Impact of Infertility Etiology on Clinical Pregnancy, Cumulative Live Birth Rate, and Time to Live Birth After IVF/ICSI: A Retrospective Cohort Study

Why It Matters

Understanding the impact of infertility etiology enables clinics to personalize treatment plans, improving success rates and resource allocation in a competitive ART market.

Key Takeaways

• Tubal factor caused 39.6% of infertility cases in cohort
• Ovulatory dysfunction achieved 76.8% cumulative live‑birth rate
• Diminished ovarian reserve showed 37.6% CLBR, longest time to birth
• Median time to live birth was 11 months for ovulatory dysfunction

Pulse Analysis

The recent single‑center analysis of 2,444 couples embarking on their first IVF or ICSI cycle provides robust evidence that the underlying cause of infertility is a decisive predictor of assisted reproductive technology (ART) success. By stratifying patients into nine etiological categories, the researchers could isolate performance differentials that traditional aggregate metrics often mask. This granularity is especially valuable for fertility clinics seeking to benchmark outcomes against industry standards while navigating the nuanced expectations of patients.

Clinically, the data reveal stark contrasts: patients with ovulatory dysfunction not only achieved a 76.8% cumulative live‑birth rate but also reached a live birth in a median of 11 months, positioning them as the most time‑efficient group. In contrast, those diagnosed with diminished ovarian reserve faced a 37.6% live‑birth rate and extended timelines, highlighting the need for more aggressive or adjunctive interventions such as donor gametes or adjunctive ovarian stimulation protocols. The pronounced disparity in first‑cycle clinical pregnancy rates—ranging from 32.9% to 63.7%—further emphasizes that a one‑size‑fits‑all approach to cycle planning may leave high‑risk groups underserved.

From a strategic perspective, these findings reinforce the shift toward etiology‑driven counseling and personalized treatment pathways. Clinics can leverage this evidence to refine patient education, set realistic expectations, and allocate laboratory resources more efficiently. Moreover, insurers and policymakers may consider incorporating etiology‑specific success benchmarks into coverage decisions, potentially improving overall reproductive efficiency and reducing the financial burden of repeated cycles. Future research should explore how emerging technologies, such as AI‑guided embryo selection, interact with specific infertility causes to further optimize live‑birth outcomes.