Influencing factors of fertilization failure during in vitro fertilization

To identify independent risk factors for fertilization failure (FF) during conventional in vitro fertilization (IVF) and to develop a predictive model for clinical risk stratification.

This retrospective study included 2,474 IVF cycles (173 FF cycles with fertilization rate <30% and 2,301 normal fertilization cycles with rate ≥30%) at a single center (2018-2023). Univariate and multivariate logistic regression analyses were performed to screen independent risk factors for FF. A combined predictive model was constructed using logistic binary regression, and its discriminative ability was assessed by the area under the ROC curve (AUC).

Multivariate analysis identified four independent risk factors for FF: high proportion of primary infertility (OR = 2.19, 95% CI: 1.48-3.25), low proportion of tubal factor (OR = 0.60, 95% CI: 0.42-0.86), low normal sperm morphology rate (OR = 0.81, 95% CI: 0.73-0.91), and low sperm concentration after treatment (OR = 0.94, 95% CI: 0.91-0.97). The combined predictive model achieved an AUC of 74.6% (95% CI: 70.3%-78.9%), with an optimal cutoff of 0.185 (sensitivity 68.2%, specificity 71.5%).

Primary infertility, absence of tubal factor, low normal sperm morphology, and low post-treatment sperm concentration are independent predictors of FF. This combined prediction model shows moderate discriminative power for fertilization failure risk stratification. However, its low positive predictive value (15.1%) restricts accurate identification of high-risk IVF patients. The model can only be used as an auxiliary counseling tool rather than a definitive reference for choosing conventional IVF or ICSI; prospective multicenter external validation is required before clinical application.