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ObjectiveTo estimate the prevalence of low-prognosis patients according to the POSEIDON criteria using real-world data.DesignMulticenter population-based cohort study.SettingsFertility clinics in Brazil, Turkey, and Vietnam.PatientsInfertile women undergoing assisted reproductive technology using standard ovarian stimulation with exogenous gonadotropins.InterventionsNone.Main outcome measuresPer-period prevalence rates of POSEIDON patients (overall, stratified by POSEIDON groups and by study center) and the effect of covariates on the probability that a patient be classified as “POSEIDON”.ResultsA total of 13,146 patients were included. POSEIDON patients represented 43.0% (95% confidence interval [CI] 42.0–43.7) of the studied population, and the prevalence rates varied across study centers (range: 38.6–55.7%). The overall prevalence rates by POSEIDON groups were 44.2% (group 1; 95% CI 42.6–45.9), 36.1% (group 2; 95% CI 34.6–37.7), 5.2% (group 3; 95% CI 4.5–6.0), and 14.4% (group 4; 95% CI: 13.3–15.6). In general, POSEIDON patients were older, had a higher body mass index (BMI), lower ovarian reserve markers, and a higher frequency of female factor as the primary treatment indication than non-POSEIDON patients. The former required larger doses of gonadotropin for ovarian stimulation, despite achieving a 2.5 times lower number of retrieved oocytes than non-POSEIDON patients. Logistic regression analyses revealed that female age, BMI, ovarian reserve, and a female infertility factor were relevant predictors of the POSEIDON condition.ConclusionsThe estimated prevalence of POSEIDON patients in the general population undergoing ART is significant. These patients differ in clinical characteristics compared with non-POSEIDON patients. The POSEIDON condition is associated with female age, ovarian reserve, BMI, and female infertility. Efforts in terms of diagnosis, counseling, and treatment are needed to reduce the prevalence of low-prognosis patients.

This multicenter study evaluated the reliability of the recently published ART calculator for predicting the minimum number of metaphase II (MII) oocytes (MIImin) to obtain at least one euploid blastocyst in patients undergoing in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI). We used clinical and embryonic retrospective data of 1,464 consecutive infertile couples who underwent IVF/ICSI with the intention to have preimplantation genetic testing for aneuploidy. The validation procedure followed a stepwise approach. Firstly, we assessed the distribution of euploid blastocysts per patient and found that it followed a negative binomial distribution. Secondly, we used generalized linear models and applied the Lasso procedure–including MII oocytes to adjust the data–to select the factors predicting the response variable “euploid blastocyst.” Third, a logistic regression model–fit to the binomial response euploid (yes/no) for each MII oocyte–was built using the relevant factors. The observational unit was the “woman” whereas the response was the pair (m, n), where n is the number of retrieved MII oocytes and m the corresponding number of euploid blastocysts. The model was internally validated by randomly splitting the data into training and validation sets. The R-squares (~0.25) and the area under the ROC curve (~0.70) did not differ between the training and validation datasets. Fourth, mathematical equations and the calculated probabilities generated by the validation model were used to determine the MIImin required for obtaining at least one euploid blastocyst according to different success probabilities. Lastly, we compared the fittings generated by the validation model and the ART calculator and assessed the predictive value of the latter using the validation dataset. The fittings were sufficiently close for both the estimated probabilities of blastocyst euploid per MII oocyte (r = 0.91) and MIImin (r = 0.88). The ART calculator positive predictive values, i.e., the frequency of patients with at least one euploid blastocyst among those who achieved the estimated MIImin, were 84.8%, 87.5%, and 90.0% for 70%, 80%, and 90% predicted probabilities of success, respectively. The ART calculator effectively predicts the MIImin needed to achieve at least one euploid blastocyst in individual patients undergoing IVF/ICSI. The prediction tool might be used for counseling and planning IVF/ICSI treatments.

Study Question: What is the agreement between antral follicle count (AFC) and anti-Müllerian hormone (AMH) levels when used to patient classification according to the Patient-Oriented Strategies Encompassing IndividualizeD Oocyte Number (POSEIDON) criteria?Summary Answer: Our study indicates a strong agreement between the AFC and the AMH levels in classifying POSEIDON patients; thus, either can be used for this purpose, although one in four women will have discordant values when both biomarkers are used.What Is Known Already: According to the POSEIDON criteria, both AFC and AMH may be used to classify low-prognosis patients. Proposed AFC and AMH thresholds of 5 and 1.2 ng/ml, respectively, have their basis in published literature; however, no study has yet determined the reproducibility of patient classification in comparing one biomarker with the other, nor have their thresholds ever been validated within this patient population.Study Design, Size, Duration: A population-based cohort study involving 9484 consecutive patients treated in three fertility clinics in Brazil, Turkey and Vietnam between 2015 and 2017.Participants/materials, Setting, Methods: Participants were infertile women between 22 and 46 years old in their first in vitro fertilization/intracytoplasmic sperm injection cycle of standard ovarian stimulation with exogenous gonadotropins whose baseline ovarian reserves had been assessed by both AFC and AMH. Details of pre- and post-treatment findings were input into a coded research file. Two indicators of interest were created to classify patients according to the POSEIDON criteria based upon AFC and AMH values. Patients who did not fit any of the four POSEIDON groups were classified as non-POSEIDON. AFC was determined in the early follicular phase using two-dimensional (2D) transvaginal ultrasonography, whereas AMH values were based on the modified Beckman Coulter generation II enzyme-linked immunosorbent assay. Agreement rates were computed between AFC and AMH to classify patients using Cohen's kappa statistics. Logistic regression analyzes were carried out to examine the association between ovarian markers and low (<4) and suboptimal (4-9) oocyte yield.Main Results and the Role Of Chance: The degree of agreement in classifying patients according to POSEIDON groups was strong overall (kappa = 0.802; 95% CI: 0.792-0.811). A total of 73.8% of individuals were classified under the same group using both biomarkers. The disagreement rates were ∼26% and did not diverge when AFC or AMH was used as the primary biomarker criterion. Significant regression equations were found between ovarian markers and oocyte yield (P < 0.0001). For low oocyte yield, the optimal AFC and AMH cutoff values were 5 and 1.27 ng/ml with sensitivities of 0.61 and 0.66, specificities of 0.81 and 0.72, and AUC receiver operating characteristics of 0.791 and 0.751, respectively. For suboptimal oocyte yield respective AFC and AMH cutoffs were 12 and 2.97 ng/ml with sensitivities of 0.74 and 0.69, specificities of 0.76 and 0.66 and AUCs of 0.81 and 0.80.Limitations, Reasons For Caution: Our study relied on 2D transvaginal sonography to quantify the AFC and manual Gen II assay for AMH determination and classification of patients. AMH data must be interpreted in an assay-specific manner. Treatment protocols varied across centers potentially affecting patient classification.Wider Implications Of the Findings: Three of four patients will be classified the same using either AFC or AMH values. Both biomarkers provide acceptable and equivalent accuracy in predicting oocyte yield further supporting their use and proposed thresholds in daily clinical practice for patient classification according to the POSEIDON criteria. However, the sensitivity of POSEIDON thresholds in predicting low oocyte yield is low. Clinicians should adopt the biomarker that may best reflect their clinical setting.Study Funding/competing Interest(s): Unrestricted investigator-sponsored study grant (MS200059_0013) from Merck KGaA, Darmstadt, Germany. The funder had no role in study design, data collection, analysis, decision to publish or manuscript preparation. S.C.E. declares receipt of unrestricted research grants from Merck and lecture fees from Merck and Med.E.A. H.Y. declares receipt of payment for lectures from Merck and Ferring. L.N.V. receives speaker fees and conferences from Merck, Merck Sharp and Dohme (MSD) and Ferring and research grants from MSD and Ferring. T.M.H. received speaker fees and conferences from Merck, MSD and Ferring. The remaining authors have nothing to disclose.Trial Registration Number: not applicable. [ABSTRACT FROM AUTHOR]

Objective: To estimate the prevalence of low-prognosis patients according to the POSEIDON criteria using real-world data. Design: Multicenter population-based cohort study. Settings: Fertility clinics in Brazil, Turkey, and Vietnam. Patients: Infertile women undergoing assisted reproductive technology using standard ovarian stimulation with exogenous gonadotropins. Interventions: None. Main outcome measures: Per-period prevalence rates of POSEIDON patients (overall, stratified by POSEIDON groups and by study center) and the effect of covariates on the probability that a patient be classified as "POSEIDON". Results: A total of 13,146 patients were included. POSEIDON patients represented 43.0% (95% confidence interval [CI] 42.0–43.7) of the studied population, and the prevalence rates varied across study centers (range: 38.6–55.7%). The overall prevalence rates by POSEIDON groups were 44.2% (group 1; 95% CI 42.6–45.9), 36.1% (group 2; 95% CI 34.6–37.7), 5.2% (group 3; 95% CI 4.5–6.0), and 14.4% (group 4; 95% CI: 13.3–15.6). In general, POSEIDON patients were older, had a higher body mass index (BMI), lower ovarian reserve markers, and a higher frequency of female factor as the primary treatment indication than non-POSEIDON patients. The former required larger doses of gonadotropin for ovarian stimulation, despite achieving a 2.5 times lower number of retrieved oocytes than non-POSEIDON patients. Logistic regression analyses revealed that female age, BMI, ovarian reserve, and a female infertility factor were relevant predictors of the POSEIDON condition. Conclusions: The estimated prevalence of POSEIDON patients in the general population undergoing ART is significant. These patients differ in clinical characteristics compared with non-POSEIDON patients. The POSEIDON condition is associated with female age, ovarian reserve, BMI, and female infertility. Efforts in terms of diagnosis, counseling, and treatment are needed to reduce the prevalence of low-prognosis patients. [ABSTRACT FROM AUTHOR]

This multicenter study evaluated the reliability of the recently published ART calculator for predicting the minimum number of metaphase II (MII) oocytes (MIImin) to obtain at least one euploid blastocyst in patients undergoing in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI). We used clinical and embryonic retrospective data of 1,464 consecutive infertile couples who underwent IVF/ICSI with the intention to have preimplantation genetic testing for aneuploidy. The validation procedure followed a stepwise approach. Firstly, we assessed the distribution of euploid blastocysts per patient and found that it followed a negative binomial distribution. Secondly, we used generalized linear models and applied the Lasso procedure–including MII oocytes to adjust the data–to select the factors predicting the response variable "euploid blastocyst." Third, a logistic regression model–fit to the binomial response euploid (yes/no) for each MII oocyte–was built using the relevant factors. The observational unit was the "woman" whereas the response was the pair (m, n), where n is the number of retrieved MII oocytes and m the corresponding number of euploid blastocysts. The model was internally validated by randomly splitting the data into training and validation sets. The R-squares (~0.25) and the area under the ROC curve (~0.70) did not differ between the training and validation datasets. Fourth, mathematical equations and the calculated probabilities generated by the validation model were used to determine the MIImin required for obtaining at least one euploid blastocyst according to different success probabilities. Lastly, we compared the fittings generated by the validation model and the ART calculator and assessed the predictive value of the latter using the validation dataset. The fittings were sufficiently close for both the estimated probabilities of blastocyst euploid per MII oocyte (r = 0.91) and MIImin (r = 0.88). The ART calculator positive predictive values, i.e., the frequency of patients with at least one euploid blastocyst among those who achieved the estimated MIImin, were 84.8%, 87.5%, and 90.0% for 70%, 80%, and 90% predicted probabilities of success, respectively. The ART calculator effectively predicts the MIImin needed to achieve at least one euploid blastocyst in individual patients undergoing IVF/ICSI. The prediction tool might be used for counseling and planning IVF/ICSI treatments. [ABSTRACT FROM AUTHOR]

The POSEIDON group (P atient- O riented S trategies E ncompassing I ndividualize D O ocyte N umber) has introduced "the ability to retrieve the number of oocytes needed to achieve at least one euploid embryo for transfer" as an intermediate marker of successful outcome in IVF/ICSI cycles. This study aimed to develop a novel calculator to predict the POSEIDON marker. We analyzed clinical and embryonic data of infertile couples who underwent IVF/ICSI with the intention to have trophectoderm biopsy for preimplantation genetic testing for aneuploidy. We used the negative binomial distribution to model the number of euploid blastocysts and the adaptive LASSO (Least Absolute Shrinkage and Selection Operator) method for variable selection. The fitted model selected female age, sperm source used for ICSI, and the number of mature (metaphase II) oocytes as predictors (p < 0.0001). Female age was the most important factor for predicting the probability of a blastocyst being euploid given each mature oocyte (loglikelihood of age [adjusted for sperm source]: 30.9; df = 2; p < 0.0001). The final predictive model was developed using logistic regression analysis, and internally validated by the holdout method. The predictive ability of the model was assessed by the ROC curve, which resulted in an area under the curve of 0.716. Using the final model and mathematical equations, we calculated the individualized probability of blastocyst euploidy per mature retrieved oocyte and the minimum number of mature oocytes required to obtain ≥1 euploid blastocyst—with their 95% confidence interval [CI]—for different probabilities of success. The estimated predicted probabilities of a mature oocyte turn into a euploid blastocyst decreased progressively with female age and was negatively modulated overall by use of testicular sperm across age (p < 0.001). A calculator was developed to make two types of predictions automatically, one using pretreatment information to estimate the minimum number of mature oocytes to achieve ≥1 euploid blastocyst, and another based on the actual number of mature oocytes collected/accumulated to estimate the chances of having a euploid blastocyst using that oocyte cohort for IVF/ICSI. The new ART calculator may assist in clinical counseling and individualized treatment planning regarding the number of oocytes required for at least one euploid blastocyst in IVF/ICSI procedures. [ABSTRACT FROM AUTHOR]