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dc.contributor.authorXu, Tingtingen_US
dc.contributor.authorde Figueiredo Veiga, Alexisen_US
dc.contributor.authorHammer, Karissa C.en_US
dc.contributor.authorPaschalidis, Ioannis C.en_US
dc.contributor.authorMahalingaiah, Shruthien_US
dc.date2021-12-28
dc.date.accessioned2022-05-12T18:59:26Z
dc.date.available2022-05-12T18:59:26Z
dc.date.issued2022-01-17
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/35039614
dc.identifier.citationT. Xu, A. de Figueiredo Veiga, K.C. Hammer, I.C. Paschalidis, S. Mahalingaiah. 2022. "Informative predictors of pregnancy after first IVF cycle using eIVF practice highway electronic health records." Sci Rep, Volume 12, Issue 1: 839. https://doi.org/10.1038/s41598-022-04814-x
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/2144/44403
dc.description.abstractThe aim of this study is to determine the most informative pre- and in-cycle variables for predicting success for a first autologous oocyte in-vitro fertilization (IVF) cycle. This is a retrospective study using 22,413 first autologous oocyte IVF cycles from 2001 to 2018. Models were developed to predict pregnancy following an IVF cycle with a fresh embryo transfer. The importance of each variable was determined by its coefficient in a logistic regression model and the prediction accuracy based on different variable sets was reported. The area under the receiver operating characteristic curve (AUC) on a validation patient cohort was the metric for prediction accuracy. Three factors were found to be of importance when predicting IVF success: age in three groups (38-40, 41-42, and above 42 years old), number of transferred embryos, and number of cryopreserved embryos. For predicting first-cycle IVF pregnancy using all available variables, the predictive model achieved an AUC of 68% + /- 0.01%. A parsimonious predictive model utilizing age (38-40, 41-42, and above 42 years old), number of transferred embryos, and number of cryopreserved embryos achieved an AUC of 65% + /- 0.01%. The proposed models accurately predict a single IVF cycle pregnancy outcome and identify important predictive variables associated with the outcome. These models are limited to predicting pregnancy immediately after the IVF cycle and not live birth. These models do not include indicators of multiple gestation and are not intended for clinical application.en_US
dc.description.sponsorshipIIS-1914792 - National Science Foundation; N00014-19-1-2571 - Office of Naval Research Global; GM135930 - Office of Extramural Research, National Institutes of Healthen_US
dc.languageeng
dc.language.isoen_US
dc.relation.ispartofSci Rep
dc.rights© The Author(s) 2022. 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleInformative predictors of pregnancy after first IVF cycle using eIVF practice highway electronic health recordsen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.1038/s41598-022-04814-x
pubs.elements-sourcemanual-entryen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Engineeringen_US
pubs.organisational-groupBoston University, College of Engineering, Department of Electrical & Computer Engineeringen_US
pubs.publication-statusPublisheden_US
dc.date.online2021-12-31
dc.identifier.mycv706807


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© The Author(s) 2022. 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
Except where otherwise noted, this item's license is described as © The Author(s) 2022. 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.