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A Diagnostic Model to Improve the Predictability of Natural Pregnancy Potential in Patients with Oligoasthenospermia

Tiancheng Zhang, Xin Wang, Zhikai Wang, Zhiming Xu, Liang Chen, Maohua Miao, Bin Wu, Xuemei Wang, Xiaorong Shen, Jun Wu, Ke Wang, Huijuan Shi, Jianhui Li, Jufen Zheng

NHC Key Laboratory of Reproduction Regulation (Shanghai Institute of Planned Parenthood Research), Pharmacy School, Fudan University, Shanghai, China (mainland)

Med Sci Monit 2020; 26:e922316

DOI: 10.12659/MSM.922316

Available online: 2020-07-03

Published: 2020-09-05

BACKGROUND: Oligoasthenospermia is one of the major reasons for male infertility in clinical practice. Nevertheless, some patients with oligoasthenospermia show normal fertility. Currently, there is a lack of an effective method to distinguish patients with oligoasthenospermia showing normal fertility from those who lack natural fertility and should participate in in vitro fertilization and assisted reproduction.
MATERIAL AND METHODS: In this study, we collected semen and blood samples from 153 males of Shui nationality at reproductive age in Guizhou Province, southwest China. We measured the routine parameters for semen and some serological indicators. A clinical diagnosis model was then constructed to evaluate the fertility potential of oligoasthenospermia patients using a logistic stepwise regression method, which was then visualized with a nomogram.
RESULTS: Our results showed that this model could effectively assess the natural pregnancy potential of patients with oligoasthenospermia, and its sensitivity and specificity were superior to those of a traditional model that used only sperm motility and count to assess male fertility potential (area under the curve=0.7626 vs. 0.6677). Additionally, we evaluated the clinical net benefit for patients with oligoasthenospermia at different risk scores in our model using decision curve analysis. The results showed that the net benefit was obtained at scores ranging from 0.1 to 0.6.
CONCLUSIONS: This comprehensive clinical prediction model can be used to determine whether infertile oligoasthenospermia patients lack natural fertility.

Keywords: Diagnosis, Oligospermia, Pregnancy