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Prediction of ovulation: new insight into an old challenge
Ultrasound monitoring and hormonal blood testing are considered by many as an accurate method to predict ovulation time. However, uniform and validated algorithms for predicting ovulation have yet to be defined. Daily hormonal tests and transvaginal ultrasounds were recorded to develop an algorithm...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10651856/ https://www.ncbi.nlm.nih.gov/pubmed/37968377 http://dx.doi.org/10.1038/s41598-023-47241-2 |
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author | Maman, Ettie Adashi, Eli Y. Baum, Micha Hourvitz, Ariel |
author_facet | Maman, Ettie Adashi, Eli Y. Baum, Micha Hourvitz, Ariel |
author_sort | Maman, Ettie |
collection | PubMed |
description | Ultrasound monitoring and hormonal blood testing are considered by many as an accurate method to predict ovulation time. However, uniform and validated algorithms for predicting ovulation have yet to be defined. Daily hormonal tests and transvaginal ultrasounds were recorded to develop an algorithm for ovulation prediction. The rupture of the leading ovarian follicle was a marker for ovulation day. The model was validated retrospectively on natural cycles frozen embryo transfer cycles with documented ovulation. Circulating levels of LH or its relative variation failed, by themselves, to reliably predict ovulation. Any decrease in estrogen was 100% associated with ovulation emergence the same day or the next day. Progesterone levels > 2 nmol/L had low specificity to predict ovulation the next day (62.7%), yet its sensitivity was high (91.5%). A model for ovulation prediction, combining the three hormone levels and ultrasound was created with an accuracy of 95% to 100% depending on the combination of the hormone levels. Model validation showed correct ovulation prediction in 97% of these cycles. We present an accurate ovulation prediction algorithm. The algorithm is simple and user-friendly so both reproductive endocrinologists and general practitioners can use it to benefit their patients. |
format | Online Article Text |
id | pubmed-10651856 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106518562023-11-15 Prediction of ovulation: new insight into an old challenge Maman, Ettie Adashi, Eli Y. Baum, Micha Hourvitz, Ariel Sci Rep Article Ultrasound monitoring and hormonal blood testing are considered by many as an accurate method to predict ovulation time. However, uniform and validated algorithms for predicting ovulation have yet to be defined. Daily hormonal tests and transvaginal ultrasounds were recorded to develop an algorithm for ovulation prediction. The rupture of the leading ovarian follicle was a marker for ovulation day. The model was validated retrospectively on natural cycles frozen embryo transfer cycles with documented ovulation. Circulating levels of LH or its relative variation failed, by themselves, to reliably predict ovulation. Any decrease in estrogen was 100% associated with ovulation emergence the same day or the next day. Progesterone levels > 2 nmol/L had low specificity to predict ovulation the next day (62.7%), yet its sensitivity was high (91.5%). A model for ovulation prediction, combining the three hormone levels and ultrasound was created with an accuracy of 95% to 100% depending on the combination of the hormone levels. Model validation showed correct ovulation prediction in 97% of these cycles. We present an accurate ovulation prediction algorithm. The algorithm is simple and user-friendly so both reproductive endocrinologists and general practitioners can use it to benefit their patients. Nature Publishing Group UK 2023-11-15 /pmc/articles/PMC10651856/ /pubmed/37968377 http://dx.doi.org/10.1038/s41598-023-47241-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Maman, Ettie Adashi, Eli Y. Baum, Micha Hourvitz, Ariel Prediction of ovulation: new insight into an old challenge |
title | Prediction of ovulation: new insight into an old challenge |
title_full | Prediction of ovulation: new insight into an old challenge |
title_fullStr | Prediction of ovulation: new insight into an old challenge |
title_full_unstemmed | Prediction of ovulation: new insight into an old challenge |
title_short | Prediction of ovulation: new insight into an old challenge |
title_sort | prediction of ovulation: new insight into an old challenge |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10651856/ https://www.ncbi.nlm.nih.gov/pubmed/37968377 http://dx.doi.org/10.1038/s41598-023-47241-2 |
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