportions, a bootstrap analysis was performed as follows: Hertig’s 107 optimal cases were categorised according to stage of cycle (Category 1 = cycle days 16–19 (n=24); Category 2 = cycle days 20–24 (n=47); Category 3 = cycle days ≥25 (n=36)), and presence and type of embryos (Category 0 = no embryo (n=73); Category 1 = normal embryo (n=24); Category 3 = abnormal embryo (n=10)). Five hundred pseudo-datasets each containing 107 cases were generated using a balanced random re-sampling method using Microsoft Excel®. The original and pseudo datasets are in Dataset 4.
Hertig’s numerical calculations, as detailed above, were repeated for each pseudo-dataset thereby generating 500 estimates for each parameter, from which median values and [95% CIs] were derived: fertility = 42% [26%, 59%]; sterility = 42% [5%, 182%]; infertility = 16% [-127%, 61%]; pre-implantation embryo survival probability = 69% [27%, 128%]; post-implantation to week two survival probability = 71% [50%, 91%]; detection rate for cycle day 25 onwards = 58% [41%, 74%]. Median values matched estimates calculated from the original dataset. Bootstrap 95% CIs for the day 25 detection rate (58%) matched those calculated using the “exact” method of Clopper & Pearson99, [41%, 74%], which are a little wider than those calculated using the “more exact” method of Agresti & Coull100, [42%, 73%]. (These analyses was performed using an online GraphPad® calculator accessed on 21st October 2016: http://www.graphpad.com/quickcalcs/ConfInterval1.cfm.) The congruence between these confidence intervals and the point estimates provides some reassurance that that the bootstrap procedure worked effectively. Estimates of parameters other than the day 25 detection rate (58%) are derived from more complex proportional relationships, and are therefore less precise. Table 5 reproduces a life table in the style of Leridon18 and includes probabilities for each reproductive step with confidence intervals. These intervals (and some noted above) are impossibly wide highlighting further problems with Hertig’s analysis.
Hertig’s analysis omits 47 cases from cycle days 20–24, comprising 44% of his data. It is clear why he cannot use it, since all five embryos were normal and, given his mathematical and biological assumptions, five normal implanting embryos could not become 29% (6/21) abnormal post-implantation. Furthermore, the data that define the 50% proportion of abnormal pre-implantation embryos (i.e., 4/8) are so few that any numerical variation will make a substantial difference to derived proportions. If he had observed 3/8 abnormal embryos, his estimate of pre-implantation loss would have been 13% rather than 30%: for 5/8 it would have been 48%, with a fertilisation rate of 111%, which is clearly impossible. It seems therefore, that Hertig designed his analysis based on a post-hoc examination and selective use of the data. His own caveat about the lack of relevant and necessary data should be taken at least as seriously as his conclusions.
Hertig and Rock’s contribution to human embryology is undeniable. However, their quantitative conclusions regarding early embryo mortality have a low precision that undermines their biological credibility or utility. Such estimates cannot be regarded as a reliable foundation upon which to evaluate and understand natural human reproduction.
Answering the question “How many fertilised human embryos die before or during implantation under natural conditions?” is difficult. Relevant, credible data are in short supply. Among regularly cited publications, the Lancet hypothesis26 is entirely speculative and in the view of the current author should cease to be used as an authoritative source. Clinical pregnancy studies are only useful for quantifying clinical pregnancy loss and contribute nothing to estimates of embryo mortality in the first two weeks’ post-fertilisation. Even Hertig’s unique dataset is inadequate to draw quantitative conclusions and oft-repeated values should be treated with scepticism. The hCG studies from 1988 onwards provide the best data for estimating embryo mortality although a lack of information on fertilisation rates13,15,33,48,101 prevents satisfactory completion of the calculations. A recent re-analysis of these data has proposed plausible limits for reproductively normal women indicating that approximately 10–40% of embryos perish before implantation and 40–60% do so between fertilisation and birth34. However, these ranges are wide, particularly for pre-implantation mortality, reflecting the lack of appropriate data. Is there any possibility of narrowing down the numbers?
Two separate groups have previously collected embryos from women following carefully timed artificial insemination as part of fertility treatment. Insemination around the time of ovulation in women of proven fertility was followed 5 days later by uterine lavage to recover ova102–105. These data appear to hold promise for determining fertilisation efficiency and some authors have made quantitative inferences about embryo mortality from them16,19,20. However, such inferences are complicated by numerous confounding factors. For example, in one series104, from 88 uterine lavages following artificial insemination by donor (AID), 4 unfertilised eggs, 6 fragmented eggs and 27 embryos from 2 cell to blastocyst stage were retrieved. In the 51 cycles in which no egg or embryo was retrieved, there was one retained pregnancy suggesting that the lavage and ova retrieval efficiency was reasonably high, albeit not perfect. These data therefore suggest that FECTOT was low (≈31/88 = 35%) although a proportion of fertilised eggs may have completely degenerated within the first 5 days. Assuming πSOC was high (given the targeted insemination), this suggests that πFERT ≈ 50%. In the context of the recent analysis34, this implies that πHCG is high and that levels of embryo mortality are therefore towards the lower end of the 10–40% and 40–60% ranges. However, the clinical pregnancy rate following transfer of the embryos was only 40%. This is equivalent to πHCG × πCLIN. If πCLIN ≈ 75%, as suggested by the hCG studies, this would mean that πHCG ≈ 50%. This would imply that πFERT is high, fertilised egg degeneration is high, occurs before day 5 and was therefore unobserved, and hence levels of embryo mortality tend towards the upper end of the 10–40% and 40–60% ranges.
It is possible that the lavage/transfer procedure reduced implantation and early developmental efficiency thereby reducing πHCG × πCLIN. A comparison of AID pregnancy rates may provide some insight as suggested by the authors104. The clinical pregnancy rate in their pharmacologically unstimulated cohort was 12.5% (11/88) which is lower than an equivalent 18.9% observed for fresh semen AID106, and also the live birth rate (which also incorporates clinical pregnancy losses) of 14.7% reported by the HFEA for AID in 2012 in unstimulated women aged 18–34107. These different success rates suggest that the lavage/transfer procedure did adversely affect implantation and early gestation with clear implications for quantitative extrapolation. Furthermore, the women who were embryo recipients were receiving fertility treatment and their overall fertility may have been lower than expected in a normal healthy cohort. In summary, it seems that there are too many unresolved variables in these data to narrow down estimates of fertilization (πFERT) or implantation (πHCG) rates.
With high fecundability, the range of possible embryo mortality rates falls. Red deer hinds have pregnancy rates of >85% following natural mating108: establishing numerical limits for embryo mortality under these efficient reproductive circumstances is more straightforward. By contrast, humans lack the instinct to mate predominantly during fertile periods thereby reducing observed reproductive efficiency substantially. In studies of early pregnancy loss, owing to sub-optimal coital frequency and cohorts including sub-fertile couples, natural fecundability was almost certainly not maximised34. Combining data on coital frequency and hCG elevation may help to address this. In a later analysis, applying the Schwartz model85 to hCG data, Wilcox calculated a FECHCG value of 36% for high coital frequencies (>4 days with intercourse in 6 pre-ovulatory days)79. However, the model assumed that cycle viability was evenly distributed among couples, a condition which the authors recognised was not true and is contradicted by a subsequent analysis which suggests that approximately a quarter of the Wilcox cohort was sub-fertile34. If possible, focussing analytical attention on normally fertile women with the highest coital frequencies may help to further narrow the range of plausible embryo mortality.
In this review of natural early embryo mortality no use has been made of data from in vitro fertilisation (IVF) and associated laboratory studies. Sub-optimal conditions for embryo culture mean that it was109,110 and probably still is111 doubtful that reliable values can be extrapolated from laboratory in vitro to natural in vivo circumstances20. Importantly, the reproductive stages are also altered. In IVF, πSOC = 1 and for transferred embryos πFERT = 1. Furthermore, transferred embryos are selected based on quality criteria, however inexact those may be111,112. IVF program manipulations may reduce πHCG compared to natural circumstances3 and implantation failure remains a substantial issue for IVF113,114. Although for IVF cycles, the reported live birth rate per cycle has gone up (from 14% in 1991 to 25.4% in 201234), comparison of IVF success rates and natural live birth fecundability values involves too many undefined variables to shed numerical light on early natural embryo development and mortality.
In vitro fertilisation per se may provide some insight into values of πFERT, since πSOC = 1, and successful fertilisation can be observed. In seven studies of natural cycle IVF, fertilisation was successful in 70.9% (443/625) of attempts115–121. If this represented natural, in vivo fertilisation, based on the recent analysis34, it implies that πHCG ≈ 0.75, focusing estimates for pre-implantation embryo loss on 25%, and for total loss on 50%. However, high frequencies of chromosomal aberrations caused by the in vitro handling of human oocytes122 can render any comparison of natural and assisted reproduction open to criticism4.
In calculating summary values of embryo mortality, it is important to note that human fertility is as numerically heterogeneous as it could possibly be. Some couples are infertile and some are highly fertile. Excessive attention to averages and neglect of variances fosters a misleading appreciation of reality. The hCG studies clearly had both fertile and sub-fertile participants: use of overall values underestimated fecundability for the fertile majority34. Furthermore, apparently ‘optimal’ conditions for conception may not maximise human biological fecundability. Other biological factors also contribute to reproductive heterogeneity in humans; however, even after controlling for age-related decline, fecundability remains highly variable107,123. For intercourse occurring 2 days prior to ovulation, average fecundabilities resembled those previously published124, but for couples at the 5th and 95th percentiles, fecundabilities were 5% and 83%. 83% fecundability implies a very low embryo mortality rate. In conclusion, apparent low fecundability in humans need not necessarily be caused by embryo mortality, but also defects of ovulation, mistimed coitus, or fertilisation failure34. Where fecundability is low, any or all of these factors may contribute.
Pregnancy loss and embryo mortality under natural conditions are real and substantial. However, estimates of 90%29, 85%28, 80%6,27, 78%26, 76%5,25 and 70%19–23 loss are excessive and not supported by available data. Estimates for clinical pregnancy loss are approximately 10–20%. For women of reproductive age, losses between implantation and clinical recognition are approximately 10–25%. Loss from implantation to birth is approximately one third34,63,65,66.
Natural pre-implantation embryo loss remains quantitatively undefined. In the absence of knowledge of πSOC and πFERT it is almost impossible to estimate precisely. Hertig’s estimate is 30%; however, mathematically and biologically implausible confidence intervals [-28%, 73%] betray the quantitative weaknesses in his data and analysis. The best available data are from studies monitoring daily hCG levels in women attempting to conceive63,65,66. Based on analyses of these data, in normal healthy women, 10–40% is a plausible range for pre-implantation embryo loss and overall pregnancy loss from fertilisation to birth is approximately 40–60%34. This latter range is similar to, although a little narrower than the 25–70% suggested by Professor Robert Edwards125.
In the absence of suitable data to quantify pre-implantation loss, many published articles and reviews merely restate previously published values6,20,21. It has been suggested that “for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias”126. Widely held views on early embryo mortality may reflect an entrenched and biased view of the biology. For example, the Macklon “Black Box” review20 has been cited over 200 times (Web of Knowledge citations on 10th October 2016) with many articles explicitly referencing its 30% survival/70% failure value8,21,113,127–133. Macklon’s quantitative summary in his “Pregnancy Loss Iceberg” (30% implantation failure; 30% early pregnancy loss; 10% clinical miscarriage; 30% live births) is a direct, unedited reproduction of estimates published over 10 years previously19. 30% pre-implantation loss fairly represents Hertig’s conclusions although, as has been shown, this estimate is highly imprecise. However, Macklon misrepresents the best data which he reviews63,65. Wilcox reports early pregnancy loss (i.e., [1 - πCLIN]) of 21.7% whereas Macklon’s iceberg implies that 43% (30/70) of implanting embryos fail before clinical recognition. The iceberg’s clinical loss rate of 25% (10/40) is also higher than relevant data indicate (Figure 2 & Figure 3). Total loss of implanting (hCG+) embryos (i.e., [1 - (πCLIN × πLB]) is 57% (40/70) according to the iceberg. By contrast, Wilcox63 and Zinaman65, both included in Macklon’s review, both report that only 31% of hCG positive pregnancies fail.
If Macklon’s (and Chard’s19) estimates are excessive as the data suggest, this casts doubt on claims113,132 that the frequency of embryonic abnormalities observed in vitro is representative of the natural in vivo situation. In turn, this implies that many of the chromosomal abnormalities observed in in vitro human embryos are, to a greater extent than currently recognised113, an artefact of the clinical and experimental context of assisted reproduction technologies.
In attempting to quantify pre-implantation embryo mortality it is easy to appreciate why “a claim of ‘no significant difference’ might easily be sustained against any interpretation proffered”48, and why estimates are “difficult to defend with any precision”5. In conclusion, “poor estimates of fertilization failure rate and the mortality at 2 weeks after fertilisation”15 drawn “from unusual or biased samples”134 indicate that the “black box” of early pregnancy loss20 is not as wide open as has been thought.
F1000Research: Dataset 1. Figure 1 data, 10.5256/f1000research.8937.d140569135
F1000Research: Dataset 2. Figure 2 data, 10.5256/f1000research.8937.d140570136
F1000Research: Dataset 3. Figure 3 data, 10.5256/f1000research.8937.d140571137
F1000Research: Dataset 4. Pseudo-datasets of Hertig’s study, obtained via a bootstrap procedure, 10.5256/f1000research.8937.d140572138
Thanks are due to Professor David Paton, Dr Paul Schofield and Dr Amanda Sferruzzi-Perri for reviewing and providing helpful comments during the writing of this article.
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References
1. Austin C. R.: Pregnancy losses and birth defects.Reproduction in Mammals 2: Embryonic and Fetal Development. 1972. 134-153Competing Interests: No competing interests were disclosed.
Competing Interests: Conducting peer review may be beneficial to my career. I am funded by a Doctoral Research Fellowship from the National Institute for Health Research (DRF-2014-07-050). The views expressed in this peer review are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health. JW is a statistical editor of the Cochrane Gynaecology and Fertility Group.
Competing Interests: No competing interests were disclosed.
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