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Jiang X, Cai J, Liu L, Liu Z, Wang W, Chen J, Yang C, Geng J, Ma C, Ren J. Does conventional morphological evaluation still play a role in predicting blastocyst formation? Reprod Biol Endocrinol 2022; 20:68. [PMID: 35439999 PMCID: PMC9016972 DOI: 10.1186/s12958-022-00945-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 04/09/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Advanced models including time-lapse imaging and artificial intelligence technologies have been used to predict blastocyst formation. However, the conventional morphological evaluation of embryos is still widely used. The purpose of the present study was to evaluate the predictive power of conventional morphological evaluation regarding blastocyst formation. METHODS Retrospective evaluation of data from 15,613 patients receiving blastocyst culture from January 2013 through December 2020 in our institution were reviewed. Generalized estimating equations (GEE) were used to establish the morphology-based model. To estimate whether including more features regarding patient characteristics and cycle parameters improve the predicting power, we also establish models including 27 more features with either LASSO regression or XGbosst. The predicted number of blastocyst were associated with the observed number of the blastocyst and were used to predict the blastocyst transfer cancellation either in fresh or frozen cycles. RESULTS Based on early cleavage and routine observed morphological parameters (cell number, fragmentation, and symmetry), the GEE model predicted blastocyst formation with an AUC of 0.779(95%CI: 0.77-0.787) and an accuracy of 74.7%(95%CI: 73.9%-75.5%) in the validation set. LASSO regression model and XGboost model based on the combination of cycle characteristics and embryo morphology yielded similar predicting power with AUCs of 0.78(95%CI: 0.771-0.789) and 0.754(95%CI: 0.745-0.763), respectively. For per-cycle blastocyst yield, the predicted number of blastocysts using morphological parameters alone strongly correlated with observed blastocyst number (r = 0.897, P < 0.0001) and predicted blastocyst transfer cancel with an AUC of 0.926((95%CI: 0.911-0.94). CONCLUSION The data suggested that routine morphology observation remained a feasible tool to support an informed decision regarding the day of transfer. However, models based on the combination of cycle characteristics and embryo morphology do not increase the predicting power significantly.
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Affiliation(s)
- Xiaoming Jiang
- Reproductive Medicine Center, Xiamen University Affiliated Chenggong Hospital, Xiamen, 361003, Fujian, China.
| | - Jiali Cai
- Reproductive Medicine Center, Xiamen University Affiliated Chenggong Hospital, Xiamen, 361003, Fujian, China
- School of Medicine, Xiamen University, Xiamen, 361005, Fujian, China
| | - Lanlan Liu
- Reproductive Medicine Center, Xiamen University Affiliated Chenggong Hospital, Xiamen, 361003, Fujian, China
- School of Medicine, Xiamen University, Xiamen, 361005, Fujian, China
| | - Zhenfang Liu
- Reproductive Medicine Center, Xiamen University Affiliated Chenggong Hospital, Xiamen, 361003, Fujian, China
| | - Wenjie Wang
- Reproductive Medicine Center, Xiamen University Affiliated Chenggong Hospital, Xiamen, 361003, Fujian, China
| | - Jinhua Chen
- Reproductive Medicine Center, Xiamen University Affiliated Chenggong Hospital, Xiamen, 361003, Fujian, China
| | - Chao Yang
- Reproductive Medicine Center, Xiamen University Affiliated Chenggong Hospital, Xiamen, 361003, Fujian, China
| | - Jie Geng
- Reproductive Medicine Center, Xiamen University Affiliated Chenggong Hospital, Xiamen, 361003, Fujian, China
| | - Caihui Ma
- Reproductive Medicine Center, Xiamen University Affiliated Chenggong Hospital, Xiamen, 361003, Fujian, China
| | - Jianzhi Ren
- Reproductive Medicine Center, Xiamen University Affiliated Chenggong Hospital, Xiamen, 361003, Fujian, China
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