Objectives: To establish visualised prediction models of low fertilisation rate (LFR) and total fertilisation failure (TFF) for patients in conventional in vitro fertilisation (IVF) cycles.
Design: A retrospective cohort study.
Setting: Data from August 2017 to August 2021 were collected from the electronic records of a large obstetrics and gynaecology hospital in Sichuan, China.
Participants: A total of 11 598 eligible patients who underwent the first IVF cycles were included. All patients were randomly divided into the training group (n=8129) and the validation group (n=3469) in a 7:3 ratio.
Primary outcome measure: The incidence of LFR and TFF.
Results: Logistic regressions showed that ovarian stimulation protocol, primary infertility and initial progressive sperm motility were the independent predictors of LFR, while serum luteinising hormone and P levels before human chorionic gonadotropin injection and number of oocytes retrieved were the critical predictors of TFF. And these indicators were incorporated into the nomogram models. According to the area under the curve values, the predictive ability for LFR and TFF were 0.640 and 0.899 in the training set and 0.661 and 0.876 in the validation set, respectively. The calibration curves also showed good concordance between the actual and predicted probabilities both in the training and validation group.
Conclusion: The novel nomogram models provided effective methods for clinicians to predict LFR and TFF in traditional IVF cycles.
Keywords: epidemiology; reproductive medicine; subfertility.
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