Researchers built a simple calculator that uses six lab measurements (like progesterone levels and kidney function) to guess the chance of having a baby after a fresh IVF cycle that uses a GnRH‑antagonist drug regimen. The tool worked fairly well in the data they studied, especially when they tested it on a separate group.

This investigation sought to determine optimal prognostic indicators and develop an implementable predictive framework for estimating live birth probabilities in subfertile individuals receiving gonadotropin-releasing hormone antagonist-based ovarian stimulation during fresh embryo transfer cycles of assisted reproductive technology. In this observational cohort analysis, we examined consecutive fresh in vitro fertilization/embryo transfer (IVF/ET) cycles utilizing GnRH antagonist protocols (training = 587, validation = 168 cycles; 2017-2022). Live birth rate served as the principal outcome measure. Through multivariable regression modeling, we identified key predictive variables and constructed a visual prediction tool. Model robustness was assessed using ROC-AUC metrics and decision curve validation with 500 bootstrap iterations. The final predictive algorithm incorporated six clinical parameters: serum progesterone on post-ovulation day 9 (serum P (OPU+9) ≥ 51.4 ng/mL), transferred embryo count, progesterone-follicle ratio (PFR), triggering-day progesterone levels, progesterone-to-total follicle ratio, and creatinine concentrations. The training cohort demonstrated moderate discriminative capacity (ROC-AUC 0.72, 95% CI 0.68-0.76), with enhanced performance in validation samples (AUC 0.81, 95% CI 0.73-0.89). Decision curve evaluations confirmed the model's clinical applicability. Our prognostic scoring chart offers an accessible and practical clinical instrument for estimating reproductive success in GnRH antagonist-based IVF/ICSI cycles. This tool facilitates personalized treatment planning and therapeutic strategy optimization, potentially improving resource allocation in fertility care.