Serum estradiol level on the fifth day of ovarian stimulation in a GnRH antagonist protocol can predict pregnancy outcomes in IVF/ICSI

Blom, Jessica N.; Tan, L.; Hughes, L.; Tekpetey, F.; Rafea, Basim Abu

doi:10.1016/j.fertnstert.2018.07.618

Cited by 3 publications

(3 citation statements)

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“…Basal testosterone, LH, TSH and E2 were less important. Nevertheless, Blom et al (2018) found that serum E2 levels on the fifth day of stimulation were significantly associated with serum E2 levels on the day of triggering, a strong predictor of stimulation response ( Kummer et al , 2013 )—future work might be able to achieve greater clinical value by recording and considering such during-treatment measurements on top of baseline characteristics.…”

Section: Discussionmentioning

confidence: 99%

Predicting the number of oocytes retrieved from controlled ovarian hyperstimulation with machine learning

Boulant

Chambost³

et al. 2023

Human Reproduction

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STUDY QUESTION Can machine learning predict the number of oocytes retrieved from controlled ovarian hyperstimulation (COH)? SUMMARY ANSWER Three machine-learning models were successfully trained to predict the number of oocytes retrieved from COH. WHAT IS KNOWN ALREADY A number of previous studies have identified and built predictive models on factors that influence the number of oocytes retrieved during COH. Many of these studies are, however, limited in the fact that they only consider a small number of variables in isolation. STUDY DESIGN, SIZE, DURATION This study was a retrospective analysis of a dataset of 11,286 cycles performed at a single centre in France between 2009 and 2020 with the aim of building a predictive model for the number of oocytes retrieved from ovarian stimulation. The analysis was carried out by a data analysis team external to the centre using the Substra framework. The Substra framework enabled the data analysis team to send computer code to run securely on the centre’s on-premises server. In this way, a high level of data security was achieved as the data analysis team did not have direct access to the data, nor did the data leave the centre at any point during the study. PARTICIPANTS/MATERIALS, SETTING, METHODS The Light Gradient Boosting Machine algorithm was used to produce three predictive models: one that directly predicted the number of oocytes retrieved and two that predicted which of a set of bins provided by two clinicians the number of oocytes retrieved fell into. The resulting models were evaluated on a held-out test set and compared to linear and logistic regression baselines. In addition, the models themselves were analysed to identify the parameters that had the biggest impact on their predictions. MAIN RESULTS AND THE ROLE OF CHANCE On average, the model that directly predicted the number of oocytes retrieved deviated from the ground truth by 4.21 oocytes. The model that predicted the first clinician’s bins deviated by 0.73 bins whereas the model for the second clinician deviated by 0.62 bins. For all models, performance was best within the first and third quartiles of the target variable, with the model underpredicting extreme values of the target variable (no oocytes and large numbers of oocytes retrieved). Nevertheless, the erroneous predictions made for these extreme cases were still within the vicinity of the true value. Overall, all three models agreed on the importance of each feature which was estimated using Shapley Additive Explanation (SHAP) values. The feature with the highest mean absolute SHAP value (and thus the highest importance) was the antral follicle count, followed by basal AMH and FSH. Of the other hormonal features, basal TSH, LH, and testosterone levels were similarly important and baseline LH was the least important. The treatment characteristic with the highest SHAP value was the initial dose of gonadotropins. LIMITATIONS, REASONS FOR CAUTION The models produced in this study were trained on a cohort from a single centre. They should thus not be used in clinical practice until trained and evaluated on a larger cohort more representative of the general population. WIDER IMPLICATIONS OF FINDINGS These predictive models for the number of oocytes retrieved from COH may be useful in clinical practice, assisting clinicians in optimizing COH protocols for individual patients. Our work also demonstrates the promise of using the Substra framework for allowing external researchers to provide clinically relevant insights on sensitive fertility data in a fully secure, trustworthy manner and opens a number of exciting avenues for accelerating future research. STUDY FUNDING/COMPETING INTEREST(S) This study was funded by the French Public Bank of Investment as part of the Healthchain Consortium. T.Fe., C.He., J.C., C.J., C.-A.P., and C.Hi. are employed by Apricity. C.Hi. has received consulting fees and honoraria from Vitrolife, Merck Serono, Ferring, Cooper Surgical, Dibimed, Apricity, and Fairtility and travel support from Fairtility and Vitrolife, participates on an advisory board for Merck Serono, was the founder and organizer of the AI Fertility conference, has stock in Aria Fertility, TMRW, Fairtility, Apricity, and IVF Professionals, and received free equipment from Planar in exchange for first user feedback. C.J. has received a grant from BPI. J.C. has also received a grant from BPI, is a member of the Merck AI advisory board, and is a board member of Labelia Labs. C.He has a contract for medical writing of this manuscript by CHU Nantes and has received travel support from Apricity. A.R. haș received honoraria from Ferring and Organon. T.Fe. has received a grant from BPI. TRIAL REGISTRATION NUMBER N/A.

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Section: Discussionmentioning

confidence: 99%

Predicting the number of oocytes retrieved from controlled ovarian hyperstimulation with machine learning

Boulant

Chambost³

et al. 2023

Human Reproduction

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“…Evidence points a close proportional relation regarding the activity of COX‐2 and aromatase activity, E 2 production,and pregnancy rate (Blom, Tan, Hughes, Tekpetey, & Rafea, 2018; Stevenson & Pulley, 2016). Consistent with our results, serum E 2 concentration among NSAID users was significantly lower than nonusers of NSAIDs (Hudson et al, 2008; Majumder, Nandi, Omar, Ugwuagbo, & Lala, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Many attempts must be collected to establish approaches to use the low concentration of ASA with an effective outcome in IVF candidates. In line with this statement, Roy et al investigated the effects of anhydride conjugates of aspirin‐fatty acids, assuming this composition would have higher permeability across the cellular membrane and found this conjugate efficiently inhibited platelet aggregation in platelets only in AA‐induced platelet aggregation condition (Blom et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Arachidonic acid alleviates the detrimental effects of acetylsalicylic acid on human granulosa cells performance in vitro

Khajeh

Nouri

Ghasemzadeh

et al. 2020

Molecular Reproduction Devel

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Here, we investigated the biological effects of arachidonic acid (AA) in human cumulus granulosa cells (CGCs) after exposure to ASA. Cells were isolated from the follicular fluid and incubated with 0.5 mM acetylsalicylic acid (ASA) and 50 µM AA.Cell viability was analyzed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. E 2 and P 4 levels were measured by chemiluminescence assay.Expression of genes including CYP19A1, FACN, and SCD1 was measured by real-time polymerase chain reaction assay. Oxidative status was analyzed by monitoring glutathione peroxidase activity. The fatty acid profile was analyzed by the gas chromatography technique. Enzyme-linked immunosorbent assay was used to measure prostaglandin E 2 (PGE 2 ) in CGCs after exposure to ASA and AA. Protein levels of the estrogen receptor were studied by immunofluorescence staining. Ultrastructural changes were evaluated by transmission electron microscopy imaging.ASA treatment reduced E 2 production, Cyp19a1 expression, glutathione peroxidase (GPx) activity, and estradiol receptor expression in CGCs. The addition of AA prevented the ASA-induced E 2 reduction (p < .05) and expression of Cyp19a1.Moreover, AA increased the antioxidant capacity of CGCs exposed to ASA by promoting GPx activity (p < .05). AA increased monounsaturated fatty acid/ saturated fatty acid ratio compared with the ASA group (p < .05). AA supplementation triggered the synthesis and secretion of PGE 2 in ASA-treated CGCS (p < .05). Cytoplasmic vacuolation observed in the ASA group and treatment with AA intensified vacuolation rate. The expression of the estrogen receptor was increased

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Machine learning for predicting elective fertility preservation outcomes

Braude,

Haikin Herzberger,

Semo

et al. 2024

Sci Rep

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This retrospective study applied machine-learning models to predict treatment outcomes of women undergoing elective fertility preservation. Two-hundred-fifty women who underwent elective fertility preservation at a tertiary center, 2019–2022 were included. Primary outcome was the number of metaphase II oocytes retrieved. Outcome class was based on oocyte count (OC): Low (≤ 8), Medium (9–15) or High (≥ 16). Machine-learning models and statistical regression were used to predict outcome class, first based on pre-treatment parameters, and then using post-treatment data from ovulation-triggering day. OC was 136 Low, 80 Medium, and 34 High. Random Forest Classifier (RFC) was the most accurate model (pre-treatment receiver operating characteristic (ROC) area under the curve (AUC) was 77%, and post-treatment ROC AUC was 87%), followed by XGBoost Classifier (pre-treatment ROC AUC 74%, post-treatment ROC AUC 86%). The most important pre-treatment parameters for RFC were basal FSH (22.6%), basal LH (19.1%), AFC (18.2%), and basal estradiol (15.6%). Post-treatment parameters were estradiol levels on trigger-day (17.7%), basal FSH (11%), basal LH (9%), and AFC (8%). Machine-learning models trained with clinical data appear to predict fertility preservation treatment outcomes with relatively high accuracy.

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Serum estradiol level on the fifth day of ovarian stimulation in a GnRH antagonist protocol can predict pregnancy outcomes in IVF/ICSI

Cited by 3 publications

References 0 publications

Predicting the number of oocytes retrieved from controlled ovarian hyperstimulation with machine learning

Predicting the number of oocytes retrieved from controlled ovarian hyperstimulation with machine learning

Arachidonic acid alleviates the detrimental effects of acetylsalicylic acid on human granulosa cells performance in vitro

Machine learning for predicting elective fertility preservation outcomes

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