Predicting oocyte fertilisability in intracytoplasmic sperm injection cycles: a retrospective observational study. (23rd February 2017)
- Record Type:
- Journal Article
- Title:
- Predicting oocyte fertilisability in intracytoplasmic sperm injection cycles: a retrospective observational study. (23rd February 2017)
- Main Title:
- Predicting oocyte fertilisability in intracytoplasmic sperm injection cycles: a retrospective observational study
- Authors:
- Orsi, Nicolas
Dasgupta, Tathagata
Cummings, Michele
Adebayo, Julius
Sharma, Vinay
Gunawardena, Jeremy
Field, Sarah - Abstract:
- Abstract: Background: Conception assisted by intracytoplasmic sperm injection (ICSI) requires oocyte stripping for morphological evaluation of maturity status. However, this approach prevents further maturation and poorly predicts fertilisability, so more robust assessment strategies are needed. Given that cytokines orchestrate oocyte development, we aimed to assess the association of follicular fluid cytokine profiles with maturation stage and develop predictive machine learning-based methods to identify those with the greatest fertilisation potential. Methods: In this retrospective study, follicular fluid was collected at oocyte retrieval from 64 women and linked to oocyte maturity status or fate—namely, germinal vesicle (n=26), metaphase I (51), metaphase II not fertilised (51), and metaphase II fertilised (84). 51 follicular fluid cytokines were profiled by multiplex immunoassay. Machine learning-based classifiers to predict oocyte fertilisability were subjected to iterative feature reduction to a threshold suitable for developing a clinically viable assessment of oocyte maturity. Women gave written, informed consent. Findings: Cytokine profiles varied dynamically throughout maturation. When applied to naive samples with known outcome, classifiers developed using tumour necrosis factor-related apoptosis-inducing ligand and interleukin 18 profiles alone correctly discriminated 89% of metaphase II not fertilised oocytes (ie, those with the highest fertilisability) withAbstract: Background: Conception assisted by intracytoplasmic sperm injection (ICSI) requires oocyte stripping for morphological evaluation of maturity status. However, this approach prevents further maturation and poorly predicts fertilisability, so more robust assessment strategies are needed. Given that cytokines orchestrate oocyte development, we aimed to assess the association of follicular fluid cytokine profiles with maturation stage and develop predictive machine learning-based methods to identify those with the greatest fertilisation potential. Methods: In this retrospective study, follicular fluid was collected at oocyte retrieval from 64 women and linked to oocyte maturity status or fate—namely, germinal vesicle (n=26), metaphase I (51), metaphase II not fertilised (51), and metaphase II fertilised (84). 51 follicular fluid cytokines were profiled by multiplex immunoassay. Machine learning-based classifiers to predict oocyte fertilisability were subjected to iterative feature reduction to a threshold suitable for developing a clinically viable assessment of oocyte maturity. Women gave written, informed consent. Findings: Cytokine profiles varied dynamically throughout maturation. When applied to naive samples with known outcome, classifiers developed using tumour necrosis factor-related apoptosis-inducing ligand and interleukin 18 profiles alone correctly discriminated 89% of metaphase II not fertilised oocytes (ie, those with the highest fertilisability) with high confidence from all other maturation stages. Interpretation: These classifiers offer the prospect of cost-effective, point-of-care testing, and streamlined ICSI-based workflows. This assessment circumvents stripping such that immature or low fertilisability oocytes could benefit from in-vitro maturation and increase the pool of usable oocytes. Further studies will confirm the robustness of these classifiers in women with a broader morbidity spectrum and their translational value across a range of clinical settings. Funding: Infertility Research Trust. … (more)
- Is Part Of:
- Lancet. Volume 389(2017)Supplement 1
- Journal:
- Lancet
- Issue:
- Volume 389(2017)Supplement 1
- Issue Display:
- Volume 389, Issue 1 (2017)
- Year:
- 2017
- Volume:
- 389
- Issue:
- 1
- Issue Sort Value:
- 2017-0389-0001-0000
- Page Start:
- S75
- Page End:
- Publication Date:
- 2017-02-23
- Subjects:
- Medicine -- Periodicals
Medicine -- Periodicals
Medicine
Medicine
Electronic journals
Periodicals
610.5 - Journal URLs:
- http://www.thelancet.com/ ↗
http://www.sciencedirect.com/science/journal/01406736 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/S0140-6736(17)30471-3 ↗
- Languages:
- English
- ISSNs:
- 0140-6736
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5146.000000
British Library DSC - BLDSS-3PM
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