Fertilization rate is an independent predictor of
implantation rate
Mitchell P. Rosen, M.D.,a Shehua Shen, M.D.,a Paolo F. Rinaudo, M.D., Ph.D.,a Heather
G. Huddleston, M.D.,a Charles E. McCulloch, Ph.D.,b and Marcelle I. Cedars, M.D.a
a
Department of Obstetrics, Gynecology and Reproductive Sciences, and
University of California, San Francisco, California
b
Department of Epidemiology and Biostatistics,
Objective: To determine whether fertilization rate serves as a biological assay, reflects oocyte quality, and may be
used to help predict patient implantation rate.
Design: Retrospective cohort study.
Setting: Academic center.
Patient(s): Couples undergoing 3603 in vitro fertilization (IVF) cycles from 2001 to 2007.
Intervention(s): None.
Main Outcome Measure(s): We compared the implantation rate among cycles with high versus low fertilization
rate. Univariate analyses were performed to determine the association of implantation rate with potential confounding variables: age, day-3 follicle-stimulating hormone level, day-3 estradiol level, antral follicle count, oocyte
number, cycle attempts, embryo grading, and number of embryos transferred. Multivariate analysis was then performed to determine whether the fertilization rate remained an independent predictor.
Result(s): Cutoffs for fertilization rate were 50% for intracytoplasmic sperm injection (ICSI) and 75% for conventional insemination. Higher ICSI fertilization was statistically significantly associated with the implantation rate
(25.2% vs. 17.8 %). After adjusting for variables associated with implantation rate, fertilization rate for ICSI
remained a strong independent predictor of implantation. Higher conventional insemination fertilization was
statistically significantly associated with implantation (32.1% vs. 25.7%) and remained a statistically significant
predictor after adjustment.
Conclusion(s): Fertilization is a strong, independent predictor of implantation rate and may be useful in modeling
to guide decision making for the number of embryos to transfer. (Fertil Steril 2010;94:1328–33. 2010 by American Society for Reproductive Medicine.)
Key Words: Fertilization rate, IVF, predictors, pregnancy, implantation rate, ART, oocyte quality, ICSI
The high incidence of multiple births is a significant complication of in vitro fertilization (IVF) treatment. The balance
between the desires to maintain pregnancy rates while decreasing multiple births is often times difficult to achieve.
The inability to accurately predict whether an individual embryo will implant often leads to the transfer of multiple
embryos to ensure implantation of at least one. The most
common method of determining the number of embryos to
transfer is limited to few patient characteristics, embryo morphologic criteria, and number of prior cycles (1). Using these
criteria, the current guidelines from the Society for Assisted
Reproductive Technology (SART) have resulted in a decrease
in the number of higher order pregnancies but without a significant reduction in the twinning rate (2).
To maintain the pregnancy rate and further minimize the
number of embryos transferred, additional important predictors of implantation need to be identified. Much attention has
Received October 16, 2008; revised May 8, 2009; accepted May 11, 2009;
published online June 27, 2009.
M.P.R. has nothing to disclose. S.S. has nothing to disclose. P.F.R. has
nothing to disclose. H.G.H. has nothing to disclose. C.E.M. has nothing
to disclose. M.I.C. has nothing to disclose.
Reprint requests: Mitchell P. Rosen, M.D., UCSF Center for Reproductive
Health, 2356 Sutter Street, 8th floor, Box 0916, San Francisco, CA
94115 (FAX: 415-353-7744; E-mail: [email protected]).
1328
focused on the morphologic characteristics of the oocyte or
embryo destined for transfer. For example, various morphologic characteristics of the oocyte, such as zona pellucida
thickness, appearance of the cytoplasm, and polar bodies
have been investigated (3–6). However, the literature is conflicting, and it is difficult to assess the actual impact of each of
these parameters (7, 8). Embryo grading does correlate with
pregnancy outcome and is the most widely used assessment
to determine which embryos to transfer, although with obvious limitations. Other than standard morphology, investigators have determined that assessment at the zygote stage,
evaluation of embryo behavior at early cleavage, or extended
culture performed to day 5 improves pregnancy outcomes (9,
10). With advances in technology, there is active research at
the molecular level to increase our prediction of implantability (11). However, as we await the development of predictive
molecular markers, recommendations for embryo transfer
number will depend on the inclusion of multiple covariates
and will likely include the combination of patient characteristics, ovarian response, and laboratory findings both at the
cohort level and the individual embryo level.
In our practice, we have observed considerable variability
in fertilization rates. Therefore, we questioned whether fertilization could serve as a potential predictor of implantation at
the cohort level. With conventional insemination, the
Fertility and Sterility Vol. 94, No. 4, September 2010
Copyright ª2010 American Society for Reproductive Medicine, Published by Elsevier Inc.
0015-0282/$36.00
doi:10.1016/j.fertnstert.2009.05.024
variables that contribute to fertilization rate are numerous. Intracytoplasmic sperm injection (ICSI) has alleviated the impact of sperm quality, and has removed the nuclear
immature oocyte as an etiology of failed fertilization. However, even with ICSI, there remains considerable variation
in fertilization rate. The etiology has been attributed to a number of factors. Most notably, the fertilization rate depends on
the skill level of the ICSI technician, the cause of the male infertility, and/or the sperm origin (i.e., testis, epididymis, or
ejaculate) (12–15). Additionally, some propose that DNA
damage may have an impact on fertilization with ICSI (16–
19). Others suggest that inherent oocyte quality or failure
of cytoplasmic maturation may contribute to fertilization failure (6, 20–22). Likely, the variation in fertilization rate is
a composite of multiple factors, each having some independent effect and potentially predictive of oocyte and/or sperm
health.
We were interested in whether the fertilization potential
was a function of the oocyte cohort quality. Therefore, in
this study we initially explored the predictive value of fertilization rate after ICSI on implantation. We chose ICSI to
eliminate confounding with immature oocytes and any negative impact of sperm function. Once we noted a difference
with ICSI cycles, we repeated separately an analysis with
conventional insemination, aware that there are additional
factors beyond oocyte quality that impact successful fertilization with conventional insemination. We included all cycles,
separated by type of insemination and adjusted for known
variables that were associated with the likelihood of implantation, to determine the magnitude of the impact that fertilization rate has on implantation.
MATERIALS AND METHODS
Data from couples undergoing IVF/ICSI from 2001 to 2007
were reviewed. A total of 3603 cycles were analyzed. Cases
where sperm retrieval was required were excluded from the
analysis. The study was approved by the institutional review
board at the University of California, San Francisco.
Treatment Regimen
All patients underwent standard ovarian stimulation protocols (59% long luteal, 15.2% antagonist, and 25.8% microdose flare). Transvaginal ultrasound assessment of follicular
growth and endometrial thickness commenced on day 4 or
5, and serum estradiol levels were obtained at each clinic
visit. Thereafter, the physician, based on the ultrasonographic
findings and serum estradiol levels, adjusted the frequency of
monitoring and the amount of gonadotropins.
The day of the human chorionic gonadotropin (hCG) trigger (and thus the total days of stimulation) was based on follicle size and number in addition to the serum estradiol level.
The dose of hCG was 5000 or 10,000 units, depending upon
the risk for hyperstimulation. The egg retrieval was performed 36 hours after administration of hCG. The embryos
were transferred on day 2 through 5, depending on clinical
Fertility and Sterility
scenario (20% day 2, 77% day 3, and 3% day 4 or 5). All cycles had luteal-phase support with 50 mg intramuscular progesterone in oil beginning 2 days after the retrieval, and 2 mg
of estradiol beginning 6 days after hCG administration.
ICSI
After oocyte retrieval, the cumulus was denuded 38 to 42
hours after hCG administration. Stripping was accomplished
by placing the cumulus–oocyte complex in 80 IU/mL of hyaluronidase (Sage Biopharma, Bedminster, NJ) for 30 to 60
seconds, rinsing five to six times in working solution
(mHTF þ 10% SSS or GMOPS þ 5% HSA), and then mechanically removing the cumulus from the oocyte using
a plastic pipette (ID ¼ 135 mm; MidAtlantic Diagnostics,
Inc., Mount Laurel, NJ).
The sperm was first visualized under 40 magnification
and then was immobilized in 7% polyvinylpyrrolidone
(PVP; Irvine Scientific, Santa Ana, CA) using the tip of the
injection needle (Humagen Fertility Diagnostic, Inc., Charlottesville, VA). It was then aspirated tail-first into the injection needle. The oocyte was grasped with a holding pipette at
9 o’clock using gentle suction, then was rotated such that the
first polar body was located at either the 6 o’clock or 12
o’clock position. The injection pipette was pushed through
the zona at the 3 o’clock position and advanced to the outer
surface of the oolemma. The oolemma was penetrated by
direct penetration technique. Entry into the oocyte was confirmed by free flow of cytoplasm into the injection pipette.
The cytoplasm was then slowly injected back into the oocyte
until the sperm was seen to pass the tip of the injection pipette
and slide into the cytoplasm. The procedure was concluded
by gently removing the injection pipette.
Conventional Insemination
Oocytes undergoing conventional insemination were
grouped in 200-mL drops and fertilized with approximately
100,000 spermatozoa/mL 4 hours after the retrieval.
Evaluation of Fertilization
Normal fertilization was identified by the presence of two
pronuclei (2PN) at the time of fertilization assessment, 16
to 19 hours after ICSI or conventional insemination.
Statistical Analysis
A Lowess plot was performed to determine the relationship
between ICSI fertilization rate (2PN/number of oocytes injected), conventional insemination fertilization rate (2PN/
number of oocytes inseminated), and implantation rate. A
cutoff was established for each method of fertilization where
the change in implantation approximated a plateau in the
Lowess plot, thus distinguishing the low and high fertilization rates. Variables considered to be predictive of implantation (a priori based on the literature) were described
separately by high or low fertilization rate. These included
1329
TABLE 1
Characteristics of patient cycles (mean and standard deviation) stratified by high and low fertilization
and method of fertilization.
Intracytoplasmic sperm injection
Characteristic
IR
Age (y)
FSH
No. of attempts
Oocytes recovereda
E2 at day of hCGa
Average cell numbera
Average fragmentation
Conventional insemination
FR %50%
>50%
FR <75%
FR R75%
17.8%
36.2 5.2
7.4 2.8
1.6 1.0
12.3 8.0
2049 1256
5.5 2.1
2.3 09
25.2
35.5 5.3
7.3 3.2
1.5 0.9
14.3 8.6
2390 1444
6.7 1.8
2.2 09
25.7%
35.6 5.6
7.1 4.2
1.4 0.9
16.5 9.5
2646 1499
6.7 1.8
2.2 09
32.1%
35.3 5.4
7.2 3.2
1.4 0.8
13.7 8.6
2346 1369
7.0 1.5
2.2 09
Note: E2 ¼ estradiol; FR ¼ fertilization rate; FSH ¼ follicle-stimulating hormone; hCG ¼ human chorionic gonadotropin;
IR ¼ implantation rate.
a
P< .05.
Rosen. Fertilization rate predicts implantation. Fertil Steril 2010.
patient characteristics (age, day-3 FSH, and number of cycle
attempts), ovarian response (estradiol concentration on the
day of hCG administration, and oocyte number), and embryo
quality (average cell number and fragmentation).
Univariate analyses adjusting for repeat measures were
performed to evaluate the magnitude of effect on implantation rate for each predictor as well as on higher fertilization.
Multivariate analyses were then performed to adjust for all
variables that were statistically significantly associated with
implantation to determine whether the fertilization rate remained an independent predictor. Sensitivity analyses were
performed for the first cycle and for day-2 and day-3 embryo
transfers. Descriptive analyses were performed on the raw
data to compare the effect of a woman’s age (32 to 34 years
old versus 38 to 40 years old) with fertilization rate (high versus low).
The data was analyzed in Stata, version 7.0 (Stata Corporation, College Station, TX). The logistic multivariate analyses were performed using the generalized linear model
routines with adjustment for clustering using Pearson correction for overdispersion. A two-sided P<.05 was considered
statistically significant.
RESULTS
The fertilization rate with ICSI was 72.2% and for conventional insemination was 55.0%, and the overall implantation
rate was 24.7%.
Using Lowess analyses, the cutoff for a binary predictor
was set at 50% for ICSI and 75% for conventional insemination. A total of 2444 cycles (1175 first attempts) were analyzed for ICSI, comprising 2150 cases where the
fertilization rate was >50% (high) and 294 cases where the
1330
Rosen et al.
Fertilization rate predicts implantation
fertilization rate %50% (low). The implantation rate was
25.2% versus 17.8%, respectively. A total of 1159 cycles
(655 first attempts) were analyzed for conventional insemination, comprising 286 cases where the fertilization rate was
R75% (high) and 863 cases where the fertilization rate
<75% (low). The implantation rate was 32.1% versus 25.7,
respectively (Table 1).
The patient characteristics, ovarian response, and embryo
quality are given in Table 1, stratified by the fertilization
rate and the method of fertilization. For the ICSI group, the
average age was younger (35.50 vs. 36.18, P<.02), and the
number of oocytes retrieved was higher (14.31 vs. 12.26,
P<.0001) in cases where the fertilization rate with ICSI
was >50%. The estradiol concentration on the day of hCG
administration, number of oocytes retrieved, and the average
embryo cell number were statistically significantly higher in
cases where the fertilization rate was >50%. For the conventional insemination group, the number of oocytes retrieved
and estradiol concentration on the day of hCG were higher
in cases where the fertilization rate were <75%. However,
the average embryo cell number was statistically significantly higher where the where conventional insemination
rate was R75%.
The results from the univariate analyses are shown in Table
2. For a high fertilization rate (>50%), the odds of implantation with ICSI are 65% higher than for low fertilization (OR
1.65; 95% CI, 1.2, 2.1), and with conventional insemination
(R75%) the odds are 50% more likely (OR 1.50; 95% CI,
1.15, 1.97). In comparison, the effect of age on implantation
is that for every year increase, the odds of implantation decrease by 8% (OR 0.92; 95%, CI 0.91, 0.94). The number
of prior attempts, number of oocytes collected, estradiol on
the day of hCG, and the embryo quality were also statistically
significantly correlated with implantation rate. Basal FSH
Vol. 94, No. 4, September 2010
TABLE 2
Univariate logistic regression analyses to determine the odds ratio, P value, and 95% confidence
interval for each predictor of implantation rate.
Intracytoplasmic sperm injection
IR predictor
ICSI FR >50%
Conventional FR R75%
Age (y)
Day-3 FSH
No. of attempts
E2 at hCGa
Oocytes collected
Average cell number
Average fragmentation
Oligozoospermia
Teratozoospermia
Odds ratio
P value
95% CI
1.65
< .001
1.28–2.13
0.93
0.97
0.84
1.05
1.04
1.31
0.82
1.06
1.04
< .001
.100
< .001
< .001
< .001
< .001
< .001
.58
.62
0.91–0.94
0.94–1.01
0.76–0.92
0.99–1.03
1.02–1.06
1.21–1.41
0.74–0.91
0.87–1.29
0.87–1.27
Conventional insemination
Odds ratio
P value
95% CI
1.51
0.94
1.01
0.92
1.01
1.04
1.25
0.77
1.17
0.88
.003
< .001
.56
.32
.25
< .001
< .001
< .001
.61
.40
1.15–1.99
0.91–0.98
0.98–1.05
0.80–1.07
0.99–1.04
1.03–1.05
1.19–1.31
0.66–0.90
0.65–2.09
0.67–1.17
Note: CI ¼ confidence interval; E2 ¼ estradiol; FR ¼ fertilization rate; FSH ¼ follicle-stimulating hormone; ICSI ¼ intracytoplasmic
sperm injection; hCG ¼ human chorionic gonadotropin; IR ¼ implantation rate.
a
E2 pg/mL at hCG is per unit change of 200 pg/mL.
Rosen. Fertilization rate predicts implantation. Fertil Steril 2010.
levels as a continuous variable were not statistically significantly associated with implantation rate.
Table 3 shows the multivariate analysis estimating the independent effect of ICSI >50% and conventional insemination R75% fertilization rates while adjusting for all the
potential confounders revealed in the univariate analyses.
The analysis confirms that the ICSI fertilization rate of
>50% (AOR 1.54; 95% CI, 1.14, 2.07) and conventional insemination R75% (AOR 1.53; 95% CI, 1.11, 2.11) remained
independent predictors of implantation rate. The sensitivity
analyses that included first-cycle-only showed similar results.
The descriptive data on the implantation rate stratified by
fertilization rate for women 32 to 34 years of age (young)
and 38 to 40 years of age (older) are shown in Table 4. For
TABLE 3
Multivariate logistic regression analysis to evaluate the independent effects of each predictor on
implantation rate.
Intracytoplasmic sperm injection
IR predictor
ICSI FR >50%
Conventional FR R75%
Age (y)
Day-3 FSH
No. of attempts
E2 at hCGa
Oocytes collected
Average cell number
Average fragmentation
Oligozoospermia
Teratozoospermia
Odds ratio
P value
95% CI
1.65
< .001
1.28–2.13
0.93
0.97
0.84
1.05
1.04
1.31
0.82
1.06
1.04
< .001
.100
< .001
< .001
< .001
< .001
< .001
0.58
0.62
0.91–0.94
0.94–1.01
0.76–0.92
0.99–1.03
1.02–1.06
1.21–1.41
0.74–0.91
0.87–1.29
0.87–1.27
Conventional inseminationa
Odds ratio
P value
95% CI
1.51
0.94
1.01
0.92
1.01
1.04
1.25
0.77
1.17
0.88
.003
< .001
.56
.32
.25
< .001
< .001
< .001
.61
.67
1.15–1.99
0.91–0.98
0.98–1.05
0.80–1.07
0.99–1.04
1.03–1.05
1.19–1.31
0.66–0.90
0.65–2.09
0.67–1.17
Note: CI ¼ confidence interval; E2 ¼ estradiol; FR ¼ fertilization rate; FSH ¼ follicle-stimulating hormone; hCG ¼ human
chorionic gonadotropin; ICSI ¼ intracytoplasmic sperm injection; IR ¼ implantation rate.
a
E2 pg/mL at hCG is per unit change of 200 pg/mL.
Rosen. Fertilization rate predicts implantation. Fertil Steril 2010.
Fertility and Sterility
1331
TABLE 4
Implantation rate for two age groups stratified by high and low fertilization rate and method of
fertilization.
Intracytoplasmic sperm injection
Age group
Conventional insemination
FR %50%
FR >50%
Overall FR%
FR <75%
FR R75%
Overall FR%
17.6
9.9
13.0
34.0
19.1
24.5
31.0
18.2
23.0
18.0
19.4
41.0
19.0
28.1
31.0
18.3
32–34
38–40
Overall IR
Note: FR ¼ fertilization rate; IR ¼ implantation rate.
Rosen. Fertilization rate predicts implantation. Fertil Steril 2010.
the group who underwent ICSI, the odds of implantation were
2.0 times higher for young women versus older women. In
comparison, the odds of implantation were 2.2 times more
for the higher ICSI fertilization compared with the lower fertilization. For the group who underwent conventional insemination, the odds of implantation were similarly 2.0 times higher
for young women versus older women. In comparison, the
odds of implantation were 1.62 times higher for higher conventional insemination fertilization versus lower fertilization.
DISCUSSION
This study shows that cohort fertilization rate is a statistically
significant predictor of the individual implantation rate even
after adjustment for common variables known to be associated with implantation. The strength of the effect (see Table
4) is similar to the difference in implantation comparing the
younger (32 to 34 years) to older (38 to 40 years) women.
This is the first study to reveal the association of fertilization
rate with implantation rate, thereby suggesting that fertilization rate may serve as a bioassay of oocyte health.
It is possible that the regulatory mechanisms that are required for fertilization may influence the development and
health of the preimplantation embryo (22). Previously we
had shown that triploidy formation after ICSI is associated
with stimulation variables and was a predictor of implantation
rate (23). The finding in our present study, that cohort fertilization rate is also associated with implantation rate, further
suggests fertilization outcomes may serve as a bioassay of
oocyte quality and can be a valuable clinical determinant of
oocyte health. To begin to understand how to improve care,
more studies are needed to elucidate the mechanisms involved in fertilization and developmental competence. It is
possible that alterations in ovarian stimulation may improve
oocyte quality, fertilization potential, and hence implantation.
Biologically, the inherent quality of the oocyte influences
the molecular and cellular mechanisms for fertilization
(22). For fertilization to occur, the oocyte must undergo
a complex series of events for proper nuclear and cytoplasmic
maturation, and inadequacies cannot be seen by visual inspection. It is evident that inadequate oocyte maturation
1332
Rosen et al.
Fertilization rate predicts implantation
can impede fertilization. Further, it has been shown that ovarian stimulation may alter oocyte metabolism and alter zona
pellucida formation, and may create asynchrony between nuclear and cytoplasmic maturation (24, 25). To support this
finding, we previously showed that the formation of nuclear
maturation is not the single determinant that allows fertilization; oocytes from smaller follicles are less likely to fertilize,
despite apparent nuclear maturation (26).
The ultimate goal is to determine, with predictive modeling, cycles for which implantation is likely so that we can decrease the incidence of multiple gestations by decreasing the
number of embryos transferred. There have been numerous
studies that show that predictors such as age, duration of infertility, baseline hormone levels, antral follicle count, oocyte
number, and oocyte/embryo quality are associated with pregnancy outcome (10, 27–30). Although many factors influence
the chance of implantation in IVF, limited assessment using
age, embryo morphology, and having additional embryos to
freeze are regarded as standard to determine the number of
embryos to transfer (1). A limitation is that each of these predictors, including fertilization rate, has a modest impact. Several models have been created to determine good prognosis
patients (31–33).
However, existing models are limited in their ability to predict the likelihood of pregnancy with great efficiency (34).
One likely reason is that as yet unidentified predictors are
not included in the model, thus limiting the ability of any
model to fully explain implantation potential. Identifying additional independent predictors, such as fertilization rate, will
increase our efficiency at predicting pregnancy potential, and
thus will allow more individualization of treatment decisions
and minimization of the number of embryos transferred,
thereby decreasing multiple pregnancy rates.
Limitations of our study include the possibility of bias due
to the retrospective nature of the study. There were baseline
differences in the population of patients who had high and
low ICSI and conventional insemination fertilization rates.
We adjusted for these potential confounders simultaneously
to isolate the effect of fertilization rate on implantation
rate. Because the differences in characteristics observed between the strata have wide standard deviations, we feel that
Vol. 94, No. 4, September 2010
there is significant overlap and the adjustment is valid. Additionally, it is not known whether the sperm quality had a significant impact on the outcomes. However, the analysis that
only evaluated ICSI cases limited sperm factors, so it supports our hypothesis that fertilization rate may be a component of oocyte health.
It is not known whether fertilization rate is sufficiently predictive to determine good prognosis patients so that we can
limit the number of embryos to transfer. A clinical trial designed to include fertilization rate in a predictive model
would best answer this question. However, further predictors
are needed before a model can be used to determine the optimal number of embryos to transfer to maintain pregnancy
rates and reduce multiple gestations.
We have shown that fertilization rate is a strong, independent predictor of implantation rate and may be used to improve subsequent treatment decisions and in a model to
guide decision making for the number of embryos to transfer.
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