Nomogram models to predict low fertilisation rate and total fertilisation failure in patients undergoing conventional IVF cycles

ObjectivesTo establish visualised prediction models of low fertilisation rate (LFR) and total fertilisation failure (TFF) for patients in conventional in vitro fertilisation (IVF) cycles.DesignA retrospective cohort study.SettingData from August 2017 to August 2021 were collected from the electronic records of a large obstetrics and gynaecology hospital in Sichuan, China.ParticipantsA 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 measureThe incidence of LFR and TFF.ResultsLogistic 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.ConclusionThe novel nomogram models provided effective methods for clinicians to predict LFR and TFF in traditional IVF cycles.

Automated summary

This study established visual prediction models for low fertilisation rate (LFR) and total fertilisation failure (TFF) in conventional IVF cycles. Logistic regressions identified the independent predictors for LFR and TFF, which were then incorporated into the nomogram models. The models showed good predictive ability and calibration in both the training and validation sets.