Predictive value of semen parameters across three sequential processing stages for clinical pregnancy in donor sperm IVF cycles
Year: 2026
Type: preprint
Abstract: Background: Conventional semen parameters exhibit limited predictive value for in vitro fertilization (IVF) outcomes, and their performance across sequential sperm processing stages remains poorly characterized in donor sperm cycles. This study aimed to evaluate whether semen parameters measured at pre-cryopreservation, post-thaw, and post-density gradient centrifugation (DGC) stages can predict clinical pregnancy in IVF cycles using cryopreserved donor sperm. Methods: We conducted a single-center retrospective cohort study at the CITIC Xiangya Sperm Bank, Reproductive & Genetic Hospital of CITIC-Xiangya. A total of 775 first IVF oocyte retrieval cycles using donor sperm (September 2022–January 2025) were included and stratified into clinical pregnancy (n = 357) and non-pregnancy (n = 418) groups. Non-parametric tests, Spearman correlation, univariate and multivariate logistic regression, and receiver operating characteristic (ROC) curve analyses were performed to assess group differences and predictive performance. Results: All semen parameters changed significantly across cryopreservation and DGC (all P < .001). Only post-thaw total motility was slightly higher in pregnant cycles (45.00% vs 44.45%, P = .036) and remained an independent predictor after adjustment (adjusted OR = 1.023, 95% CI 1.004–1.043, P = .020). All individual and combined conventional semen parameters showed poor discriminative capacity for clinical pregnancy, with AUC values ranging from 0.50 to 0.55. Conclusions: Post-thaw total motility can serve as a simple quality control marker for donor sperm allocation. Standard semen metrics across all processing stages lack sufficient power to forecast IVF pregnancy outcomes, indicating that novel sperm functional biomarkers are required in clinical practice.
Source: Research Square
Language: English
Subfield: Reproductive Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7225616917