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Establishment of predictive model for analyzing clinical pregnancy outcome based on IVF-ET and ICSI assisted reproductive technology
In order to explore the predictive model for analyzing clinical pregnancy outcomes based on IVF-ET (in vitro fertilization and embryo transfer) and ICSI (Intracytoplasmic sperm injection) assisted reproductive technology (ART). Methods: this study selected the embryo transfer (fresh) patients who re...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7105670/ https://www.ncbi.nlm.nih.gov/pubmed/32256165 http://dx.doi.org/10.1016/j.sjbs.2020.02.021 |
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author | Jiang, Songwei Li, Liuming Li, Feiwen Li, Mujun |
author_facet | Jiang, Songwei Li, Liuming Li, Feiwen Li, Mujun |
author_sort | Jiang, Songwei |
collection | PubMed |
description | In order to explore the predictive model for analyzing clinical pregnancy outcomes based on IVF-ET (in vitro fertilization and embryo transfer) and ICSI (Intracytoplasmic sperm injection) assisted reproductive technology (ART). Methods: this study selected the embryo transfer (fresh) patients who received IVF-ET or ICSI treatment in the First Affiliated Hospital of Guangxi Medical University as the subjects. Moreover, the controlled ovarian stimulation (COS) and follow-up were conducted to collect relevant data for analysis, and finally a prediction model was established. Results: The results showed that the patients were divided into different ovarian response groups at first. The age, bFSH and bFSH/bLH were the highest in the poor ovarian response group (POR), followed by the normal ovarian response group (NOR) and the lowest in the high ovarian response group (HOR). The area under the ROC curve was 0.669 according to the predictive model of pregnancy-related factors. The confidence interval of 94% was 0.629–0.697, with statistical significance (P = 0.000, P < 0.01). Conclusion: it can be concluded that in clinical pregnancy, for many related factors, regression equation can be used to establish a prediction model to diagnose the success rate of pregnancy. In conclusion, a prediction model can be built based on the relevant experimental results, to provide experimental reference ideas for increasing the success rate of ART in late clinical pregnancy, which is of great research significance. |
format | Online Article Text |
id | pubmed-7105670 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-71056702020-03-31 Establishment of predictive model for analyzing clinical pregnancy outcome based on IVF-ET and ICSI assisted reproductive technology Jiang, Songwei Li, Liuming Li, Feiwen Li, Mujun Saudi J Biol Sci Article In order to explore the predictive model for analyzing clinical pregnancy outcomes based on IVF-ET (in vitro fertilization and embryo transfer) and ICSI (Intracytoplasmic sperm injection) assisted reproductive technology (ART). Methods: this study selected the embryo transfer (fresh) patients who received IVF-ET or ICSI treatment in the First Affiliated Hospital of Guangxi Medical University as the subjects. Moreover, the controlled ovarian stimulation (COS) and follow-up were conducted to collect relevant data for analysis, and finally a prediction model was established. Results: The results showed that the patients were divided into different ovarian response groups at first. The age, bFSH and bFSH/bLH were the highest in the poor ovarian response group (POR), followed by the normal ovarian response group (NOR) and the lowest in the high ovarian response group (HOR). The area under the ROC curve was 0.669 according to the predictive model of pregnancy-related factors. The confidence interval of 94% was 0.629–0.697, with statistical significance (P = 0.000, P < 0.01). Conclusion: it can be concluded that in clinical pregnancy, for many related factors, regression equation can be used to establish a prediction model to diagnose the success rate of pregnancy. In conclusion, a prediction model can be built based on the relevant experimental results, to provide experimental reference ideas for increasing the success rate of ART in late clinical pregnancy, which is of great research significance. Elsevier 2020-04 2020-03-04 /pmc/articles/PMC7105670/ /pubmed/32256165 http://dx.doi.org/10.1016/j.sjbs.2020.02.021 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Jiang, Songwei Li, Liuming Li, Feiwen Li, Mujun Establishment of predictive model for analyzing clinical pregnancy outcome based on IVF-ET and ICSI assisted reproductive technology |
title | Establishment of predictive model for analyzing clinical pregnancy outcome based on IVF-ET and ICSI assisted reproductive technology |
title_full | Establishment of predictive model for analyzing clinical pregnancy outcome based on IVF-ET and ICSI assisted reproductive technology |
title_fullStr | Establishment of predictive model for analyzing clinical pregnancy outcome based on IVF-ET and ICSI assisted reproductive technology |
title_full_unstemmed | Establishment of predictive model for analyzing clinical pregnancy outcome based on IVF-ET and ICSI assisted reproductive technology |
title_short | Establishment of predictive model for analyzing clinical pregnancy outcome based on IVF-ET and ICSI assisted reproductive technology |
title_sort | establishment of predictive model for analyzing clinical pregnancy outcome based on ivf-et and icsi assisted reproductive technology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7105670/ https://www.ncbi.nlm.nih.gov/pubmed/32256165 http://dx.doi.org/10.1016/j.sjbs.2020.02.021 |
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