A prediction model to select PCOS patients suitable for IVM treatment based on anti-Mullerian hormone and antral follicle count

Abstract

Which baseline patient characteristics can help assisted reproductive technology practitioners to identify patients who are suitable for in-vitro maturation (IVM) treatment? In patients with polycystic ovary syndrome (PCOS) who undergo oocyte IVM in a non-hCG-triggered system, circulating anti-Müllerian hormone (AMH), antral follicle count (AFC) and total testosterone are independently related to the number of immature oocytes and hold promise as outcome predictors to guide the patient selection process for IVM. Patient selection criteria for IVM treatment have been described in normo-ovulatory patients, although patients with PCOS constitute the major target population for IVM. With this study, we assessed the independent predictive value of clinical and endocrine parameters that are related to oocyte yield in patients with PCOS undergoing IVM. Cohort study involving 124 consecutive patients with PCOS undergoing IVM whose data were prospectively collected. Enrolment took place between January 2010 and January 2012. Only data relating to the first IVM cycle of each patient were included. Patients with PCOS underwent oocyte retrieval for IVM after minimal gonadotrophin stimulation and no hCG trigger. Correlation coefficients were calculated to investigate which parameters are related to immature oocyte yield (patient's age, BMI, baseline hormonal profile and AMH, AFC). The independence of predictive parameters was tested using multivariate linear regression analysis. Finally, multivariate receiver operating characteristic (ROC) analyses for cumulus oocyte complexes (COC) yield were performed to assess the efficiency of the prediction model to select suitable candidates for IVM. Using multivariate regression analysis, circulating baseline AMH, AFC and baseline total testosterone serum concentration were incorporated into a model to predict the number of COC retrieved in an IVM cycle, with unstandardized coefficients [95% confidence interval (CI)] of 0.03 (0.02-0.03) (P < 0.001), 0.012 (0.008-0.017) (P < 0.001) and 0.37 (0.18-0.57) (P < 0.001), respectively. Logistic regression analysis shows that a prediction model based on AMH and AFC, with unstandardized coefficients (95% CI) of 0.148 (0.03-0.25) (P < 0.001) and 0.034 (-0.003-0.07) (P = 0.025), respectively, is a useful patient selection tool to predict the probability to yield at least eight COCs for IVM in patients with PCOS. In this population, patients with at least eight COC available for IVM have a statistically higher number of embryos of good morphological quality (2.9 ± 2.3; 0.9 ± 0.9; P < 0.001) and cumulative ongoing pregnancy rate [30.4% (24 out of 79); 11% (5 out of 45); P = 0.01] when compared with patients with less than eight COC. ROC curve analysis showed that this prediction model has an area under the curve of 0.7864 (95% CI = 0.6997-0.8732) for the prediction of oocyte yield in IVM. The proposed model has been constructed based on a genuine IVM system, i.e. no hCG trigger was given and none of the oocytes matured in vivo. However, other variables, such as needle type, aspiration technique and whether or not hCG-triggering is used, should be considered as confounding factors. The results of this study have to be confirmed using a second independent validation sample. The proposed model could be applied to patients with PCOS after confirmation through a further validation study. This study was supported by a research grant by the Institute for the Promotion of Innovation by Science and Technology in Flanders, Project number IWT 070719.