Objective  To analyze the influencing factors of ovulation induction therapy in patients with polycystic ovary syndrome (PCOS), and to construct a predictive model for the efficacy of ovulation induction therapy in PCOS patients.

Methods  A total of 200 infertile PCOS patients suitable for ovulation induction therapy were selected as the study subjects.All patients underwent ovulation induction with letrozole or letrozole combined with urinary gonadotropins. They were divided into effective group (n=160) and ineffective group (n=40) based on the efficacy of ovulation induction. The clinical data of the two groups were retrospectively collected and analyzed. Logistic regression analysis was used to analyze the influencing factors of ovulation induction therapy in PCOS patients, and a nomogram prediction model for the efficacy of ovulation induction was constructed. The predictive performance of the nomogram model for ovulation induction therapy in PCOS patients was evaluated.

Results  The number of ovulations and mature follicles, proportion of ovulation patients and endometrial thickness in the effective group were significantly higher, and the androgen level in the effective group was significantly lower than that in the ineffective group (P<0.05). Logistic regression analysis showed that androgen level, ovulation count, the number of mature follicles and endometrial thickness were influencing factors for the efficacy of ovulation induction in PCOS patients (OR<1, P<0.05). Receiver operating characteristic (ROC) curve analysis revealed that the area under the curve (AUC) values for ovulation count, the number of mature follicles, endometrial thickness and androgen level in assessing the efficacy of ovulation induction therapy in PCOS patients were greater than 0.60. The verification results of the nomogram prediction model showed that the consistency index value of the calibration curve was 0.984.

Conclusions  Androgen level, ovulation count, the number of mature follicles and endometrial thickness areinfluencing factors for the efficacy of ovulation induction therapy in PCOS patients. The evaluation performance of the nomogram prediction model based on the above factors is good.