Informative predictors of pregnancy after first IVF cycle using eIVF practice highway electronic health records.

Autor: Xu T; Center for Information and Systems Engineering, Boston University, 8 St. Mary's St, Boston, MA, 02215, USA., de Figueiredo Veiga A; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Building 1 655 Huntington Avenue, Building 1, 14th floor, Boston, MA, 02115, USA., Hammer KC; Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, 55 Fruit Street Yawkey 10, Boston, MA, 02114, USA., Paschalidis IC; Center for Information and Systems Engineering, Boston University, 8 St. Mary's St, Boston, MA, 02215, USA.; Division of Systems Engineering, Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's St, Boston, MA, 02215, USA.; Department of Biomedical Engineering, Faculty of Computing and Data Sciences, Boston University, 8 St. Mary's St, Boston, MA, 02215, USA., Mahalingaiah S; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Building 1 655 Huntington Avenue, Building 1, 14th floor, Boston, MA, 02115, USA. shruthi@hsph.harvard.edu.; Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, 55 Fruit Street Yawkey 10, Boston, MA, 02114, USA. shruthi@hsph.harvard.edu.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Jan 17; Vol. 12 (1), pp. 839. Date of Electronic Publication: 2022 Jan 17.
DOI: 10.1038/s41598-022-04814-x
Abstrakt: The 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.
(© 2022. The Author(s).)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje