ESTABLISH A NOMOGRAM MODEL TO PREDICT LIVE BIRTH RATE OF PCOS PATIENTS AFTER IVF/ICSI TREATMENT

Abstract

The clinical outcome after in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) treatment is diverse in infertility patients with polycystic ovary syndrome (PCOS). The aim of this study was to develop a nomogram model based on patients’ characteristics to predict the live birth rate of PCOS patients after IVF/ICSI treatment. Retrospective cohort study. All women in a public university hospital who attempted to conceive by IVF/ICSI for PCOS infertility from January 2014 to October 2018 were included. PCOS was diagnosed according to the Rotterdam criteria. A nomogram was built from a training cohort of 178 consecutive patients and tested on an independent validation cohort of 81 patients. Multivariable logistic regression was used to generate coefficients for each variable and the constant in the equation. The nomogram was constructed to be a graphic representation of the prediction model with the R software. Backward stepwise selection was performed to determine independent covariates. No significant difference was observed in the patients’ characteristics between the two cohorts. 79 patients (44.69%) achieve live birth in the training cohort. According to univariable logistic regression analysis, live birth was significantly correlated with total serum cholesterol (TC) (p = 0.005), BMI (p=0.010) and basal FSH (p=0.080). In multivariable analysis of the training cohort, live birth was significantly correlated with TC > 6.11 ng/ml (odds ratio [OR] 0.209; 95% CI 0.069–0.548; p=0.003), BMI > 23.9 (OR 0.478; 95% CI 0.250–0.900; p=0.023) and basal FSH (OR 1.291; 95% CI 0.990–1.705; p = 0.064). Higher TC, BMI and FSH were associated with a decreased live birth rate. Age and Day of Transfer were not statistically related to the live birth rate but were included in the predictive model due to their clinical relevance. Their inclusion improved the overall quality of the model as well (as measured by the Akaike information criterion).Therefore, this predictive model built on the basis of BMI, TC, basal FSH, Day of Transfer and age showed good calibration and discriminatory abilities, with an area under the curve (AUC) of 0.708 (95% CI 0.632–0.785) for the training cohort. The nomogram showed satisfactory goodness-of-fit and discrimination abilities in the independent validation cohort, with an AUC of 0.686 (95% CI 0.556–0.815). Our simple evidence-based nomogram presents graphically risk factors and prognostic models for IVF/ICSI outcomes in patients with PCOS, which can offer useful guidance to clinicians and patients for individual adjuvant therapy.