Exploring determinants of cumulative live birth rates in IVF: insights from the EELI study in Lebanon

Abstract Background Infertility affects a significant proportion of couples worldwide, yet data from low- and middle-income countries, including Lebanon, remain limited. Cumulative live birth rates (CLBR) are a key indicator of assisted reproductive technology (ART) success. This study aimed to assess the demographic and clinical predictors of CLBR among Lebanese patients undergoing ART, focusing on maternal age, oocyte yield, embryo transfer stage and strategy. Methods Anonymous medical record of 1,243 patients undergoing their first ART cycles at a fertility center in Beirut, Lebanon, between June 16, 2020 and June 30, 2024 was retrieved. Data on demographic characteristics, infertility indications, ovarian stimulation outcomes, and transfer strategy (first fresh cycles vs. freeze-all) were collected. CLBRs were calculated using both conservative and optimistic approaches across up to four embryo transfers. Logistic regression models were applied to identify predictors of live birth. Results The overall live birth rate was 46.3%, with higher CLBRs observed in patients undergoing freeze-all compared to First Fresh cycles (51.4% vs. 41.7% by the fourth transfer). Maternal age was inversely associated with success, with women ≥ 35 years showing significantly reduced odds of live birth. Oocyte yield was a strong predictor, with > 15 oocytes retrieved resulting in a CLBR exceeding 60%. Blastocyst-stage (Day 5) transfers were associated with markedly higher odds of live birth compared to cleavage-stage (Day 3) transfers (aOR = 3.89, p < 0.001). Conclusions This is the first comprehensive study of its kind in Lebanon, demonstrating that ART outcomes are significantly influenced by maternal age, oocyte yield, and embryo transfer stage. Despite the country’s recent political, economic, and health crises, ART success rates remain promising. Future studies should explore additional variables, such as environmental exposures and dietary patterns, to enhance CLBR predictive models for In vitro-fertilization (IVF) success ultimately improving patient counseling and personalized care.