Key Finding
Three validated prediction models for IVF cumulative live birth rate achieved discrimination accuracy of 74-83%, with the post-treatment model showing the highest predictive value (C-index 0.8270) when incorporating age, oocyte yield, and embryo quality data.
Researchers in China have developed online calculators to help predict the chances of having a baby through IVF (in vitro fertilization) treatment. The study analyzed 32,306 IVF cycles from nearly 30,000 couples treated between 2014 and 2020 at a university fertility center. The team created three prediction models that can estimate a woman's cumulative live birth rate—the likelihood of having a baby after completing all stages of one IVF cycle, including fresh embryo transfer and any frozen embryo transfers. The first model uses information available before treatment begins, such as a woman's age, egg reserve (antral follicle count), body weight, and previous IVF history. The second model adds information collected after ovarian stimulation, including the number of eggs retrieved and hormone levels. The third and most accurate model incorporates all available information after treatment is complete, including embryo quality. The models showed good predictive ability, with accuracy ranging from 74% to 83%. Women's age and the number of eggs retrieved were particularly important factors, showing complex non-linear relationships with success rates. While this study focused on conventional IVF treatment and does not directly address acupuncture, many patients undergoing fertility treatment seek complementary therapies. These prediction tools can help set realistic expectations during the emotionally and financially demanding IVF journey. Patients interested in acupuncture as complementary support during fertility treatment should consult with a licensed acupuncturist experienced in reproductive health.
This retrospective cohort study of 32,306 complete IVF cycles from 29,023 couples (2014-2020) developed three validated prediction models for cumulative live birth rate (CLBR) at different treatment phases. The pre-treatment model (C-index 0.7559) incorporated female age, antral follicle count (AFC), BMI, previous IVF attempts, transfer failures, infertility type, and pathology factors. The post-stimulation model (C-index 0.7744) added oocyte yield, stimulation protocol, and trigger-day parameters. The post-treatment model achieved highest discrimination (C-index 0.8270) by incorporating cumulative Day-3 embryo live-birth capacity. Non-linear relationships were modeled using restricted cubic splines for age, AFC, and oocyte number. Internal validation confirmed discrimination (C-indices 0.7422-0.8234), though temporal validation revealed calibration concerns, possibly reflecting evolving IVF practices. Models are accessible via online calculator. Clinical utility: These tools enable personalized patient counseling and expectation management across treatment phases, with post-treatment predictions offering greatest accuracy for subsequent cycle planning.
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