Influencing factors of fertilization failure during

Objective: To identify independent risk factors for fertilization failure (FF) during conventional Methods: 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). Results: 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%). Conclusion: 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.