Development and validation of a clinical prediction model of fertilization failure during routine IVF cycles
PurposeThis study aims to create and validate a clinical model that predict the probability of fertilization failure in routine in-vitro fertilization (IVF) cycles.MethodsThis study employed a retrospective methodology, gathering data from 1770 couples that used reproductive center’s of the Fourth H...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2023.1331640/full |
| _version_ | 1797351534004011008 |
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| author | Liu Xingnan Zhang Na |
| author_facet | Liu Xingnan Zhang Na |
| author_sort | Liu Xingnan |
| collection | DOAJ |
| description | PurposeThis study aims to create and validate a clinical model that predict the probability of fertilization failure in routine in-vitro fertilization (IVF) cycles.MethodsThis study employed a retrospective methodology, gathering data from 1770 couples that used reproductive center’s of the Fourth Hospital of Hebei Medical University standard IVF fertilization between June 2015 and June 2023. 1062 were in the training set and 708 were in the validation set when it was randomly split into the training set and validation set in a 6:4 ratio. The study employed both univariate and multivariate logistic regression analysis to determine the factors those influence the failure of traditional in vitro fertilization. Based on the multiple regression model, a predictive model of traditional IVF fertilization failure was created. The calibration and decision curves were used to assess the effectiveness and therapeutic usefulness of this model.ResultsThe following factors independently predicted the probability of an unsuccessful fertilization: infertility years, basal oestrogen, the rate of mature oocytes, oligoasthenozoospermia, sperm concentration, sperm vitality, percentage of abnormal morphological sperm, and percentage of progressive motility (PR%).The receiver operating characteristic curve’s area under the curve (AUC) in the training set is 0.776 (95% CI: 0.740,0.812), while the validation set’s AUC is 0.756 (95% CI: 0.708,0.805), indicating a rather high clinical prediction capacity.ConclusionOur generated nomogram has the ability to forecast the probability of fertilization failure in couples undergoing IVF, hence can assist clinical staff in making informed decisions. |
| first_indexed | 2024-03-08T13:01:52Z |
| format | Article |
| id | doaj.art-02b2f010aea94516a14934c81d97b9bf |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-03-08T13:01:52Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-02b2f010aea94516a14934c81d97b9bf2024-01-19T04:43:57ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922024-01-011410.3389/fendo.2023.13316401331640Development and validation of a clinical prediction model of fertilization failure during routine IVF cyclesLiu XingnanZhang NaPurposeThis study aims to create and validate a clinical model that predict the probability of fertilization failure in routine in-vitro fertilization (IVF) cycles.MethodsThis study employed a retrospective methodology, gathering data from 1770 couples that used reproductive center’s of the Fourth Hospital of Hebei Medical University standard IVF fertilization between June 2015 and June 2023. 1062 were in the training set and 708 were in the validation set when it was randomly split into the training set and validation set in a 6:4 ratio. The study employed both univariate and multivariate logistic regression analysis to determine the factors those influence the failure of traditional in vitro fertilization. Based on the multiple regression model, a predictive model of traditional IVF fertilization failure was created. The calibration and decision curves were used to assess the effectiveness and therapeutic usefulness of this model.ResultsThe following factors independently predicted the probability of an unsuccessful fertilization: infertility years, basal oestrogen, the rate of mature oocytes, oligoasthenozoospermia, sperm concentration, sperm vitality, percentage of abnormal morphological sperm, and percentage of progressive motility (PR%).The receiver operating characteristic curve’s area under the curve (AUC) in the training set is 0.776 (95% CI: 0.740,0.812), while the validation set’s AUC is 0.756 (95% CI: 0.708,0.805), indicating a rather high clinical prediction capacity.ConclusionOur generated nomogram has the ability to forecast the probability of fertilization failure in couples undergoing IVF, hence can assist clinical staff in making informed decisions.https://www.frontiersin.org/articles/10.3389/fendo.2023.1331640/fullin vitro fertilizationrescue ICSIclinical prediction modelfertilization failurenomogram |
| spellingShingle | Liu Xingnan Zhang Na Development and validation of a clinical prediction model of fertilization failure during routine IVF cycles Frontiers in Endocrinology in vitro fertilization rescue ICSI clinical prediction model fertilization failure nomogram |
| title | Development and validation of a clinical prediction model of fertilization failure during routine IVF cycles |
| title_full | Development and validation of a clinical prediction model of fertilization failure during routine IVF cycles |
| title_fullStr | Development and validation of a clinical prediction model of fertilization failure during routine IVF cycles |
| title_full_unstemmed | Development and validation of a clinical prediction model of fertilization failure during routine IVF cycles |
| title_short | Development and validation of a clinical prediction model of fertilization failure during routine IVF cycles |
| title_sort | development and validation of a clinical prediction model of fertilization failure during routine ivf cycles |
| topic | in vitro fertilization rescue ICSI clinical prediction model fertilization failure nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2023.1331640/full |
| work_keys_str_mv | AT liuxingnan developmentandvalidationofaclinicalpredictionmodeloffertilizationfailureduringroutineivfcycles AT zhangna developmentandvalidationofaclinicalpredictionmodeloffertilizationfailureduringroutineivfcycles |