Zhongqiang Liu

Work place: School of Mathematics and Information Science, Henan Polytechnic University, Jiaozuo, 454000, China

E-mail: zhongqiang@hpu.edu.cn

Website: https://orcid.org/0000-0002-1060-8154

Research Interests: Mathematics, Statistics, Mathematics of Computing


Zhongqiang Liu is an associate professor at the School of Mathematics and Information Science of Henan Polytechnic University, a doctor of Statistics from Renmin University of China, a postdoctoral fellow and master supervisor in Biostatistics of Zhejiang University. He presided over one Youth Foundation project of the National Natural Science Foundation of China and one Post-doctoral Science Foundation project of China. Participated in the National Natural Science Foundation project, the Ministry of Education Humanities and Social Science Statistics project and the National Bureau of Statistics project. His research interest is biostatistics.

Author Articles
An Empirical Predictive Model for Formation Rate of the Day 5 Blastocyst

By Xi Wang Zhongqiang Liu

DOI: https://doi.org/10.5815/ijmsc.2023.02.03, Pub. Date: 8 May 2023

Day 5 (D5) blastocyst transfers present higher clinical pregnancy and live birth rates than day 6 in both fresh and frozen transfers [1]. To investigate the D5 blastocyst formation rate, in this study, we first collected clinical data from a hospital in Jiaozuo and partitioned the data into training set and validation set. We conducted univariate logistic regression analyses, which were possible predictors of the D5 blastocyst formation rate, on 12 patient covariates. According to the univariate analysis, we determined 10 covariates were suitable for multivariate analysis. Finally, we identified five covariates to construct a logistic regression model to predict the D5 blastocyst formation rate. We also used the receiver operating characteristic curve, the Hosmer–Lemeshow test, and the calibration curve to verify the accuracy of this model. The results showed that logistic regression model of D5 blastocyst formation rate directly reflected the relationship between transplantation results and covariates. According to the model, doctors can provide guidance to patients before treatment and improve the rate of blastocyst formation by changing patients' physical fitness. The model has certain clinical application value.

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