Early prediction of live birth for assisted reproductive technology patients: a convenient and practical prediction model
Abstract Live birth is the most important concern for assisted reproductive technology (ART) patients. Therefore, in the medical reproductive centre, obstetricians often need to answer the following question: “What are the chances that I will have a healthy baby after ART treatment?” To date, our ob...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-020-79308-9 |
| _version_ | 1818734088484290560 |
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| author | Hong Gao Dong-e Liu Yumei Li Xinrui Wu Hongzhuan Tan |
| author_facet | Hong Gao Dong-e Liu Yumei Li Xinrui Wu Hongzhuan Tan |
| author_sort | Hong Gao |
| collection | DOAJ |
| description | Abstract Live birth is the most important concern for assisted reproductive technology (ART) patients. Therefore, in the medical reproductive centre, obstetricians often need to answer the following question: “What are the chances that I will have a healthy baby after ART treatment?” To date, our obstetricians have no reference on which to base the answer to this question. Our research aimed to solve this problem by establishing prediction models of live birth for ART patients. Between January 1, 2010, and May 1, 2017, we conducted a retrospective cohort study of women undergoing ART treatment at the Reproductive Medicine Centre, Xiangya Hospital of Central South University, Hunan, China. The birth of at least one live-born baby per initiated cycle or embryo transfer procedure was defined as a live birth, and all other pregnancy outcomes were classified as no live birth. A live birth prediction model was established by stepwise multivariate logistic regression. All eligible subjects were randomly allocated to two groups: group 1 (80% of subjects) for the establishment of the prediction models and group 2 (20% of subjects) for the validation of the established prediction models. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of each prediction model at different cut-off values were calculated. The prediction model of live birth included nine variables. The area under the ROC curve was 0.743 in the validation group. The sensitivity, specificity, PPV, and NPV of the established model ranged from 97.9–24.8%, 7.2–96.3%, 44.8–83.8% and 81.7–62.5%, respectively, at different cut-off values. A stable, reliable, convenient, and satisfactory prediction model for live birth by ART patients was established and validated, and this model could be a useful tool for obstetricians to predict the live rate of ART patients. Meanwhile, it is also a reference for obstetricians to create good conditions for infertility patients in preparation for pregnancy. |
| first_indexed | 2024-12-17T23:59:48Z |
| format | Article |
| id | doaj.art-3c540f7b33714cc6a198c97d12561453 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-17T23:59:48Z |
| publishDate | 2021-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-3c540f7b33714cc6a198c97d125614532022-12-21T21:27:59ZengNature PortfolioScientific Reports2045-23222021-01-011111710.1038/s41598-020-79308-9Early prediction of live birth for assisted reproductive technology patients: a convenient and practical prediction modelHong Gao0Dong-e Liu1Yumei Li2Xinrui Wu3Hongzhuan Tan4School of Nursing, University of South ChinaReproductive Medicine Centre, Xiangya Hospital of Central South UniversityReproductive Medicine Centre, Xiangya Hospital of Central South UniversityDepartment of Epidemiology and Health Statistics, XiangYa School of Public Health, Central South UniversityDepartment of Epidemiology and Health Statistics, XiangYa School of Public Health, Central South UniversityAbstract Live birth is the most important concern for assisted reproductive technology (ART) patients. Therefore, in the medical reproductive centre, obstetricians often need to answer the following question: “What are the chances that I will have a healthy baby after ART treatment?” To date, our obstetricians have no reference on which to base the answer to this question. Our research aimed to solve this problem by establishing prediction models of live birth for ART patients. Between January 1, 2010, and May 1, 2017, we conducted a retrospective cohort study of women undergoing ART treatment at the Reproductive Medicine Centre, Xiangya Hospital of Central South University, Hunan, China. The birth of at least one live-born baby per initiated cycle or embryo transfer procedure was defined as a live birth, and all other pregnancy outcomes were classified as no live birth. A live birth prediction model was established by stepwise multivariate logistic regression. All eligible subjects were randomly allocated to two groups: group 1 (80% of subjects) for the establishment of the prediction models and group 2 (20% of subjects) for the validation of the established prediction models. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of each prediction model at different cut-off values were calculated. The prediction model of live birth included nine variables. The area under the ROC curve was 0.743 in the validation group. The sensitivity, specificity, PPV, and NPV of the established model ranged from 97.9–24.8%, 7.2–96.3%, 44.8–83.8% and 81.7–62.5%, respectively, at different cut-off values. A stable, reliable, convenient, and satisfactory prediction model for live birth by ART patients was established and validated, and this model could be a useful tool for obstetricians to predict the live rate of ART patients. Meanwhile, it is also a reference for obstetricians to create good conditions for infertility patients in preparation for pregnancy.https://doi.org/10.1038/s41598-020-79308-9 |
| spellingShingle | Hong Gao Dong-e Liu Yumei Li Xinrui Wu Hongzhuan Tan Early prediction of live birth for assisted reproductive technology patients: a convenient and practical prediction model Scientific Reports |
| title | Early prediction of live birth for assisted reproductive technology patients: a convenient and practical prediction model |
| title_full | Early prediction of live birth for assisted reproductive technology patients: a convenient and practical prediction model |
| title_fullStr | Early prediction of live birth for assisted reproductive technology patients: a convenient and practical prediction model |
| title_full_unstemmed | Early prediction of live birth for assisted reproductive technology patients: a convenient and practical prediction model |
| title_short | Early prediction of live birth for assisted reproductive technology patients: a convenient and practical prediction model |
| title_sort | early prediction of live birth for assisted reproductive technology patients a convenient and practical prediction model |
| url | https://doi.org/10.1038/s41598-020-79308-9 |
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