A visualized clinical model predicting good quality blastocyst development in the first IVF/ICSI cycle. Issue 5 (November 2020)

• Record Type: Journal Article
• Authors: Xiong, Feng; Wang, Sisi; Sun, Qing; Ye, Lijun; Yao, Zhihong; Chen, Peilin; Wan, Caiyun; Zhong, Huixian; Zeng, Yong
• Is Part Of: Reproductive biomedicine online. Volume 41:Issue 5(2020)
• Page Start: 807
• Page End: 817
• Publication Date: 2020-11
• Subjects: Extended culture -- Good quality blastocyst -- LASSO -- Nomogram -- Prediction model; Human reproductive technology -- Periodicals; Human embryo -- Periodicals; Reproduction -- Periodicals; 616.692
• DOI: 10.1016/j.rbmo.2020.07.018
• Languages: English
• ISSNs: 1472-6483

Abstract

Abstract: Research question: Is it possible to establish a visualized clinical model predicting good quality blastocyst (GQB) formation for patients in their first IVF/intracytoplasmic sperm injection (ICSI) cycle? Design: A total of 4783 patients in their first IVF/ICSI cycle between January 2015 and December 2019 were retrospectively included and randomly divided into the training set ( n = 3826) and the testing set ( n = 957) in an 8:2 ratio. The least absolute shrinkage and selection operator (LASSO) regression was adopted to select the most critical predictors for GQB formation to construct a visualized nomogram model based on the data of patients in the training set. Receiver operating characteristic and calibration curves were used to evaluate the predictive accuracy and discriminative ability. The performance of the model was also validated on independent data from patients treated in the testing set. Results: Maternal age, maternal serum anti-Müllerian hormone (MsAMH) concentration and the number of oocytes retrieved were highlighted as critical predictors of GQB development and were incorporated into the nomogram model. Based on the area under the curve (AUC) values, the predictive ability for ≥1, ≥3 and ≥5 GQB were 0.831, 0.734 and 0.748, respectively. The calibration curve also showed high concordance between the observed and predicted results. The AUC for predicting ≥1, ≥3 and ≥5 GQB in the testing set were 0.805, 0.695 and 0.707, respectively, which were similar to those for the training set. Conclusions: The visualized nomogram model provides great predictive value for GQB development in patients in their first IVF/ICSI cycle and can be used to improve clinical counselling.