Identification of predictors of the ovarian response to clomiphene citrate in infertile women with polycystic ovary syndrome: A post-hoc analysis of a randomized controlled trial.
Clomiphene citrate (CC) is the first-line medication for inducing ovulation in women with polycystic ovary syndrome (PCOS). However, approximately 20% of patients with PCOS are resistant to CC. This study aims to identify reliable baseline predictors of CC resistance in infertile women with PCOS.
A post-hoc analysis of a large, multicenter randomized controlled trial (PCOSAct trial) conducted in China. The current analysis comprised the 471 participants who were randomized to the active CC arm and completed the requisite follow-up. To identify potential candidate variables, we employed multivariable logistic and LASSO regression analyses. Within the framework of a multivariable logistic regression model, we also estimated the independent associations between the identified candidate variables and resistance to CC. Additionally, we plotted the Receiver Operating Characteristic (ROC) curve and utilized the DeLong method to compare the statistical differences in the area under the curve (AUC). Finally, we constructed a restricted cubic spline (RCS) logistic regression model to illustrate the dose-response relationship between continuous predictor variables and CC resistance.
CC resistance was identified in 32 (6.8%) participants. Body Mass Index (BMI), Total Testosterone (TT), and Anti-Müllerian Hormone (AMH) were useful predictors of ovarian response to CC. The "T+BMI" dual-factor model demonstrated high discriminative power (AUC = 0.801) and was statistically comparable to the three-factor model including AMH (AUC = 0.818; P = 0.347). TT was the strongest individual predictor (OR = 2.73 per 1-unit), while BMI was the most significant modifiable risk factor (OR = 2.49 per 1-SD).
A simplified "T + BMI" assessment provides comparable prognostic utility without the need for AMH testing. For patients at high risk of CC resistance, we recommend upfront use of aromatase inhibitors or low-dose gonadotropins. This strategy avoids ineffective treatment cycles and enables personalized ovulation induction.
The study was registered on ClinicalTrials.gov under the identification number NCT01573858 on July 6, 2012.
A post-hoc analysis of a large, multicenter randomized controlled trial (PCOSAct trial) conducted in China. The current analysis comprised the 471 participants who were randomized to the active CC arm and completed the requisite follow-up. To identify potential candidate variables, we employed multivariable logistic and LASSO regression analyses. Within the framework of a multivariable logistic regression model, we also estimated the independent associations between the identified candidate variables and resistance to CC. Additionally, we plotted the Receiver Operating Characteristic (ROC) curve and utilized the DeLong method to compare the statistical differences in the area under the curve (AUC). Finally, we constructed a restricted cubic spline (RCS) logistic regression model to illustrate the dose-response relationship between continuous predictor variables and CC resistance.
CC resistance was identified in 32 (6.8%) participants. Body Mass Index (BMI), Total Testosterone (TT), and Anti-Müllerian Hormone (AMH) were useful predictors of ovarian response to CC. The "T+BMI" dual-factor model demonstrated high discriminative power (AUC = 0.801) and was statistically comparable to the three-factor model including AMH (AUC = 0.818; P = 0.347). TT was the strongest individual predictor (OR = 2.73 per 1-unit), while BMI was the most significant modifiable risk factor (OR = 2.49 per 1-SD).
A simplified "T + BMI" assessment provides comparable prognostic utility without the need for AMH testing. For patients at high risk of CC resistance, we recommend upfront use of aromatase inhibitors or low-dose gonadotropins. This strategy avoids ineffective treatment cycles and enables personalized ovulation induction.
The study was registered on ClinicalTrials.gov under the identification number NCT01573858 on July 6, 2012.