A simple multivariate score could predict ovarian reserve, as well as pregnancy rate, in infertile women

Objective: To find a simple multivariate score that has the potential to predict ovarian reserve, as well as pregnancy rate, in infertile women. Design: A prospective study. Setting: A university-affiliated reproductive medicine unit. Patient(s): One hundred sixty-eight consecutive women undergoing their first IVF-ET treatment at our unit. Intervention(s): Basal ovarian reserve studies, endocrine and sonographic, were performed before starting therapy. After completion of treatment, a logistic regression analysis was performed to examine which parameters significantly determined low ovarian reserve. These parameters were incorporated thereafter in a multivariate score to predict ovarian reserve, as well as clinical pregnancy rate. Main Outcome Measure(s): Low ovarian reserve defined as ≤3 oocytes on retrieval day and clinical implantation and pregnancy rates. Result(s): Logistic regression analysis revealed that age, antral follicle count, basal FSH, FSH/LH ratio, mean ovarian volume, infertility duration, number of previous cycle cancellations, and body mass index were all, in decreasing significance, independent factors that determine low ovarian reserve. The multivariate score was shown to have a distinctive prediction of ovarian reserve. A cumulative score of >14 was shown to be more accurate in predicting low ovarian reserve than age, day 3 FSH, or antral follicle count separately. Moreover, a score of >14 was shown to have a sensitivity of 88% and a specificity of 69% in predicting low ovarian reserve. More important, women with a score of >14 had significantly lower clinical implantation and pregnancy rates relative to women with a score of ≤14, corresponding to 6.7% versus 22.4%, and 11.3% versus 38.6%, respectively. Conclusion(s): A novel and simple multivariate score using clinical and basal endocrine and sonographic parameters has a distinctive prediction of low ovarian reserve in infertile women undergoing assisted reproductive technology treatment. Moreover, it has the potential to predict clinical implantation and pregnancy rates in women with low and good ovarian reserve.