Using the embryo-uterus statistical model to predict pregnancy chances by using cleavage stage morphokinetics and female age: two centre-specific prediction models and mutual validation
Abstract Background The predictive capability of time-lapse monitoring (TLM) selection algorithms is influenced by patient characteristics, type and quality of data included in the analysis and the used statistical methods. Previous studies excluded DET cycles of which only one embryo implanted, int...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-03-01
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| Series: | Reproductive Biology and Endocrinology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12958-023-01076-8 |
| _version_ | 1797853559756161024 |
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| author | Eva S. van Marion Esther B. Baart Margarida Santos Linette van Duijn Evert J. P. van Santbrink Régine P. M. Steegers-Theunissen Joop S. E. Laven Marinus J. C. Eijkemans |
| author_facet | Eva S. van Marion Esther B. Baart Margarida Santos Linette van Duijn Evert J. P. van Santbrink Régine P. M. Steegers-Theunissen Joop S. E. Laven Marinus J. C. Eijkemans |
| author_sort | Eva S. van Marion |
| collection | DOAJ |
| description | Abstract Background The predictive capability of time-lapse monitoring (TLM) selection algorithms is influenced by patient characteristics, type and quality of data included in the analysis and the used statistical methods. Previous studies excluded DET cycles of which only one embryo implanted, introducing bias into the data. Therefore, we wanted to develop a TLM prediction model that is able to predict pregnancy chances after both single- and double embryo transfer (SET and DET). Methods This is a retrospective study of couples (n = 1770) undergoing an in vitro fertilization cycle at the Erasmus MC, University Medical Centre Rotterdam (clinic A) or the Reinier de Graaf Hospital (clinic B). This resulted in 2058 transferred embryos with time-lapse and pregnancy outcome information. For each dataset a prediction model was established by using the Embryo-Uterus statistical model with the number of gestational sacs as the outcome variable. This process was followed by cross-validation. Results Prediction model A (based on data of clinic A) included female age, t3-t2 and t5-t4, and model B (clinic B) included female age, t2, t3-t2 and t5-t4. Internal validation showed overfitting of model A (calibration slope 0.765 and area under the curve (AUC) 0.60), and minor overfitting of model B (slope 0.915 and AUC 0.65). External validation showed that model A was capable of predicting pregnancy in the dataset of clinic B with an AUC of 0.65 (95% CI: 0.61–0.69; slope 1.223, 95% CI: 0.903–1.561). Model B was less accurate in predicting pregnancy in the dataset of clinic A (AUC 0.60, 95% CI: 0.56–0.65; slope 0.671, 95% CI: 0.422–0.939). Conclusion Our study demonstrates a novel approach to the development of a TLM prediction model by applying the EU statistical model. With further development and validation in clinical practice, our prediction model approach can aid in embryo selection and decision making for SET or DET. |
| first_indexed | 2024-04-09T19:51:31Z |
| format | Article |
| id | doaj.art-ff9225e5a0f64685a6ebc6ad6ac8427d |
| institution | Directory Open Access Journal |
| issn | 1477-7827 |
| language | English |
| last_indexed | 2024-04-09T19:51:31Z |
| publishDate | 2023-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Reproductive Biology and Endocrinology |
| spelling | doaj.art-ff9225e5a0f64685a6ebc6ad6ac8427d2023-04-03T05:44:45ZengBMCReproductive Biology and Endocrinology1477-78272023-03-0121111210.1186/s12958-023-01076-8Using the embryo-uterus statistical model to predict pregnancy chances by using cleavage stage morphokinetics and female age: two centre-specific prediction models and mutual validationEva S. van Marion0Esther B. Baart1Margarida Santos2Linette van Duijn3Evert J. P. van Santbrink4Régine P. M. Steegers-Theunissen5Joop S. E. Laven6Marinus J. C. Eijkemans7Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynaecology, Erasmus MC, University Medical CentreDivision of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynaecology, Erasmus MC, University Medical CentreFertility Center, Reinier de Graaf HospitalDepartment of Obstetrics and Gynaecology, Erasmus MC, University Medical CentreFertility Center, Reinier de Graaf HospitalDepartment of Obstetrics and Gynaecology, Erasmus MC, University Medical CentreDivision of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynaecology, Erasmus MC, University Medical CentreDepartment of Data Science and Biostatistics, University Medical Centre, Utrecht UniversityAbstract Background The predictive capability of time-lapse monitoring (TLM) selection algorithms is influenced by patient characteristics, type and quality of data included in the analysis and the used statistical methods. Previous studies excluded DET cycles of which only one embryo implanted, introducing bias into the data. Therefore, we wanted to develop a TLM prediction model that is able to predict pregnancy chances after both single- and double embryo transfer (SET and DET). Methods This is a retrospective study of couples (n = 1770) undergoing an in vitro fertilization cycle at the Erasmus MC, University Medical Centre Rotterdam (clinic A) or the Reinier de Graaf Hospital (clinic B). This resulted in 2058 transferred embryos with time-lapse and pregnancy outcome information. For each dataset a prediction model was established by using the Embryo-Uterus statistical model with the number of gestational sacs as the outcome variable. This process was followed by cross-validation. Results Prediction model A (based on data of clinic A) included female age, t3-t2 and t5-t4, and model B (clinic B) included female age, t2, t3-t2 and t5-t4. Internal validation showed overfitting of model A (calibration slope 0.765 and area under the curve (AUC) 0.60), and minor overfitting of model B (slope 0.915 and AUC 0.65). External validation showed that model A was capable of predicting pregnancy in the dataset of clinic B with an AUC of 0.65 (95% CI: 0.61–0.69; slope 1.223, 95% CI: 0.903–1.561). Model B was less accurate in predicting pregnancy in the dataset of clinic A (AUC 0.60, 95% CI: 0.56–0.65; slope 0.671, 95% CI: 0.422–0.939). Conclusion Our study demonstrates a novel approach to the development of a TLM prediction model by applying the EU statistical model. With further development and validation in clinical practice, our prediction model approach can aid in embryo selection and decision making for SET or DET.https://doi.org/10.1186/s12958-023-01076-8Embryo transferIn vitro fertilisationPrediction modelStatistical modelsTime-lapse imaging |
| spellingShingle | Eva S. van Marion Esther B. Baart Margarida Santos Linette van Duijn Evert J. P. van Santbrink Régine P. M. Steegers-Theunissen Joop S. E. Laven Marinus J. C. Eijkemans Using the embryo-uterus statistical model to predict pregnancy chances by using cleavage stage morphokinetics and female age: two centre-specific prediction models and mutual validation Reproductive Biology and Endocrinology Embryo transfer In vitro fertilisation Prediction model Statistical models Time-lapse imaging |
| title | Using the embryo-uterus statistical model to predict pregnancy chances by using cleavage stage morphokinetics and female age: two centre-specific prediction models and mutual validation |
| title_full | Using the embryo-uterus statistical model to predict pregnancy chances by using cleavage stage morphokinetics and female age: two centre-specific prediction models and mutual validation |
| title_fullStr | Using the embryo-uterus statistical model to predict pregnancy chances by using cleavage stage morphokinetics and female age: two centre-specific prediction models and mutual validation |
| title_full_unstemmed | Using the embryo-uterus statistical model to predict pregnancy chances by using cleavage stage morphokinetics and female age: two centre-specific prediction models and mutual validation |
| title_short | Using the embryo-uterus statistical model to predict pregnancy chances by using cleavage stage morphokinetics and female age: two centre-specific prediction models and mutual validation |
| title_sort | using the embryo uterus statistical model to predict pregnancy chances by using cleavage stage morphokinetics and female age two centre specific prediction models and mutual validation |
| topic | Embryo transfer In vitro fertilisation Prediction model Statistical models Time-lapse imaging |
| url | https://doi.org/10.1186/s12958-023-01076-8 |
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