Frontiers in endocrinology · 2024
A predictive model for clinical pregnancy following single Day-6 blastocyst transfer in frozen-thawed embryo transfer cycles.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Liu Lidan, Gan Qiuying, Yang Yihua, Liu Bo, Huang Qianyi, Li Mujun
Аннотация
PURPOSE: This study aimed to develop a predictive model for assessing clinical pregnancy probabilities in patients undergoing frozen-thawed cycles with single Day 6 blastocyst transfers.
METHODS: We conducted a two-center retrospective cohort study analyzing 1,381 frozen-thawed single Day 6 blastocyst transfer cycles from June 2016 to December 2022. The primary outcome was the clinical pregnancy rate per cycle. Data were divided into training, testing, and validation groups in a 6:2:2 ratio. Univariate and LASSO regression analyses identified factors influencing clinical pregnancy, which were incorporated into a multiple regression model to predict outcomes. Model performance was assessed in terms of discrimination, calibration, and clinical utility.
RESULTS: Factors independently predicting clinical pregnancy included inner cell mass (ICM) grade, trophectoderm (TE) grade, Day 3 (D3) fragmentation, endometrium thickness, and male age at oocyte pick-up (OPU). The AUC values for the training, testing, and validation sets were 0.66, 0.65, and 0.60, respectively, indicating acceptable performance. Calibration curves demonstrated good predictive accuracy, with slopes of 0.988, 0.871, and 1.263 for the respective groups.
CONCLUSION: The developed nomogram accurately predicts clinical pregnancy probabilities in patients undergoing single Day 6 blastocyst transfers in frozen-thawed cycles, enhancing clinical decision-making by integrating crucial embryological and clinical parameters.
METHODS: We conducted a two-center retrospective cohort study analyzing 1,381 frozen-thawed single Day 6 blastocyst transfer cycles from June 2016 to December 2022. The primary outcome was the clinical pregnancy rate per cycle. Data were divided into training, testing, and validation groups in a 6:2:2 ratio. Univariate and LASSO regression analyses identified factors influencing clinical pregnancy, which were incorporated into a multiple regression model to predict outcomes. Model performance was assessed in terms of discrimination, calibration, and clinical utility.
RESULTS: Factors independently predicting clinical pregnancy included inner cell mass (ICM) grade, trophectoderm (TE) grade, Day 3 (D3) fragmentation, endometrium thickness, and male age at oocyte pick-up (OPU). The AUC values for the training, testing, and validation sets were 0.66, 0.65, and 0.60, respectively, indicating acceptable performance. Calibration curves demonstrated good predictive accuracy, with slopes of 0.988, 0.871, and 1.263 for the respective groups.
CONCLUSION: The developed nomogram accurately predicts clinical pregnancy probabilities in patients undergoing single Day 6 blastocyst transfers in frozen-thawed cycles, enhancing clinical decision-making by integrating crucial embryological and clinical parameters.