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. 2021 Feb 11;16(2):e0246440. doi: 10.1371/journal.pone.0246440

The impact of preovulatory versus midluteal serum progesterone level on live birth rates during fresh embryo transfer

Abdelhamid Benmachiche 1,*, Sebti Benbouhedja 1, Abdelali Zoghmar 1, Peter Samir Hesjaer Al Humaidan 2,3
Editor: Stephen L Atkin4
PMCID: PMC7877612  PMID: 33571260

Abstract

Background

Conflicting evidence still prevails concerning the effect of preovulatory elevated progesterone (EP4) on reproductive outcomes in fresh embryo transfer (ET). However, few studies have analyzed the effect of EP4 on the likelihood of pregnancy using multivariate regression approach. The potential confounding factors tested in these studies were limited to either patient’s characteristics or to stimulation related parameters. Yet, several studies have shown that postovulatory parameters such as midluteal progesterone (P4) level may be considered as a proxy variable of endometrial receptivity as well.

Objective

The aim of the present study was to estimate the independent effect of preovulatory P4 effect, if any, on the probability of live birth (LB) by considering the midluteal endocrine profile when controlling for the potential confounding factors.

Methods

This is a secondary data analysis of a cohort of fresh IVF/ICSI cycles triggered with GnRH agonist (n = 328) performed in a single IVF center during the period 2014–2016. Patients contributed only one cycle and were stratified into four groups according to preovulatory P4 quartiles. We assessed the association between preovulatory P4 and the odds of LB calculated by logistic regression analysis after controlling for the most clinically relevant confounders. The primary outcome measure: Live birth rates (LBR).

Results

Both preovulatory and midluteal P4 were significantly correlated with the ovarian response. Logistic regression analysis showed that preovulatory serum P4 did not have a significant impact on LBR. In contrast, midluteal serum P4 level was an important independent factor associated with LBR. The optimal chance of LBR was achieved with midluteal serum P4 levels of 41–60 ng/ml, [OR: 2.73 (1.29–5.78); p< 0.008].

Conclusion

The multivariate analysis suggests that the midluteal P4 level seems to impact LBR more than the preovulatory P4 level in women undergoing IVF treatment followed by fresh ET.

Introduction

During the natural cycle, the progesterone (P4) rise precedes the luteinizing hormone (LH) peak on the day of ovulation [1], and thus, may play a physiological role in the ovulation process [2]. After ovulation, P4 is essential for the secretory transformation of the endometrium and, thus, for the implantation [3,4]. However, during controlled ovarian hyperstimulation, 6–30% of cycles may display a rise in serum P4 levels particularly at the end of stimulation mostly higher than those of natural cycle [5,6]. The preovulatory P4 rise might be attributed to an increased number of follicles, each one produces a physiological amount of P4, rather than P4 being produced by granulosa cells as a consequence of premature luteinization [7]. Over the years, different thresholds of serum P4 have been proposed ranging from 0.8 to 2 ng/mL above which deleterious in vitro fertilization (IVF) outcome may occur during fresh embryo transfer (ET) [5,6]. Although the impact—or not of elevated progesterone (EP4) on reproductive outcomes has been debated for almost three decades [79], this controversy remains unsolved, presumably due to the lack of well-designed studies. In this respect, strong reservations have recently been expressed regarding methodological approaches applied to address this question, indicating that live birth (LB) is a multifactorial process that cannot be defined by a single threshold value, in most instances suggested arbitrarily after dichotomizing continuous data [1012]. Further, the weakness of bivariate analysis in this context has also been well documented through receiver operating characteristic (ROC) curves as reported in several previous studies demonstrating that the predictive performance of the preovulatory P4 level to discriminate between conception and non-conception cycles is very limited [1315]. Accordingly, the implementation of prognostic prediction models instead of simple bivariate analysis is mandatory to address the issue and thus, might serve as a useful tool to estimate the relative contribution of different factors to a single outcome [11,16]. Indeed, the multivariable analysis provides the ability to remove the effect of confounders or other forms of biases and, thus, get a more realistic picture compared to looking at only one variable [1719]. So far, only a few studies performed multivariate regression analyses to explore the possible effect of EP4 on the likelihood of pregnancy [11,2023]. Interestingly, the covariates tested in the regression models of these studies were mostly related either to the patient’s characteristics or to the ovarian stimulation parameters.

To our knowledge, none of the above studies incorporated parameters related to the luteal phase particularly the endocrine profile, presumably owing to a lack of the availability of serum hormones measurements, as the hormonal assessment is not routinely performed during the luteal phase following fresh embryo transfer. Yet prior investigations have shown that the midluteal P4 level may also be considered as a promising predictor of IVF outcome, not only in fresh but also in frozen-thaw transfer cycles [24,25]. Based on this evidence, we hypothesized that by controlling for the differences in midluteal P4 levels, the effect of the preovulatory serum P4 on the probability of LB might be more accurately estimated. Thus, the primary objective of the present study was to estimate the independent effect of preovulatory P4, if any, on the probability of LB after 26 completed gestational weeks by considering the midluteal P4 level when controlling for the most common confounding factors. Moreover, the relationship between preovulatory and midluteal P4 was investigated.

Materials and methods

Study design

This is a secondary analysis of data from a cohort of 328 patients undergoing a fresh IVF/intracytoplasmic sperm injection (ICSI) cycles in which GnRH agonist (GnRH-a) was used for triggering final oocyte maturation. The details of the flow chart of participants in the study have been described previously [26]. Each patient was only included once. All cycles were performed at the IVF unit- Ibn Rochd, Constantine, Algeria, between 2014 and 2016. All patients gave written informed consent to participate in the study, which was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice. The research project was approved by the Ethics Committee of the University hospital Centre Ibn Badis, Constantine, 20 October 2013. The original study was registered in ClinTrial.gov, Number: 02053779.

Ovarian stimulation

In brief, ovarian stimulation was performed with GnRH antagonist co-treatment, using exclusively recombinant follicle stimulating hormone (r-FSH), (Gonal F., Merck Serono; Puregon., MSD) The initial dose of r- FSH was individualized for each patient according to the female’s age and ovarian reserve markers (basal FSH level antral follicular count). Stimulation was started from the day 3 of menstruation and dose adjustments were performed on day 5 or 6 of stimulation based on ovarian response. GnRH antagonist 0.25 mg was daily administered from cycle day 5 or 6 when follicles reached a mean diameter of 13 mm and continued until to the day of final oocyte maturation. The ovulation was triggered with GnRH-a when minimum of two follicles reached 17 mm or more in size and followed by oocyte pick up (OPU) 36–38 hours later. Fertilization of mature oocytes was carried out using either conventional IVF or ICSI technique based on the sperm quality. Blood collection was done for FSH, LH and Estradiol (E2) on day 1 of stimulation, for FSH, LH, E2 and P4 on the day of triggering and on day OPU+7 and for Beta-human chorionic gonadotropin (ß-hCG) on day OPU + 14. Serum was analyzed for endocrine parameters by a central laboratory.

Blood samples have been processed according to the manufacturer’s instructions. Serum was analyzed immediately using a Vidas kit (BioMerieux, France). The calibration range of the VIDAS Progesterone kit is 0.25–80 ng/mL.

Embryo grading and embryo transfer

One to three embryos per patient were replaced on day OPU+2 or 3, depending upon the age and the ovarian response. Embryo quality was assessed at the cleavage stage based on the embryo morphology. A good quality embryo (Grade 1 and 2) was defined as follows: the 2–4 cells on day 2 and 6–8 cells by day 3, <20% of fragmentation, and regular shaped cells [27]. No embryo transfer was cancelled due to EP4 on the day of GnRH-a trigger.

Luteal phase support

Luteal phase support (LPS) was previously described with more details [26]. Briefly, all patients received a bolus of HCG 1500 IU 1 hour after OPU, micronized progesterone vaginally (600 mg/day) and estradiol orally (4 mg/day) starting from the night of OPU and continuing until 12 weeks of gestation or a negative pregnancy test. Besides, patients have been randomized on the day of embryo transfer into two groups; the study group received an additional single dose of GnRH-a (Triptorlin 0.1 mg) on day OPU+6 while the control group did not.

The primary outcome of the current study was live birth rate (LBR) defined as a live neonate beyond 26 weeks of gestation. Further, the correlation between preovulatory serum P4 levels, midluteal serum P4 levels and ovarian response was investigated.

Statistics

All statistical analyses were performed using IBM SPSS Statistics 26.0 (IBM Inc., New York, USA).

The distributions of continuous parameters were evaluated using the Shapiro–Wilk test to determine whether each variable followed a normal distribution. Serum P4 levels on the day of trigger as well as midluteal P4 (OPU+7) levels were converted from continuous variables into categorical variables by apportioning them into four groups (quartiles) based on 25th, 50th and 75th percentiles. Q1 included 0–25%, Q2 included 25–50%, Q3 included 50–75% and Q4 included 75–100%. Data are presented as means and standard deviations for continuous data with normal distribution, as medians and ranges for continuous data with skewed distribution and as percentages for categorical variables.

Differences in skewed continuous data between the four preovulatory P4 groups were assessed using Kruskal–Wallis test followed by a post hoc pairwise comparison in case of a statistical difference between groups. One-way analysis of variance analysis (Anova) was used in case of normal continuous data. Difference in categorical variables between P4 groups was assessed using Pearson’s chi-square test or Fishers exact test where appropriate. Spearman’s correlation coefficient was used to assess the association between preovulatory and midluteal P4 levels as well as ovarian response elements in terms of E2 on the trigger day, number of follicles > 11 mm and number of oocytes retrieved. Patients contributed only one cycle in the dataset analysed. Logistic regression analysis was used to assess 13 parameters possibly related to LBR, including covariates demonstrating a P≤ .25 for the association with outcome in the univariable models as well as clinically relevant predictive variables which were selected based on previous studies [11,21,28]. The factors tested in the model were: (i) Patient’s characteristics: female age, female BMI and number of previous failed IVF (ii) The intensity of ovarian response: total dose of FSH consumption, duration of stimulation, number of follicles > 11 mm on the day of trigger which, optimally, equals to the number of oocytes. (iii) LH on the day of GnRH-a trigger (iv) Embryo’s characteristics: number of embryos obtained, whether at least one good embryo transferred and number of transferred embryos. Additionally, an adjustment for midluteal P4 along with LPS imbalances by including the extra dose of GnRH-a (yes versus no) as a covariate was performed. A standard, i.e., direct logistic regression was used as an analysis method to develop the final model [29]. Box and Tidwell, 1962 procedure was assessed to test the linearity of the continuous variables with respect to the logit of the dependent variable, i.e., LB [30]. The multicollinearity among all the factors was examined using the variance inflation factor (VIF). The model fit was evaluated by the Hosmer and Lemeshow test [18]. Odds ratios (ORs) and 95% confidence intervals (CIs) were assessed distinctly for each factor. All statistical analyses were two-tailed, and results were considered significant when p-values < 0.05 were obtained.

Results

Baseline patient and cycle characteristics

The present study evaluated a total of 328 IVF/ICSI cycles followed by fresh ET. The spectrum of patients was considered as large including both normal responders (NR): 80% (260/328) and high responders (HR) >18 follicles: 20% (68/328). Baseline and cycle characteristics according to quartiles of serum P4 levels on the day of trigger are provided in Table 1. The overall mean female age and female body mass index (BMI) were 31.17 ± 4.02 years and 27.23 ± 4.19 kg/m2, respectively. Patients were divided into four distinct groups according to their quartile serum P4 levels on the day of GnRH-a trigger: [Q1: <0.74, Q2: 0.75–0.98, Q3: 0.99–1.30, and Q4: > 1.30 ng/mL]. Conversion factor to SI unit, 3.180. The four groups (Q1, Q2, Q3 and Q4) were comparable as regards age, BMI, duration of ovarian stimulation, total dose of FSH and serum FSH on day OPU+7. However, there were significant differences between the groups regarding the ovarian response parameters (number of follicles >11mm and E2) as well as hormones on day OPU+7 (LH, E2 and P4).

Table 1. Baseline patient and cycle characteristics according to serum P4 quartiles on the day of GnRH agonist trigger.

Characteristic a P4 Q1 (<0.74) P4 Q2 (0.75–0.94) P4 Q3 (0.95–1.30) P4 Q4 (>1.30) P-value b Total
Number 88 80 79 81 NA 328
Age (years) 32.50 ±3.66 32.25 ± 3.84 31.68 ±3.77 31.17±4.02 .12 31.91±3.84
BMI (kg/m2) 28.69 ±5.17 28.45 ± 4.31 27.52 ±4.72 27.23±4.19 .12 27.99±4.65
Previous IVF, (n) 1 (1–3) 1 (1–3) 1 (1–3) 1 (1–4) .44 1.3 ± 0.57
Stimulation, (days) 9 (7–15) 9 (6–12) 9 (6–13) 9 (6–15) .79 9(6–15)
r-FSH (IU) 1871.30±333.63 1825.93±294.50 1809.17±288.54 1840.43±303.25 .66 1837.65±305.71
Follicles >11mm trigger, (n) 9 (4–28) 10 (5–26) 15 (4–30) 16 (4–26) .0001 13 (4–30)
E2 trigger, (pg/mL) 1277.50(37–6298) 1646.45(304–3000) 1880(426–3000) 2600(433–4300) .0001 1943.21 (304–6298)
LH trigger, IU/L 0.92 (0.10–3.30) 0.98 (0.10–4.65) 0.93 (0.10–4.57) 1.25 (0.10–6) .02 1.28 (0.10–6)
P4 (OPU+7), ng/mL 38.71 (10–127) 36.20 (7–182) 40 (14–322) 49 (11.78–192.80) .02 45.53 (7–322)
E2 (OPU+7), pg/mL 739(89–4747) 903(110–4300) 948(144–6071) 1033(182–4315) .001 867.47 (89–6071)
LH (OPU+7), IU/L 2.5(0.10–13.43) 1.93(0.10–9) 1.60(0.10–8.32) 2.47(0.10–9.34) .003 2.77 (0.10–13.43)
FSH (OPU+7), IU/L 0.74 (0.14–2) 0.67 (0.10–2.46) 0.63 (0.15–1.81) 0.68 (0.19–2.50) .41 0.77 (0.10–2.50)

a Descriptive data are presented as mean ± SD for continuous normal data and as median (range) for continuous skewed data. Groups are compared using Anova or Kruskal-Wallis tests as appropriate.

b Two-side P < .05 were considered significant.

BMI, body mass index; E2, estradiol; IVF, in vitro-fertilization; IU, international units; LH, luteinizing hormone; NA, not applicable; OPU, oocyte pick-up; P4, progesterone(ng/ml); Q, quartile; r-FSH, recombinant follicle-stimulating hormone; SD, standard deviation.

Relationship between preovulatory, midluteal P4 levels and ovarian response

Spearman’s correlation revealed that the preovulatory P4 level was significantly correlated with ovarian response elements in terms of E2 levels, number of follicles >11 mm and number of oocytes retrieved (All P<0.0001) as well as with midluteal P4 level (P<0.007) (S1 Table).

Reproductive outcomes

Reproductive outcomes are provided in Table 2. The overall positive hCG rate per transfer, ongoing pregnancy rate and LBR in the study was 44.2% (145/328), 34.5% (113/328) and 33.5% (110/328), respectively. Although the number of oocytes retrieved as well as the number of embryos obtained were significantly different between the different P4 groups on the day of ovulation trigger, however, the pregnancy outcomes were comparable (Table 2).

Table 2. Outcome of ovarian stimulation, fertilization and embryo transfer according to serum P4 quartiles on the day of GnRH agonist trigger.

Characteristica P4 Q 1 (<0.74) P4 Q2 (0.75–0.94) P4 Q 3 (0.95–1.30) P4 Q 4 (>1.30) P valueb Total
Number 88 80 79 81 NA 328
Oocytes retrieved 8.47 ± 4.88 7.78 ± 3.74 10.33 ± 5.84 11.33 ±5.84 .0001 9.41 ± 4.97
2PN oocytes 5.43 ± 3.42 5.08 ± 2.60 5.94 ± 3.43 6.46 ± 3.43 .04 5.72 ± 3.27
Embryos 5.09 ± 3.24 4.79 ± 2.62 5.59 ± 3.14 6.17 ± 3.33 .02 5.41 ± 3.13
Embryo transfer 2.31 ± 0.63 2.43 ± 0.61 2.30 ± 0.58 2.32 ± 0.49 0.50 2.34 ± 0.58
Positive hCG, n (%) 41 (46.60%) 34 (42.5%) 38 (48.10%) 32 (39.50%) 0.68 145 (44.2%)
Ongoing pregnancy, n (%) 31 (35.22%) 30 (37.5%) 28 (35.44%) 24 (29.63%) 0.74 113 (34.5%)
Live birth, n (%) 31 (35.22%) 29 (36.25%) 26 (32.91%) 24 (29.62%) .41 110 (33.5%)

aAll values are presented as mean ± (SD) or count n (%)

bKruskal-Wallis test or Chi-squared test for differences between preovulatory serum P4 groups.

E2, estradiol; IU, international units; LH, luteinizing hormone; NA, not applicable; OPU, oocyte pick-up; P4, progesterone (ng/mL); PN, pronuclei; Q, quartile; r-FSH, recombinant follicle-stimulating hormone; SD, standard deviation.

Table 3. summarizes the results of a multivariate regression analysis related to the LBR. The logistic regression model was statistically significant, X2 = 60.02, p < .0005. The model explained 23.6% (Nagelkerke R2) of the variance in LB and correctly classified 73% of cases. Sensitivity was 43.1%, specificity was 88.3%, positive predictive value was 65.3% and negative predictive value was 75.2%. The independent factors found to be significantly associated with LB were: midluteal serum P4 level, i.e., on day OPU+7, serum LH levels and final follicles >11 mm on the day of trigger, number of embryos obtained, number of transferred embryos and whether at least one good embryo transferred. However, adding the extra dose of GnRH agonist on day OPU+6 (yes versus no) to the regression model did not change estimates significantly.

Table 3. Multivariate regression analysis of independent factors related to the live birth.

Variable Regression coefficient Standard error OR 95% CI P-value
P4 day of trigger, ng/mL
    Q1 < 0.74 [reference category] 1
    Q2 (0.75–0.94) .004 .366 1.00 0.49–2.06 0.99
    Q3 0.95–1.30) -.038 .372 0.96 0.46–2.00 0.92
    Q4 >1.30 -.582 .392 0.56 0.26–1.20 0.14
LH day of trigger, IU/L
    Q1 < 0.68 [reference category] 1
    Q2 (069–0.98) .178 .379 1.19 0.57–2.51 0.64
    Q3 (0.99–1.60) .206 .375 1.23 0.59–2.56 0.58
    Q4 >1.60 .856 .379 2.35 1.12–4.94 0.02a
Follicles> 11 mm day of trigger, (n)
    (1–6) [reference category] 1
    (7–18) -.434 .391 0.65 0.30–1.40 0.29
    (>18) -2.263 .586 0.10 0.03–0.33 0.000a
Embryos, (n) .142 .054 1.15 1.04–1.28 0.008a
Embryos transferred, (n) .621 .294 1.86 1.04–3.31 0.03a
P4 (OPU+7), ng/mL
    Q1 <28 [reference category] 1
    Q2 (29–40) -.397 .379 0.67 0.32–1.41 0.29
    Q3 (41–60) 1.007 .382 2.73 1.29–5.78 0.008a
    Q4 >60 .406 .453 1.50 0.62–3.64 0.37

For estimates, adjustment was made for female age; female BMI; number of previous failed IVF; duration of stimulation; total dose of FSH during stimulation; number of follicle > 11mm on the day of trigger; hormones on the day of trigger (P4 and LH); number of embryos obtained; number of embryos transferred; whether at least one good embryo transferred; additional dose of GnRH agonist on day OPU+6 (yes vs. no) and P4 on day OPU+7.

Note

Preovulatory serum P4 was compared between the first quartile (<0.74 ng/mL; reference category). and the rest of quartiles (2–4). Follicles> 11 mm were compared between low ovarian response (< 6 follicles; reference category), and intermediate response (6–18) and high ovarian response (>18). Preovulatory serum LH was compared between the first quartile (<0.68 IU/L; reference category), and the rest of quartiles (2–4). Midluteal serum P4 (OPU+7) was compared between the first quartile (<28 ng/mL; reference category) and the rest of quartiles (2–4).

Fig 1 depicts the OR, 95% CI for LBR according to serum P4 quartiles on the day of ovulation trigger versus serum P4 quartiles on day OPU+7. Each group of P4 was compared with the lowest quartile (Q1) [Fig 1A and 1B], respectively. After adjustment for relevant confounders, serum P4 on the day of trigger did not have a significant impact on LBR [Fig 1A]. In contrast, the LBR increased significantly in patients with a midluteal serum P4 level of 41–60 ng/mL compared to the lowest quartile Q1 (P4 < 28 ng/mL = a reference category); [OR: 2.73 (1.29–5.78); p< 0.008] [Fig 1B].

Fig 1.

Fig 1

Forest plot of adjusted live birth rates according to preovulatory (A) versus midluteal serum P4 levels (B) after IVF/ICSI with fresh ET. Data are presented in OR (95% CI) of the comparison of the odds between each P4 quartile with the lowest P4 quartile (Q1: reference category). For estimates of live birth, adjustment was made for female age; female BMI; number of previous failed IVF; duration of stimulation; total dose of FSH during stimulation; number of follicle > 11mm on the day of trigger; hormones on the day of trigger (P4 and LH); number of embryos obtained; number of transferred embryos; whether at least one good embryo transferred; additional dose of GnRH agonist on day OPU+6 (yes vs. no) and P4 on day OPU+7.

Discussion

Using a multivariate regression analysis, the present study suggests that the preovulatory P4 level did not show a significant effect on reproductive outcomes. In contrast, the midluteal P4 level significantly impacted the LBR in a nonlinear pattern suggesting that both low and high P4 levels during this period, may reduce the chance of LB in women undergoing IVF treatment followed by fresh ET. Indeed, an optimal midluteal P4 range (41–60) ng/mL was identified. The same pattern was seen for both crude and adjusted OR of preovulatory and midluteal P4 levels. Furthermore, a positive correlation was found between the magnitude of the ovarian response and P4 levels on the day of ovulation induction as well as on the day OPU +7. Regarding the preovulatory P4, our data failed to reproduce the findings of previous studies, particularly those which implemented a multivariate regression analysis and showing negative effect of EP4 on reproductive outcomes [11,2026]. The discrepancy between these studies and our findings may be attributed to the following reasons; (i) Aside from the hypothesis of being a true null result, an underpowered genuine effect of preovulatory P4 on LBR may be considered in the framework of the present study design limitations since the sample size is relatively modest compared to the previous reports which may constitute a type II error [31]. (ii) Another possibility that deserves to be examined in the interpretation of the present results is that we included the midluteal P4 as a novel covariate to adjust for along with the patient characteristics and stimulation parameters; this may generate different estimates in the regression model. (iii) The current published data on EP4 and IVF outcomes predominantly derive from hCG triggered cycles [8] whereas in our study, all patients were triggered with GnRH-a. In this respect, the P4 concentration during the early luteal phase was found to be significantly higher in hCG trigger compared to GnRH-a trigger [32,33] which may yield substantial discrepancy in the endocrine profile [34].

In contrast with earlier studies suggesting that the impact of EP4 on pregnancy outcome does not seem to be modulated by the ovarian response [20,21], it has been recently shown that the number of oocytes may be an important confounder associated with both the exposure (EP4) and the outcome (LB) [11]. In this respect, accumulating evidence revealed that EP4 does not seem to significantly affect reproductive outcomes in the high response (HR) category in which EP4 is being more common than poor and intermediate response categories. [11,14,23]. In the current study, the final follicle count >11mm was found in the regression analysis to significantly reduce LBR in HR (> 18 follicles) when compared with poor and intermediate responders [OR: 0.10, 95% CI, 0.03–0.33] (Table 3), which is consistent with the findings reported by a recent study showing a steady reduction in LBR beyond twenty oocytes retrieved, presumably due to supraphysiological circulating steroids levels [35]. Besides, the present data show a significant correlation between the ovarian response and both preovulatory and midluteal P4 levels (S1 Table) suggesting that each follicle contributes to the pool of serum P4 before ovulation triggering [20,36] as well as after oocyte retrieval [24,37]. Contrasting the preovulatory serum P4 level, the current results suggest that the midluteal P4 level is an independent factor associated with LB potential, and, interestingly, in a nonlinear pattern, [OR: 2.73 (1.29–5.78); p< 0.008] (Table 3). While sufficient evidence has accrued, demonstrating that a low luteal P4 level is associated with low pregnancy rates in fresh ET despite transfer of morphologically good embryos [38], the impact of EP4 during the midluteal period on the cycle outcome has not been fully elucidated. A recent study analyzing a dataset of 602 patients reported that the optimal chance of pregnancy was achieved with midluteal serum P4 of 150–250 nmol/l, i.e., 47–78 ng/mL which is close to the optimal range found in our study, 41–60 ng/mL [24].

Currently, it is not yet clarified whether the late follicular P4 rise is a cause or a confounder of lower reproductive outcome in fresh ET since the conclusions drawn from bivariate analysis may be prone to bias [11]. Further, in the majority of previous studies where the midluteal P4 was not considered in the multivariate regression analysis, it is believed that the preovulatory EP4 per se reduces pregnancy outcome by altering the endometrial receptivity [39,40] rather than oocyte/embryos quality [41].

Conversely, the current study argues against the above-mentioned findings, showing that the detrimental effect of EP4 seems to be attributed to the EP4 during midluteal time rather than to EP4 at the day of ovulation induction. As depicted in Fig 1A, preovulatory P4 levels did not affect LBR. Importantly, the nonlinear model of the correlation between midluteal P4 levels and LBR suggests that there is an optimal range of midluteal P4, a window at which the most optimal implantation rates can be expected since pregnancy rates are lower with both low and high P4 levels Fig 1B, which is in agreement with a previous report [42]. Indeed, the present study suggests that a suboptimal midluteal P4 level seems to decrease the chance of LB following fresh ET from 45.10% in patients with optimal midluteal P4 levels (41–60) ng/mL to 30.12%, 25.27% and 30.77% in patients with P4 < 28 ng/mL, P4 (29–40) ng/mL and P4 >60 ng/mL respectively (S2 Table). Accordingly, we speculate that the likely reason for the negligible effect of preovulatory EP4 on the cycle outcome of HR may be explained by the availability of adequate P4 around the time of implantation rather than the availability of high-quality embryos including blastocysts for transfer, as previously suggested [43,44]. Further, one might wonder whether reports failing to show any relation between late follicular P4 levels and the reproductive outcome may have analyzed more HR who had an appropriate midluteal endocrine profile. Providing further support for our observations, Wang et al. retrospectively compared the effect of preovulatory versus early luteal P4 levels on reproductive outcomes in 384 patients [34]. The study showed that low responders undergoing intensive ovarian stimulation are more likely to exhibit low reproductive outcomes in fresh ET compared to normal and HR which was attributed to an elevated P4 ratio (the rise in P4 between trigger day and oocyte retrieval) rather than to preovulatory P4 level per se.The strength of the present findings derives from the fact that they are provided by multivariable regression analysis instead of bivariate analysis which may remove the effect of confounders and, thus, more reliably may estimate the true effect of preovulatory P4 on the LBR. Nonetheless, the study also has some limitations, including the fact that the observed data derive from a post hoc analysis which may prevent statistical detection of further significant differences particularly when the study is underpowered and not truly negative; moreover, data collection ended in 2016; at that time, the use of blastocyst transfer was not systematically implemented in our center. Although new confounders such as those related to the luteal phase have been added to the regression model, there could still be residual variables which are not considered. Furthermore, the findings of the current study may not be applicable to a population triggered with hCG. Lastly, the validity of a single measurement instead of a median value [45], and the performance and precision of immunoassay systems, particularly in the lower range of detectable P4levels (<2.5 ng/mL) [46]. From a clinical perspective, this study highlights that monitoring P4 levels during the midluteal phase would be an important and innovative practice which might increase the reproductive outcomes of fresh ET; thus, in patients with P4 levels below 40 ng/mL, additional progesterone could still be provided to rescue the cycle [47]; alternatively in cycles with P4 levels above 60 ng/mL, additional progesterone support is redundant and likely to be harmful for the endometrial receptivity. To conclude, the findings of the present study suggest that the midluteal P4 level seems to impact LBR more than the preovulatory P4 level. The most optimal midluteal P4 level identified was 41–60 ng/mL, and both preovulatory and midluteal phase serum P4 are positively correlated with the ovarian response. More research into the luteal phase steroids profile of the IVF cycle is needed before final conclusions can be drawn.

Supporting information

S1 Table. Spearman’s correlation between preovulatory, midluteal serum P4 concentration and ovarian response.

N, 328 patients undergoing IVF/ICSI treatment. Rho, Spearman’s correlation coefficient. P < .05: Statistically significant.

(DOC)

S2 Table. Relationship between Live birth rates and midluteal (OPU+7) serum P4 quartiles.

*Chi-squared test for differences between mid-luteal serum P4 groups. P < .05: Statistically significant. NA, not applicable; OPU, Ovum pick-up; P4, progesterone (ng/mL).

(DOC)

S1 Dataset

(DOC)

S2 Dataset

(XLSX)

Acknowledgments

The authors thank Abdelmadjid Barkat and Abdelhamid Aberkane as a member and a president of a Medical Research Ethics Committee (University Hospital Benbadis, Constantine) respectively, for their valuable collaboration.

Data Availability

Dataset supporting the findings of the current study is available in the Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

References

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Decision Letter 0

Stephen L Atkin

16 Nov 2020

PONE-D-20-32335

The preovulatory versus the midluteal serum progesterone level: Which is better for live birth prediction during fresh embryo transfer?

PLOS ONE

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Reviewer #1: The present study aims to evaluate the impact of preovulatory and midluteal progesterone levels on live birth rates in the course of IVF. In this attempt 328 IVF cycles were evaluated. The authors concluded that midluteal progesterone levels were associated with LBR. Indeed, progesterone ranges between 41 and 60 ng/mL were found to be related with higher pregnancy potential and live birth chances.

The research question is of interest and falls within the scope of the journal. The study is well-designed and the manuscript is written concisely and in standard English.

However, there are some issues that deserve consideration:

In contrast to previous publications preovulatory progesterone was not associated with pregnancy potential. Please provide a power analysis to ensure that the sample size was sufficient to exclude a type II error.

The number of embryos transferred was shown to influence results. Please report whether midluteal progesterone levels were associated with implantation rates (i.e. singletons versus multiples) and may, thus, have contributed to progesterone levels.

Though the reviewer highly cherishes the scientific level of the discussion, the number of references could be tightened.

Reviewer #2: 1) The term “nonparametric variables” is awkward. The term “nonparametric” can be used to describe modeling methods, but not variables.

2) It is unclear what “A sequential logistic regression was used as an analysis method to develop the final model” means. Please elaborate. Does “sequential” means “stepwise” https://en.wikipedia.org/wiki/Stepwise_regression ?

3) Please use “multiple logistic regression model” to correctly describe the regression models used.

4) From Table 3, it is still unclear to me what models were fitted and what is the final model. Please explicitly clarify what variables / models were considered, how the variable selection was performed, what is the final model.

5) All the sentences on prediction performance, e.g, ROC, should be removed, because there is only one dataset involved in the current study. One cannot evaluate the prediction performance of a model on the same dataset based on which the model is derived.

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Reviewer #2: No

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PLoS One. 2021 Feb 11;16(2):e0246440. doi: 10.1371/journal.pone.0246440.r002

Author response to Decision Letter 0


22 Dec 2020

Dear Editor,

We appreciate the opportunity to revise and resubmit our paper titled “The preovulatory versus the midluteal serum progesterone level: Which is better for live birth prediction during fresh embryo transfer? “

We want to thank the editor and reviewers for their constructive comments and criticism helping us improving the article. We provided a point-by-point response to each of the editor’s and reviewers’ comments. We have included the page and line numbers in the revised manuscript to help the reviewers identify our changes.

Reviewer’s comments are written in italic; authors’ responses are shown in upright font.

We believe that we had addressed all the questions and concerns raised by the editor as well as reviewers.

Should you have any further requests or questions, please do not hesitate to contact me.

Abdelhamid Benmachiche

Corresponding Author

RESPONSE TO COMMENTS FROM THE EDITOR AND REVIEWERS

I. Editor

According to your comments and suggestions, we have provided changes as follows:

� Manuscript format

We have made the requested formatting adjustments to comply with the PLOS ONE manuscript checklist.

Regarding the figure extension, we have uploaded our figure file (Fig1) to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com and get the label with the extension TIF (Fig1.tif)

� Text similarity

We went over the parts of our manuscript that show similarity with previous literature, and rephrased the content to the extent possible accompanied by an appropriate citation.

Seen from the same perspective, that is, to avoid redundancy and copyright issues, we have also removed the supplementary figure 1 (S1_Fig) which describes the flow chart of patients taken from our own original study (1) and we subsequently referred the reader to the source.

� Please see page 4 line (70, 71) in the revised paper

(1): Reference number [26] in the revised manuscript

� Title: The dos and don’ts about how to write a great title from PLOS ONE

suggest that the title – in the most cases- shouldn’t need to be framed as a question

(https://plos.org/resource/how-to-write-a-great-title/)

Accordingly, the title of our manuscript has been modified

Original:

“The preovulatory versus the midluteal serum progesterone level: Which is better for

live birth prediction during fresh embryo transfer? ”

Revised:

“The Impact Of Preovulatory Versus Midluteal Serum Progesterone Level On Live

Birth Rates During Fresh Embryo Transfer”

� Please see page 1 in the revised paper

� Ethics statement

Written informed consent with more details has been added in the Methods and online

submission information

� Please see page 4, lines (72, 74) in the revised paper

� Data Availability Statement:

The authors confirm that the data supporting the findings of this study are available as

a part of manuscript supporting information without restriction.

Reviewer 1

Question #1

In contrast to previous publications preovulatory progesterone was not associated with pregnancy potential. Please provide a power analysis to ensure that the sample size was sufficient to exclude a type II error.

Response #1

In the present study, we found that preovulatory P4 does not impact the LBR which is in line with many previous publications [5, 6, 7, 13]. However, in the discussion section, we thought necessary to focus rather on the discrepancy between our findings and those studies reporting a negative effect of elevated preovulatory P4 on pregnancy outcome [20-22].

Regarding the post-hoc power, we would say that:

We appreciate the reviewer’s insightful suggestion and agree that it would be useful to provide the study’s power in order to discriminate between a study which is truly negative or simply underpowered, however, we were not able to compute a post-hoc power for explaining the observed data, but we could only estimate it because its retrospective nature.

While the utility of prospective power analysis in experimental design is universally accepted, the usefulness of retrospective techniques is controversial (1).

This was also nicely explained by Hoenig and Heisey ( 2001) indicating that there is a large current literature that advocates the inappropriate use of post-experiment power calculations as a guide to interpreting tests with statistically nonsignificant results. The authors show that, power is mathematically directly related to the p-value; hence, calculating power once you know the p-value associated with a statistic is of little help in interpreting results (2). So, we will always have low observed power when we report non-significant effects.

In fact, the data of the current study were handled using a sample size which has been already calculated in the original study with a power set before starting the enrollment of patients (3). However, in this post-hoc data analysis, i.e., using the same sample size, we found that there was no significant result regarding preovulatory P4 levels on the live birth rate, then - by definition - its power to detect the effect actually observed is low for this parameter (Preovulatory P4).

So, without a priori power and sample size calculations, one can never be sure if the results obtained were due to a ‘‘true’’ effect (2)

For this reason, we did not claim that our results are truly negative as that was clearly acknowledged in the discussion section (type II error), because it is possible that the true effect size, if any, is even smaller.

� Please see page 13, lines (263-266) in the revised paper

On the other hand, the impact of differences in the midluteal P4 levels with the same sample size show a significant effect on LBR, thus, one may conclude that the study was appropriately powered for this variable, i.e., midluteal P4.

For all these reasons and for the complex calculation of several sample sizes related to every variable examined, we considered unwise to perform a post hoc analysis.

Furthermore, we are confident that a statistically significant p value still can offer a reliable and useful information even if the power analysis has not been performed.

Nevertheless, we recognize that your comment should be mentioned in the paper as a limitation, so we added the following sentence “Nonetheless, the study also has some limitations, including the fact that the observed data derive from a post hoc analysis which may prevent statistical detection of further significant differences particularly when the study is underpowered and not truly negative”

� Please see page 15, lines (322-324) in the revised paper.

References

(1) Thomas, L. (1997) Retrospective power analysis. Conservation Biology 11(1):276–280

(2) Hoenig and Heisey (2001). The Abuse of Power. The American Statistician 55(1):19-24

(3) Benmachiche A, Benbouhedja S, Zoghmar A, Boularak A, Humaidan P. Impact of mid-luteal phase GnRH agonist administration on reproductive outcomes in GnRH agonist-trigger: a randomized controlled trial. Front Endocrinol. (2017) 8:12 doi: 10.3389/fendo.2017.00124.

Question #2

The number of embryos transferred was shown to influence results. Please report whether midluteal progesterone levels were associated with implantation rates (i.e. singletons versus multiples) and may, thus, have contributed to progesterone levels.

Response #2

Thanks for pointing out this comment.

The ‘implantation rate’ (IR) is calculated as [IR = ngestational sacs/ntransferred embryos ]. This calculation can be performed per patient or aggregated per group of patients.

In the current study the IR according to midluteal P4 was calculated per quartiles groups and the results are provided below:

Implantation Rates According To Midluteal P4 Groups

Q1 P4 (OPU+7) Q2 P4

(OPU+7) Q3 P4

(OPU+7) Q4 P4 (OPU+7) P-value

Nb Gestational Sacs 44 48 72 28

Nb Embryo transferred 199 213 234 121

Implantation rate n, (%) 44/199 (22.11%) 48/213

(22.53%) 72/234

(30.77%) 28/121

(23.14) NS

Besides, the Pearson´ correlation showed a significant positive correlation between the number of embryos transferred (ET) and the number of gestational sacs (GS) (P<.01).

Although the mean number of ET (mean +/- SD: 2.3 +/- .58, P= .60) was similar between the four groups, however, a bivariate analysis found that the IR was higher in the third quartile (Q3: 72/234 (30.77%)) compared with the rest of quartiles, Q1: 44/199 (22.11%), Q2: 48/213 (22.53%) and Q4: 28/121 (23.14%), but the difference did not reach the significant level. Consequently, the midluteal P4 (41-60 ng/ml) threshold appears to enhance the implantation rate.

Thereafter, in terms of LB, we found the same trend of results importantly with significant level always in favor of the third quartile of midluteal P4 in both bivariate and multivariate regression analysis (S1_Table), (Table 3) respectively, presumably due to the quality of implanted embryos.

Question #3

Though the reviewer highly cherishes the scientific level of the discussion, the number of references could be tightened.

Response #3

We agree with your suggestion that the number of references in the discussion section could be tightened.

Accordingly, we have removed 12 references

Original:

Number of references: 59

Revised:

Number of references: 47

� Please see page 16, line 362

The list of the removed references includes:

References numbers: 8, 23, 26, 28, 29, 31, 37, 44 ,46, 48, 55 and 58 from the original version.

We deeply value the input of reviewer 1

Reviewer 2

Question #1

The term “nonparametric variables” is awkward. The term “nonparametric” can be used to describe modeling methods, but not variables.

Response #1

We apologise for our lack of clarity. Accounting for the given suggestion, the term “nonparametric variables” has been changed to ” skewed continuous data “

In the revised manuscript.

Revisions in text are made for more clarity including specific information:

“Data are presented as means and standard deviations for continuous data with normal distribution, as medians and ranges for continuous data with skewed distribution and as percentages for categorical variables. Differences in skewed continuous data between the four preovulatory P4 groups were assessed using Kruskal–Wallis test followed by a post hoc pairwise comparison in case of a statistical difference between groups. One-way analysis of variance analysis was used in case of normal continuous data”.

� Please see page 6, lines (117-122) in the revised manuscript

We hope that it is now clearer.

Question #2

It is unclear what “A sequential logistic regression was used as an analysis method to develop the final model” means. Please elaborate. Does “sequential” means “stepwise” https://en.wikipedia.org/wiki/Stepwise_regression ?

Response #2

We would like to thank the reviewer 2 who helped find the error and for giving us

this opportunity to clarify this important point.

Actually, the sentence “A sequential method” is wrong and was inadvertently typed by the corresponding author while the statistician team did not detect the error when checking the final draft, therefore it should be changed to “A standard/ direct method”.

We have now made the following revisions:

Original:

“A sequential logistic regression was used as an analysis method to develop the final model.

Revised:

“A standard, i.e., a direct method was used in logistic regression to develop the final model”.

� The changes can be now seen in page 6, lines (138,139) of the revised paper.

Definitely we agree with the reviewer 2 and now we understand why the reviewer 2 was concerned by a discordance between the sentence “A sequential method” erroneously typed and the whole description of the model output as it was clearly stated in the questions #2 and #3.

Accordingly, the results provided in (Table 3) remain unchanged in the revised manuscript.

We apologize for our error and for this inconvenience.

Questions #3

Please use “multiple logistic regression model” to correctly describe the regression models used.

Response #3

You are absolutely right, the question #3 follows the question #2;

for the reason we mentioned above, when logistic regression uses “sequential method” in SPSS software, the output should logically exhibiting “multiple logistic regression models”, however, when the method used is a “standard/direct” which is labeled “Enter” in SPSS as we did to analyze our data we get in the final analysis only one model from which we can extract and display the factors showing a significant effect on the dependent variable.

Questions #4

From Table 3, it is still unclear to me what models were fitted and what is the final model. Please explicitly clarify what variables / models were considered, how the variable selection was performed, what is the final model.

Response #4

Similarly, the question #4 also follows question #2; since we performed a direct (i.e., full, standard, or simultaneous) logistic regression which is a default of sorts, since it enters all independent variables into the model at the same time and makes no assumptions about the order or relative worth of those variables, we will have only one final model.

� Please see page (10,11), lines (208-229) of the revised paper

Questions #5

All the sentences on prediction performance, e.g, ROC, should be removed, because there is only one dataset involved in the current study. One cannot evaluate the prediction performance of a model on the same dataset based on which the model is derived.

Response #5

As per the request from the reviewer 2, all sentences on prediction performance have been removed from the revised manuscript.

We are grateful to the reviewer 2 again for the help and the constructive criticism.

Attachment

Submitted filename: Response to Reviewers.doc

Decision Letter 1

Stephen L Atkin

8 Jan 2021

PONE-D-20-32335R1

The Impact Of Preovulatory Versus Midluteal Serum Progesterone Level On Live Birth Rates During Fresh Embryo Transfer

PLOS ONE

Dear Dr. Benmachiche,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

please address the reviewers comment and resubmit

==============================

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Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: (No Response)

**********

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

**********

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Reviewer #2: Yes

**********

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Reviewer #2: No

**********

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Reviewer #2: 1) Please remove some remaining sentences on prediction analysis—lines 124-125 in Statisticis section; the second sentence in the Discussion section.

**********

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PLoS One. 2021 Feb 11;16(2):e0246440. doi: 10.1371/journal.pone.0246440.r004

Author response to Decision Letter 1


15 Jan 2021

Dear Editor

We appreciate the effort and the time that you and the reviewers have dedicated to providing your valuable feedback on our manuscript.

We believe that we had addressed all the questions and concerns raised by the editor as well as the reviewer #2.

We have included the page and line numbers in the revised manuscript to help the reviewer identify our changes.

We look forward to hearing from you regarding our revision submission and to respond to any further questions and comments you may have.

Abdelhamid Benmachiche (AB)

Corresponding Author

RESPONSE TO COMMENTS FROM THE EDITOR AND REVIEWER #2

I. Editor

Data availability

According to The PLOS Data Policy, the authors are required to make all data underlying the findings described in their manuscript fully available without restriction. Three main options are being recommended; the data should be provided as part of the manuscript or its supporting information, or deposited to a public repository.

With your permission and for clarification, we take this opportunity to emphasize that the authors confirm that the relevant data supporting the findings of this manuscript are fully available in its supporting information files without any restriction;

S3 Dataset (XLSX)_File 1 provides data within a spreadsheet (Excel format).

S4 Dataset (DOCX)_File 2 (Word format) gives additional details such as the variables names, the variables meanings, the measurement units and the missing data which might be helpful for the analysis of data included in S3 Dataset (XLSX)_File 1.

� Please see Supporting information files, page 16 lines 349 and 350 of the revised manuscript.

Thus, we believe that we have totally complied with the PLOS Data Policy

II. Reviewer #2

Question #1

Please remove some remaining sentences on prediction analysis—lines 124-125 in Statisticis section; the second sentence in the Discussion section.

Response #1

We completely agree with this and have, accordingly, incorporated the requested changes, that is, all sentences on prediction analysis have been removed from the revised manuscript.

.

� Please see Statistic section, page 6 line 126 and Discussion section, page 12 lines 249-251 of the revised manuscript.

We deeply appreciate the reviewer’s input and insightful comments

Attachment

Submitted filename: Response to reviewers.doc

Decision Letter 2

Stephen L Atkin

20 Jan 2021

The Impact Of Preovulatory Versus Midluteal Serum Progesterone Level On Live Birth Rates During Fresh Embryo Transfer

PONE-D-20-32335R2

Dear Dr. Benmachiche,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Stephen L Atkin, MD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Stephen L Atkin

25 Jan 2021

PONE-D-20-32335R2

The Impact Of Preovulatory Versus Midluteal Serum Progesterone Level On Live Birth Rates During Fresh Embryo Transfer

Dear Dr. Benmachiche:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

Dr. Stephen L Atkin

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Spearman’s correlation between preovulatory, midluteal serum P4 concentration and ovarian response.

    N, 328 patients undergoing IVF/ICSI treatment. Rho, Spearman’s correlation coefficient. P < .05: Statistically significant.

    (DOC)

    S2 Table. Relationship between Live birth rates and midluteal (OPU+7) serum P4 quartiles.

    *Chi-squared test for differences between mid-luteal serum P4 groups. P < .05: Statistically significant. NA, not applicable; OPU, Ovum pick-up; P4, progesterone (ng/mL).

    (DOC)

    S1 Dataset

    (DOC)

    S2 Dataset

    (XLSX)

    Attachment

    Submitted filename: Response to Reviewers.doc

    Attachment

    Submitted filename: Response to reviewers.doc

    Data Availability Statement

    Dataset supporting the findings of the current study is available in the Supporting Information files.


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