Abstract
Purpose
To evaluate if preimplantation genetic testing (PGT) improves the odds of a healthy live birth amongst recipients of fresh donor oocytes.
Methods
We performed a retrospective cohort study including in vitro fertilization cycles of women using fresh donor oocytes reported to the Society for Assisted Reproductive Technology Clinic Outcome Reporting System, between 2013 and 2015. Cycles were categorized based on PGT. Primary outcome measure was a good birth outcome (GBO), defined as a singleton, term, live birth with an average birthweight. Multivariable generalized estimating equation models were fit to analyze the effect of PGT. Interaction effect between cycle type (fresh vs frozen) and PGT was tested.
Results
Of 28,153 included cycles, 3708 had PGT while 24,445 did not. PGT cycles were less likely to result in an embryo transfer (ET) (64 vs 94%), but were associated with increased rates of frozen ET (70 vs 41%), single ET (67 vs 44%), and blastocyst ET (87 vs 65%). There was a significant interaction between PGT and cycle type. Cycles using PGT increased the probability of a GBO 12% in frozen cycles (RR 1.12; 95% CI 1.02, 1.22; p = 0.018), but PGT was detrimental to success in fresh cycles with a 53% reduced likelihood of GBO (RR 0.47; 9% CI 0.41, 0.54; p < 0.001).
Conclusion
PGT, as practiced during the most recently available national data in women using fresh donor oocytes, was associated with increased probability of a healthy live birth amongst frozen cycles, but was not beneficial in fresh cycles.
Electronic supplementary material
The online version of this article (10.1007/s10815-020-01874-8) contains supplementary material, which is available to authorized users.
Keywords: Preimplantation genetic testing, Embryo transfer, Donor oocyte, In vitro fertilization, Good birth outcome
Introduction
Preimplantation genetic testing (PGT) on human embryos has been performed for over a decade, and despite improvements in comprehensive chromosomal sequencing (CCS), its routine use in IVF is not recommended [1]. Selecting euploid embryos for transfer with the aid of preimplantation genetic testing (PGT) for aneuploidy (PGT-A) promotes single embryo transfer (ET) and is thought to improve implantation rates in patients who are of advanced maternal age (AMA) [2, 3]. In patients who are not AMA, there is no clear benefit of PGT-A on in vitro fertilization (IVF) outcomes [4]. In donor oocyte IVF cycles, donors are typically in their 20s and early 30s, and thus, the prevalence of aneuploidy is expected to be lower than for infertility patients undergoing autologous oocytes [5]. To date, available national evidence suggests that PGT is associated with lower odds of live birth amongst donor oocyte cycles in the USA through 2013 [6]. Outcomes reflective of more current practice, including trophectoderm biopsy, PGT platforms, and vitrification, are lacking. We sought to evaluate the effect of PGT on assisted reproductive technology (ART) outcomes for fresh donor oocyte recipients in a more contemporary setting.
Materials and methods
Database query and assortment
We utilized the Society for Assisted Reproductive Technology-Clinical Outcome Reporting System (SART-CORS), a national IVF database maintained by SART. As of 2014, over 80% of IVF clinics and over 90% of all reported IVF cycles in the USA were SART members [7, 8]. Data were retrieved and confirmed by SART and reported to the Centers for Disease Control and Prevention in compliance with the Fertility Clinic Success Rate and Certification Act of 1992 (Public Law 102-493) [9, 10]. The data in the SART-CORS are validated annually with some clinics having on-site visits for chart review based on an algorithm for clinic selection. During each visit, data reported by the clinic were compared with information recorded in patients’ charts. Ten out of 11 data fields selected for validation were found to have discrepancy rates of ≤ 5% [11]. For the purpose of research, deidentified data can be analyzed in compliance with the research standards of SART. This study was approved by the Colorado Multiple Institutional Review Board at the University of Colorado.
As it is customarily done for datasets retrieved from SART-CORS [8], variable cleaning was performed for extreme outliers and impracticable values, possibly reflecting data entry errors. For patient demographics, variables were included only if patient age was ≤ 55 years. Body mass index (BMI) was set as missing outside the range of 16–60 [12], and if either gravidity or parity was > 10, the value was changed to missing. To calculate Z-scores, only live births between 22 and 43 weeks were included, and all gestational ages > 43 weeks were considered improbable and changed to missing [13]. If live births had an improbable gestational age, but a reasonable birth weight (500–6500 g), then gestational age was considered missing [14]. Birth weights outside the range of 500–6500 g were considered missing. Missing and available data can be viewed in Table 5 in the Supplementary Appendix.
Cohort selection
To assess the effects of PGT, we obtained all donor oocyte IVF cycles reported to SART that resulted in a retrieval between January 1, 2013, and December 31, 2015. All eligible cycles for a patient were included. Thawed oocytes and the reported use of donor embryo or gestational carriers were excluded. Cycles were also excluded if both fresh and frozen embryos were transferred simultaneously, if PGT status was unknown, if a repeat fresh cycle was performed, or if the ET was canceled. The subset of cycles that resulted in an ET was analyzed in separate models. PGT cycles were defined as YES based on an “all or some embryos” response for the SART PGT variable, and NO based on a “no embryo” response for the PGT variable. All forms of PGT were included. Derivation of the final analytic cohort is outlined in Table 6 of the Supplementary Appendix.
SART has a unique cycle ID for oocyte retrieval cycles, thus allowing us to sequentially link all transfers, fresh, and/or frozen, from each cycle. Information from the index fresh cycle was linked with subsequent thawed cycles. This was done for all included cycles in the current database in which the index cycle occurred after 2013 (fertilization type for cycles in which the index retrieval occurred prior to 2013 was unavailable). We included each ET per recipient, and every ET was numbered sequentially. For example, if a recipient had a fresh ET with the index cycle, and later returned for a frozen ET linked to the same index cycle, we numbered the fresh ET “cycle # one” and the subsequent frozen ET “cycle # two.”
Outcome measures
The primary outcome was a good obstetric outcome (GBO), which we defined as a singleton birth at 37 weeks gestational age or greater with an appropriate for gestational age birth weight, defined as 2500 to 3999 g [15–17]. Secondary outcomes included live birth, ongoing implantation, clinical pregnancy, spontaneous abortion, biochemical pregnancy, preterm birth, multiple births, and gestational age-adjusted birthweight. Gestational age was calculated based on the day of ET. Ongoing implantation rate was calculated using the number of heartbeats divided by the number of embryos transferred per cycle [18]. Gestational age-adjusted and sex-specific birth weight Z-scores were calculated using a US-based birth weight algorithm [13]. Z-scores then determined whether an infant was large for gestational age (LGA) (> 90%ile), appropriate for gestational age (AGA, 10–90%ile) or small for gestational age (SGA) (< 10%ile).
Statistical analysis
Statistical analysis was performed using R (Version 3.4, Vienna, Austria). Outcomes were modeled with the generalized estimating equation (GEE) approach to account for relationships produced by repeated measures within the same observation. A compound symmetry working correlation structure was assumed for all analyses [19]. Outcomes were fitted with a log binomial link function. Risk ratios were estimated for binary outcomes and an identity link function for continuous outcomes. Each model was fit with PGT vs no PGT as the primary explanatory variable first, then all covariates were added to the model for adjusted models. The following a priori covariates were included in the adjusted model: donor age, recipient age, BMI, smoking status, parity, history of preterm birth, history of spontaneous birth, assisted hatching, single ET, cycle type (fresh vs frozen), and blastocyst ET. Interaction effect between cycle type (fresh vs frozen) and PGT was also tested. Only cycles with complete covariate data were included in the analysis.
We performed several sensitivity analyses. To minimize the impact of multiple gestation on birthweight, we analyzed Z-scores of only singleton births. To attempt to control for bias of embryo quality selection in the first ET, we performed a sensitivity analysis using only the first ET cycle per patient. We also assessed the impact of multiple embryos by excluding any cycles that transferred 2 or greater embryos (Table 7 of the Supplementary appendix). To reflect most modern IVF practice, we analyzed only single, blastocyst ET cycles.
Results
Characteristics of the study cohort
During the study period of 2013–2015, a total of 41,195 cycles originating from fresh donor oocytes were performed. The final analytic sample was comprised of 28,214 (68.5%) cycles that had complete data for all covariates of interest totaling 3712 (13.2%) PGT cycles and 24,502 (86.8%) no PGT cycles. As compared with no PGT transfers, cycles that performed PGT were less likely to achieve ET, more likely to have single ET, ICSI, assisted hatching, blastocyst transfer, and frozen ET (Table 1). DOR was the most common SART diagnosis for donor oocyte IVF in both groups. PGT cycles were performed most often in the Western reporting region of the USA, whereas the region with the most total ET was the South. The number of PGT cycles increased with each passing year of the study. In 2013, all of the included PGT transfers were fresh (n = 200), whereas in 2014 only 29.1% (n = 256) and in 2015, only 20.4% (n = 263) of PGT transfers were fresh. Patients underwent 1–2 cycles on average in both groups.
Table 1.
Cycle demographics of the analyzed cohort
| Demographic data | PGT (N = 3712) | No PGT (N = 24,502) |
|---|---|---|
| Patient age (mean (SD)) | 41.7 (5.5) | 41.3 (5.2) |
| Donor age (mean (SD)) | 26 (3.7) | 26.4 (3.6) |
| BMI (mean (SD)) | 24.4 (5.0) | 25.8 (5.6) |
| Smoking status (% yes) | 24 (0.6) | 305 (1.2) |
| Gravidity (mean (SD)) | 1.6 (1.8) | 1.6 (1.7) |
| Parity (mean (SD)) | 0.5 (1.0) | 0.4 (0.8) |
| History of preterm birth (% yes) | 194 (5.2) | 1566 (6.4) |
| History of spontaneous abortion (% yes) | 1403 (37.8) | 10,108 (41.3) |
| SART diagnosis | ||
| Male infertility (%) | 716 (19.3) | 4130 (16.9) |
| Endometriosis (%) | 172 (4.6) | 1490 (6.1) |
| Polycystic ovarian syndrome (%) | 95 (2.6) | 848 (3.5) |
| Diminished ovarian reserve (%) | 2749 (74.1) | 19,636 (80.1) |
| Tubal factor (%) | 183 (4.9) | 1855 (7.6) |
| Uterine (%) | 281 (7.6) | 1492 (6.1) |
| Unexplained (%) | 178 (4.8) | 1103 (4.5) |
| Other (%) | 1273 (34.3) | 3633 (14.8) |
| Geographic region | ||
| Midwest (%) | 309 (8.3) | 4111 (16.8) |
| Northeast (%) | 536 (14.4) | 5825 (23.8) |
| South (%) | 832 (22.4) | 8643 (35.3) |
| West (%) | 2032 (54.7) | 5897 (24.1) |
| Unknown (%) | 3 (0.1) | 26 (0.1) |
| Year | ||
| 2013 (%) | 283 (7.6) | 5801 (23.7) |
| 2014 (%) | 1310 (35.3) | 9614 (39.2) |
| 2015 (%) | 2119 (57.1) | 9087 (37.1) |
| # cycles per patient (mean (SD)) | 1.4 (0.7) | 1.4 (0.7) |
| Intracytoplasmic sperm injection (ICSI) | ||
| All/some (%) | 2671 (72.0) | 15,427 (63.0) |
| Assisted hatching | ||
| All/some (%) | 2046 (55.1) | 8483 (34.6) |
| Cycle type | ||
| Fresh (%) | 2050 (55.2) | 14,976 (61.1) |
| Frozen (%) | 1662 (44.8) | 9526 (38.9) |
| Embryo transfer (% yes) | 2372 (64.0) | 23,013 (94.1) |
| Type of embryo transfer a | ||
| Fresh (%) | 719 (30.3) | 13,568 (59.0) |
| Frozen (%) | 1653 (69.7) | 9445 (41.0) |
| Blastocyst (%) | 2068 (87.2) | 15,042 (65.4) |
| Number embryos transferred a | ||
| 1 (%) | 1593 (67.2) | 10,175 (44.2) |
| 2 (%) | 759 (32) | 12,187 (53.0) |
| 3+ (%) | 20 (0.8) | 651 (2.8) |
SD, standard deviation; BMI, body mass index; SART, Society for Assisted Reproductive Technology
aDenominator is number of cycles that completed embryo transfer. Denominator for all others is total cycles
Good obstetric outcome and secondary cycle outcomes
Amongst all cycle starts, the primary outcome, GBO, was 22% less likely after PGT (16.8 vs 22.2%; risk ratio 0.78; 95% CI 0.73, 0.84; p < 0.001). Similarly, PGT was 28% less likely to result in a live birth (95% CI 0.69, 0.75), 24% less likely to result in a term pregnancy (95% CI 0.71, 0.81), 27% less likely to achieve clinical pregnancy (95% CI 0.70, 0.76), and 29% less likely to result an in singleton birth (Table 2). However, PGT was associated with 26% decreased risk of SAB (95% CI 0.65, 0.85) and a 34% reduced risk of biochemical pregnancy. There was a significant interaction at the level of α = 0.05 between fresh or frozen cycle type and PGT on all outcomes in the cohort (Table 2). The interaction term was therefore included in the overall adjusted models. Similar to the overall model, patients undergoing fresh cycles had worse outcomes with PGT, though PGT did reduce risk of SAB and biochemical pregnancy (Table 2). However, patients undergoing frozen cycles had more ART success when PGT was done: frozen ET performed with PGT was 12% more likely to result in a GBO (25.5 vs 19.9%; RR 1.12; 95% CI 1.02, 1.22; p = 0.018), 11% more likely to result in a live birth (95% CI 1.05, 1.17), 9% more likely to result in a term delivery (95% CI 1.004, 1.18), 10% more likely to result in a clinical pregnancy (95% CI 10.5, 1.15), and 11% more likely to result in a singleton birth (95% CI 1.04, 1.18) (Table 2).
Table 2.
Adjusted differences in PGT vs no PGT for all cycles (N = 28,214)
| Outcome | PGT (N = 3712) | No PGT (N = 24,502) | Estimate without interaction (95% CI) | p value | Fresh PGT (N = 2050) | Fresh no PGT (N = 14,976) | Frozen PGT (N = 1662) | Frozen no PGT (N = 9526) | Effect of PGT within fresh cycles (95% CI) | p value | Effect of PGT within frozen cycles (95% CI) | p value | Interaction p value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GBO | 622 (16.8) | 5448 (22.2) | 0.78 (0.73, 0.84) | < 0.001 | 198 (9.7) | 3553 (23.7) | 424 (25.5) | 1895 (19.9) | 0.47 (0.41, 0.54) | < 0.001 | 1.12 (1.02, 1.22) | 0.018 | < 0.001 |
| LB | 1139 (30.7) | 11,200 (45.7) | 0.72 (0.69, 0.75) | < 0.001 | 402 (19.6) | 7550 (50.4) | 737 (44.3) | 3650 (38.3) | 0.43 (0.39, 0.47) | < 0.001 | 1.11 (1.05, 1.17) | < 0.001 | < 0.001 |
| Term | 753 (20.3) | 6819 (27.8) | 0.76 (0.71, 0.81) | < 0.001 | 256 (12.5) | 4477 (29.9) | 497 (29.9) | 2342 (24.6) | 0.47 (0.42, 0.53) | < 0.001 | 1.09 (1.004, 1.18) | 0.040 | < 0.001 |
| SAB | 237 (6.4) | 2162 (8.8) | 0.74 (0.65, 0.85) | < 0.001 | 75 (3.7) | 1251 (8.4) | 162 (9.7) | 911 (9.6) | 0.48 (0.38, 0.60) | < 0.001 | 0.99 (0.85, 1.17) | 0.960 | < 0.001 |
| CP | 1384 (37.3) | 13,485 (55.0) | 0.73 (0.70, 0.76) | < 0.001 | 478 (23.3) | 8882 (59.3) | 906 (54.5) | 4603 (48.3) | 0.44 (0.40, 0.47) | < 0.001 | 1.10 (1.05, 1.15) | < 0.001 | < 0.001 |
| BP | 207 (5.6) | 2077 (8.5) | 0.66 (0.58, 0.77) | < 0.001 | 50 (2.4) | 1053 (7.0) | 157 (9.4) | 1024 (10.7) | 0.38 (0.28, 0.50) | < 0.001 | 0.87 (0.74, 1.03) | 0.108 | < 0.001 |
| Singleton | 959 (25.8) | 8348 (34.1) | 0.81 (0.77, 0.85) | < 0.001 | 307 (15.0) | 5335 (35.6) | 652 (39.2) | 3013 (31.6) | 0.50 (0.45, 0.55) | < 0.001 | 1.11 (1.04, 1.18) | 0.002 | < 0.001 |
Table includes adjusted outcomes for all cycles (top) and including the interaction term (bottom). All estimates adjusted for donor age, patient age, BMI, smoking status, parity, history of preterm birth, history of spontaneous birth, assisted hatching, single embryo transfer, cycle type, and blastocyst transfer
GBO, good obstetric outcome; LB, live birth; Term, term pregnancy; SAB, spontaneous abortion; CP, clinical pregnancy; BP, biochemical pregnancy; PGT, preimplantation genetic testing
There were 25,385 cycles that resulted in an embryo transfer totaling 64% (n = 2372) of PGT cycles and 94% (n = 23,013) of no PGT cycles. There was no significant interaction at the level of α = 0.05 between fresh or frozen cycle type and PGT in the subset of cycles that resulted in an ET (Table 8 in the Supplementary Appendix). Thus, we did not include the interaction term in these overall models. After adjustment, the primary outcome, GBO, was 8% more likely after PGT (26.2 vs 23.7%; risk ratio 1.08; 95% CI 1.001, 1.16; p = 0.047) (Table 3). Similarly, PGT was 7% more likely to result in a live birth (95% CI 1.03, 1.12), 7% more likely to result in a term pregnancy (95% CI 1.01, 1.14), 7% more likely achieve a clinical pregnancy (95% CI 1.03, 1.11), and 8% more likely to result in a singleton delivery (95% CI 1.03, 1.14) (Table 3). There was no difference in rate of spontaneous abortion, biochemical pregnancy, and ongoing implantation between groups. There were a total of 15,556 infants born to this cohort. Gestational age-adjusted weight was slightly lower when PGT was not performed (mean difference − 0.07; 95% CI − 0.14, 0; p = 0.050).
Table 3.
Adjusted differences in PGT vs no PGT for cycles that resulted in an embryo transfer
| Outcome | PGT (N = 2372) | No PGT (N = 23,013) | Adjusted estimate (95% CI) | p value |
|---|---|---|---|---|
| Good birth outcome | 622 (26.2) | 5448 (23.7) | 1.08 (1.001, 1.16) | 0.047 |
| Live birth | 1139 (48) | 11,200 (48.7) | 1.07 (1.03, 1.12) | 0.002 |
| Term | 753 (31.7) | 6819 (29.6) | 1.07 (1.01, 1.14) | 0.032 |
| Spontaneous abortion | 237 (10) | 2162 (9.4) | 1.05 (0.92, 1.21) | 0.430 |
| Clinical pregnancy | 1384 (58.3) | 13,484 (58.6) | 1.07 (1.03, 1.11) | < 0.001 |
| Biochemical | 207 (8.7) | 2074 (9) | 0.89 (0.77, 1.03) | 0.109 |
| Singleton | 959 (40.4) | 8348 (36.3) | 1.08 (1.03, 1.14) | 0.004 |
| Z-score for GAAWa | 0 (1.1) | − 0.1 (1.1) | − 0.07 (− 0.14, 0.00) | 0.050 |
| OIR* | 0.8 (0.4) | 0.8 (0.3) | 0.00 (− 0.02, 0.02) | 0.654 |
aContinuous outcome
All estimates adjusted for donor age, patient age, BMI, smoking status, parity, history of preterm birth, history of spontaneous birth, assisted hatching, single embryo transfer, cycle type, and blastocyst transfer
PGT, preimplantation genetic testing; GAAW, gestational age-adjusted weight; OIR, ongoing implantation rate
Sensitivity analyses
There were 9154 singleton births in the cohort. Of these, 933 were PGT transfers and 8221 were no PGT transfers. There was no significant difference in Z-scores when PGT was utilized compared with no PGT cycles (mean difference − 0.06; 95% CI − 0.13, 0.02; p = 0.14). When stratified by infant sex, there was still no significant difference in Z-scores between PGT and non-PGT cycles (male infant mean difference − 0.09; 95% CI − 0.20, 0.01; p = 0.09; female infant mean difference − 0.02; 95% CI − 0.12, 0.09; p = 0.74) (Fig. 1). There was not a difference between groups for rates of LGA (1.0 PGT vs 9.4% no PGT; 95% CI 0.76, 1.16) or SGA (1.3 PGT vs 9.6% no PGT; 95% CI 0.97, 1.41) infants.
Fig. 1.
Gestational age-adjusted weight for all singleton infants according to PGT status and infant sex. Includes singleton births born during the study period. Adjusted for donor age, recipient age, BMI, smoking status, parity, history of preterm birth, history of spontaneous birth, assisted hatching, single embryo transfer, cycle type (fresh vs frozen), and blastocyst transfer. Vertical lines represent 95% confidence interval
When only the first cycle per patient was considered, there were significant interactions between PGT and cycle type (fresh or frozen) on good birth outcome (p = 0.010), live birth (p = 0.007), clinical pregnancy (p = 0.010), and singleton births (p = 0.025) (Table 4). Given this significant interaction, we herein report findings separately for PGT and no PGT groups for the first cycles. Patients with frozen ET as their first transfer cycle had more ART success when PGT was done: frozen ET performed with PGT was 23% more likely to result in a GBO (RR 1.23; 95% CI 1.11, 1.37; p < 0.001), 18% more likely to result in a live birth (RR 1.18; 95% CI 1.10, 1.27; p < 0.001), 16% more likely to results in a clinical pregnancy (RR 1.16; 95% CI 1.09, 1.22; p < 0.001), and 17% more likely to result in a singleton pregnancy (RR 1.17; 95% CI 1.09, 1.27; p < 0.001). On the contrary, patients with fresh ET as their first transfer cycle did not have better outcomes based on PGT. Regardless of transfer type (frozen vs fresh), in the first transfer cycle, PGT was 10% more likely to result in a term birth (RR 1.10; 95% CI 1.03, 1.19; p = 0.006), and 21% less likely to result in a biochemical loss (RR 0.79; 95% CI 0.66, 0.95; p = 0.014).
Table 4.
Sensitivity analysis of only the first observed transfer per patient (N = 18,417)
| Outcome | Estimate without interaction (95% CI) | p value | Effect of PGT within fresh transfers (95% CI) | p value | Effect of PGT within frozen transfers (95% CI) | p value | Interaction p value |
|---|---|---|---|---|---|---|---|
| GBO | 1.12 (1.03, 1.21) | 0.007 | 0.99 (0.87, 1.12) | 0.892 | 1.23 (1.11, 1.37) | < 0.001 | 0.010 |
| LB | 1.10 (1.05, 1.16) | < 0.001 | 1.04 (0.97, 1.11) | 0.310 | 1.18 (1.10, 1.27) | < 0.001 | 0.007 |
| Term | 1.10 (1.03, 1.19) | 0.006 | 1.04 (0.93, 1.15) | 0.507 | 1.17 (1.05, 1.29) | 0.001 | 0.093 |
| SAB | 1.09 (0.93, 1.27) | 0.284 | 1.16 (0.91, 1.47) | 0.228 | 1.04 (0.85, 1.28) | 0.677 | 0.514 |
| CP | 1.09 (1.05, 1.14) | < 0.001 | 1.04 (0.99, 1.10) | 0.143 | 1.16 (1.09, 1.22) | < 0.001 | 0.010 |
| BP | 0.79 (0.66, 0.95) | 0.014 | 0.84 (0.62, 1.14) | 0.258 | 0.77 (0.61, 0.97) | 0.027 | 0.660 |
| Singleton | 1.10 (1.04, 1.17) | 0.001 | 1.03 (0.94, 1.12) | 0.563 | 1.17 (1.09, 1.27) | < 0.001 | 0.025 |
| Z-scorea | − 0.11 (− 0.19, − 0.02) | 0.011 | − 0.08 (− 0.21, 0.05) | 0.229 | − 0.13 (− 0.23, − 0.03) | 0.012 | 0.556 |
| OIR* | 0.00 (− 0.02, 0.03) | 0.764 | − 0.02 (− 0.05, 0.02) | 0.321 | 0.02 (− 0.01, 0.05) | 0.251 | 0.131 |
All estimates adjusted for donor age, patient age, BMI, smoking status, parity, history of preterm birth, history of spontaneous birth, assisted hatching, single embryo transfer, cycle type, and blastocyst transfer
GBO, good obstetric outcome; LB, live birth; Term, term pregnancy; SAB, spontaneous abortion; CP, clinical pregnancy; BP, biochemical pregnancy; OIR, ongoing implantation rate; PGT, preimplantation genetic testing
aContinuous outcome
When only single ET was considered, there were no significant differences in outcomes from the primary analysis, and a significant interaction was identified between PGT and transfer type for the clinical pregnancy outcome (Table 7 of the Supplementary Appendix). This too showed improved clinical pregnancy for PGT in frozen transfers (RR 1.15; 95% CI 1.08, 1.22; p < 0.001) but no improvement in fresh PGT transfers (RR 1.04; 95% CI 0.96, 1.12; p = 0.39).
To reflect more modern IVF practices, when only single, blastocyst ET cycles were considered, the primary outcome, GBO, was no longer significantly different between PGT and non-PGT groups (RR 1.09; 95% CI 0.99, 1.19; p = 0.07). While PGT still significantly increased chances of a live birth (RR 0.10; 95% CI 1.03, 1.1; p = 0.07) and clinical pregnancy (RR 1.08; 95% CI 1.02, 1.14; p = 0.008), there was no longer a statistical benefit for chances of a term birth (RR 1.07; 95% CI 0.99, 1.16; p = 0.09) or singleton birth (RR 1.07; 95% CI 0.997, 1.15; p = 0.06).
Discussion
In this national database study of fresh donor oocyte ART cycles, a good birth outcome was largely dependent on the cycle type being performed. Frozen cycles where PGT was performed were more likely to result in a good birth outcome (RR 1.12; 95% CI 1.02, 1.22; p = 0.018), whereas PGT was detrimental to a good birth outcome in fresh cycles (RR 0.47; 9% CI 0.41, 0.54; p < 0.001). Given the relatively low prevalence of aneuploid embryos in donor oocyte cycles as well as the prior published studies reporting detrimental impact from PGT on donor oocyte recipients in the US national cohorts, these finding was unexpected [5, 6]. We found a significant interaction existed between PGT and cycle type (fresh vs frozen) amongst the cohort, as well as in our sensitivity analysis that included only the first ET (i.e., the best prognosis transfer), such that amongst frozen (but not fresh) cycles, PGT was more likely to result in a GBO, live birth, clinical pregnancy, and singleton birth. Additionally, regardless of cycle type, for all first ET, PGT was associated with a higher likelihood of term birth and lower chance of biochemical pregnancy. While PGT cycles were less likely to undergo embryo transfer than non-PGT cycles, amongst frozen cycles and all cycles that resulted in a transfer, PGT was associated with better birth outcomes. PGT, however, did not improve GBO when only single, blastocyst ET cycles were considered, though it did still significantly increase clinical pregnancy and live birth rates.
It is worth noting significant differences between the PGT and no PGT groups. PGT was associated with higher rates of blastocyst ET, single ET, and frozen ET. We attempted to control for these differences by forcing these variables into our model. Additionally, we were able to include an interaction term to determine effects of fresh or frozen cycles on outcomes, which has proven to be of value for all cycles, as well as the first ET cycle. It appears that PGT in fresh cycles was detrimental to outcomes. When only the first fresh ET cycle was considered, PGT was also not beneficial. It remains unclear why PGT would perform better in frozen cycles. PGT-A may guide selection of the best quality euploid embryo for the first frozen ET cycle, though the STAR trial would argue against this benefit. The STAR trial demonstrated no benefit of PGT for good prognosis patients less than 35 years of age [4]. The STAR randomized trial would presumably imply that PGT is likely not beneficial for donor oocyte recipients as well, though this was not their specific study population. When we controlled for single, blastocyst ET, there was not a statistical interaction with transfer type, and we no longer saw improved chances of GBO with PGT, though PGT still increased chances of live birth and clinical pregnancy. This analysis narrowed the heterogeneity of the cohort, and demonstrated that single blastocyst transfer in either group will have similar chance of a term, singleton pregnancy, but PGT may increase chances of achieving pregnancy and live birth. We suggest and hope that this important question will be subject to clinical trial in the future.
Donor oocyte recipients differ from women undergoing autologous cycles, including but not limited to the unique advantage of transferring a fresh or frozen embryo into a physiologic uterus lacking the superovulatory uterine environment typical of autologous fresh transfers, and utilizing healthy young oocytes [20]. However, few studies have evaluated the efficacy of PGT in donor oocyte cycles. The largest published study to date is a SART cohort that analyzed fresh donor oocyte cycles from 2004 to 2013 [6]. That study found that PGT-A was detrimental to cycle outcome during the period of observation, even when attempting cross-sectional analysis for practice changes in trophectoderm biopsy from 2010 to 2013. Consistent with other PGT literature, they found that PGT cycles were less likely to result in a transfer, which is purported due to lack of euploid embryos, but possibly effect of PGT as well [4, 6, 21]. Notable strengths of that analysis included their stringent criteria for PGT-A, as they excluded any possible other reason for PGT, including sex selection or testing for monosomy, though this ultimately significantly reduced power to include only 392 total PGT cycles for analysis over a 10-year study period. Additionally, significant changes in ART practice occurred throughout their study period, including freezing methods, PGT platform, and transition from blastomere to trophectoderm biopsy. Notably, their analysis did start to see increasing rates in live birth per cycle from 2010 to 2013, with nearly equal rates of live birth per cycle between PGT and controls in 2013. Our study continues this inquiry and adds the analytic complexity of the statistical interaction, which showed a significant difference in outcome based on fresh vs frozen cycle type. We also pushed our primary outcome measure beyond live birth to include a good birth outcome. A single institution study compared fresh, frozen, and frozen with PGT-A transfers in fresh donor oocyte recipients [22]. Amongst frozen cycles, they found no significant benefit of PGT-A [22]. Several small studies have also shown no benefit of PGT-A in donor oocyte cycles [23, 24]). Contrary to the previously published smaller studies, in our analysis of over 28,000 cycles from the national database, PGT was more likely to result in a GBO in frozen cycles. We also found amongst fresh cycles that PGT was less likely to result in a SAB or biochemical pregnancy, and frozen ET cycles were less likely to result in biochemical pregnancy. This is an interesting finding given that 16% of patients seeking PGT-A are interested due to desire to reduce SAB risk [25]. Amongst frozen cycles and single ET cycles, however, we found no difference in SAB risk. While a history of SAB was slightly higher in the no PGT group, we did control for this in our model, and found significant risk reduction with PGT in fresh cycles only. Furthermore, we saw no improvement in SAB reduction with PGT in our sensitivity analysis that included only the first ET. Previous reports on PGT-A and SAB improvement are mixed, with some supporting outcomes of improved pregnancy retention after euploid embryo transfer [26, 27] and some demonstrating no significant reduction in SAB with PGT in patients with a history of RPL [28]. Our findings are mixed within themselves and demonstrate that PGT for the purpose of reducing SAB risk in its most practiced state, frozen ET, has not shown benefit. Notwithstanding the inherent limitations of the retrospective study design, our study is strengthened by its power and robust statistical analyses. We herein present compelling data to promote the need for prospective studies in the donor oocyte recipient population.
A purported benefit of PGT is the associated increased rate of single ET [1, 29]. Indeed, we found increased rates of single ET in PGT ET cycles. Of note, the rates of multiple embryo transfer were strikingly high in both groups, particularly given that this was a donor oocyte cohort which traditionally assumes high egg quality and decreased aneuploidy rates. Whereas in PGT cycles, single ET was performed in nearly two-thirds of cycles, in no PGT cycles single ET was performed in less than half of cycles, resulting in multiple rates of 16% of PGT births and 25% of no PGT births. This high rate of multiple embryo transfer is an alarming and concerning trend that was also reported in a prior SART study from 2011 to 2012 evaluating poor compliance with ASRM embryo transfer guidelines [30]. We have narrowed the outcome of our study to not simply reflect live birth rates, but have emphasized a GBO as the ultimate goal to be achieved by patients and fertility clinics alike.
Amongst the 9154 singleton births in this cohort, we found no significant effects of PGT on gestational age-adjusted birthweight, both when analyzed per transfer and when only singleton births were assessed. We also found no significant difference in rates of LGA or SGA infants when PGT embryos were transferred. These results coincide with a large single-center cohort study that analyzed birth outcomes for PGT vs non-PGT cycles from 2011 to 2018 and found no difference in birthweight from singleton pregnancies between groups [31]. Our birthweight findings are reassuring given the increasing prevalence of PGT in IVF.
Over half of all PGT cycles were performed in the Western US, followed by the Southern US performing the most PGT cycles. Unfortunately, due to SART-CORS’ specific constraints regarding identifying specific clinics for research purposes, we were unable to assess practices or outcomes for individual clinics with this dataset. Our findings suggest differences in regional practice patterns, possibly guided by physician practice and patient-specific requests or incentives. Nearly 30% of the PGT transfers were fresh, and while the number of fresh PGT transfers did not change significantly over the years, the rates of fresh ET decreased as overall rates of PGT rose. Since only several centers in the country have ever had the capability of biopsy with subsequent fresh transfer in the same cycle, this observation suggests that there are only several IVF clinics making up at least a third of PGT cycles that were analyzed in this study [2, 32, 33]. Barad and colleagues showed increasing use of PGT nationally in donor cycles from 2010 to 2013, and we saw continuation of this practice with increased utilization throughout each subsequent year of our study [6]. The rise in PGT in donor cycles further necessitates a prospective analysis of its efficacy in this population.
Strengths of this study include the recent, narrow timeframe, intended to reflect contemporary IVF practices such as blastocyst transfer, vitrification, and trophectoderm biopsy. We utilized a fresh donor oocyte cohort which eliminates confounding of oocyte freeze or thaw, and also provides a homogenous patient population in regard to egg quality consideration that might be inherent to frozen oocytes. Given the variability in IVF practice and techniques amongst individual clinics, we controlled for 10 confounders within our multivariable analysis and excluded observations where any confounders were unavailable. Finally, the power of this study decreases the likelihood of associations simply due to chance alone.
There are limitations to consider with this analysis. First, the retrospective nature is inherently limiting. With this database, we are unable to ascertain the type of PGT that was performed. We also did not discriminate reason for PGT, as the SART database did not require clinics to indicate reason for PGT during the timepoints retrieved. We therefore assumed that the majority of the PGT indications in this population would be for aneuploidy, and possibly sex selection, with fewer indications for monosomic disorders in donor oocytes. However, we analyzed over 25,000 cycles in a subset that achieved ET; thus, regardless of indication for PGT, the availability of an embryo for transfer was met for a large number of cycles. From SART, we defined PGT as cycles where “all” or “some” of the embryos were marked as having PGT testing, and therefore lacks a guarantee the specific embryo transferred underwent PGT. There was also no indication within SART during this period whether a euploid or mosaic embryo was transferred. All of the included PGT cycles in 2013 were fresh ETs, which is likely not reflective of actual practice. We contribute this limiting observation to the nature of PGT selection in the SART database. Prior to 2013, we were unable to link to the index fresh cycle where PGT was entered, and thus excluded frozen cycles missing this entry variable. While the majority of PGT embryos were transferred at the blastocyst stage, there is no indication as to whether blastomere or trophectoderm biopsy was performed in the SART database. However, given that PGT was not detrimental in our study, it is most probable that the majority of biopsies were blastocyst stage, as cleavage stage biopsy was revealed in 2013 to be harmful to implantation compared with trophectoderm biopsy and unbiopsied controls [34]. During the study period, the most prevalent platforms of PGT would have included array comparative genomic hybridization (aCGH), single nucleotide polymorphisms (SNP) array, or qPCR [35]. NexGen sequencing (NGS) was up and coming during the time frame studied and has since become a commonly used PGT platform [35, 36]. It is possible that NGS would increase sensitivity of detecting aneuploidy, thereby affecting number of embryos to transfer. However, PGT platform is not available on SART, so updated analysis as more SART data becomes available still would not address this. While our analysis is more contemporary than prior national studies, it is not fully reflective of modern PGT practice and is a limitation of this study. As more national data becomes available, another analysis will be necessary to reflect more current practice. Finally, our findings may conflate the benefits of PGT since our outcomes are analyzed per transfer. Since not all embryos are suitable for biopsy and subsequent transfer, our data may artificially raise cycle expectations for a poor prognosis patient [37].
Conclusions
Our findings suggest that PGT, as practiced during the most recently available national data in women using fresh donor oocytes, is not beneficial amongst fresh cycles, but may have value in donor oocyte cycles that undergo frozen ET. PGT in fresh donor oocyte cycles is associated with a reduced likelihood of achieving ET, but amongst frozen transfers increases rates of single ET and reduces multiple births. PGT did not significantly affect gestational age-adjusted birthweight amongst singleton births, which is reassuring given its prevalent use. We maintain that further prospective studies are needed to evaluate the utility of PGT for fresh donor oocyte recipients.
Electronic supplementary material
(DOCX 29 kb)
Acknowledgments
SART wishes to thank all of its members for providing clinical information to the SART CORS database for use by patients and researchers. Without the efforts of our members, this research would not have been possible.
Availability of data and material
We obtained our dataset cohort from the Society for Assisted Reproductive Technology-Clinical Outcome Reporting System (SART-CORS), a national IVF database. We received deidentified data for analysis, in compliance with the research standards of SART.
Author’s contributions
C. Roeca is the lead author who developed the study design and wrote the manuscript. R. Johnson performed the statistical analysis, as well as edited the manuscript. N. Carlson contributed to study design and statistical analysis. A. Polotsky oversaw study design and edited the manuscript.
Compliance with ethical standards
This study was approved by the Colorado Multiple Institutional Review Board at the University of Colorado. This data is not approved to be placed in a data repository.
Conflict of interest
The authors declare that they have no conflict of interest.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
(DOCX 29 kb)
Data Availability Statement
We obtained our dataset cohort from the Society for Assisted Reproductive Technology-Clinical Outcome Reporting System (SART-CORS), a national IVF database. We received deidentified data for analysis, in compliance with the research standards of SART.