Prediction of Reproductive Outcomes of Intracytoplasmic Sperm Injection Cycles Using a Multivariate Scoring System

Prediction of in vitro fertilisation (IVF)/intracytoplasmic sperm injection (ICSI) success is crucial in counselling patients about their real chance of getting a live birth before commencing treatment. A multivariate scoring system proposed by Younis et al., 2010, was amongst the predictive models used to evaluate IVF/ICSI success. The score entitles basal endocrine, clinical and sonographic parameters.

The objective of this study is to assess the predictability of the Younis multivariate score for pregnancy outcomes in ICSI cycles.

This prospective observational cohort study (NCT03846388) included patients who pursued IVF or ICSI in a tertiary infertility unit between February 2019 and December 2021.

The score variables were age, body mass index, antral follicle count, basal follicle-stimulating hormone (FSH), basal FSH/luteinising hormone ratio, infertility duration, number of previous cancellations and mean ovarian volume. For each woman included in the study, Younis multivariate score was calculated. Then, we correlate the different reproductive outcomes with score levels to validate the score predictability. A score of ≤14 was defined as a low score based on the previous study's results.

The student's t-test and Mann-Whitney test were used to compare numerical variables, whereas categorical variables were analysed using the Chi-square test. A receiver operating curve (ROC) and a multivariate logistic regression model were used to investigate the predictability of the Younis scoring model for cycle outcomes.

Two hundred ninety-two ICSI-ET cycles were analysed. Of the total cohort, 143 (48.97%) women included showed a low score (≤14), whereas 149 (51.03%) women showed a high score (>14). Women with low scores had significantly higher pregnancy and live birth rates compared to women with high scores (60.1% vs. 7.4%, respectively, P < 0.001; 44.7% vs. 6.7%, respectively, P < 0.001). The area under the curve (AUC) in the ROC curve analysis showed a higher predictability for the scoring system for live birth rate with an AUC of 0.796, with a sensitivity of 86.5% and specificity of 63.8% when using a cut-off level of ≤14. For pregnancy prediction, the AUC was 0.829, with a sensitivity of 88.66% and a specificity of 70.77% when using the same cut-off. Women who have a low score have a high chance of having frozen embryos. Likewise, women who have a high score have a very high chance of cycle cancellation.

The Younis multivariate score can be used for the prediction of ICSI cycle outcomes and to calculate the chance of cycle cancellation, pregnancy and take-home baby before ICSI.