Patients
A cohort was created of 1139 women with PCOS who had undergone ICSI/IVF treatment at the First Affiliated Hospital of Zhengzhou University from January 2014 to December 2019. The sample size of this study was determined to include all eligible patients who had received their first fresh IVF/ICSI cycles with autologous oocytes from 2014 to 2019.PCOS patients were diagnosed according to the Rotterdam criteria[10].None of the patients who participated in the study had other endocrine diseases, such as Cushing’s disease, congenital adrenal hyperplasia, etc. Patients with infertility due to female subjects with a medical history of endometriosis, reproductive malformation, or uterine fibroids were also excluded. In addition, patients who used donor oocytes or spermatozoa or had undergone preimplantation genetic diagnosis (PGD) or preimplantation genetic screening (PGS) were also excluded.
The study received approval from the Ethics in Research Committee of The First Affiliated Hospital of Zhengzhou University. The ethical approval number of the study is 2020-KY-419. Because it is difficult to trace patients for their informed consent in a retrospective analysis, this study was exempted following approval of the Ethics Committee.
Procedure
All individuals included in the study underwent controlled ovarian hyperstimulation according to a currently established gonadotropin-releasing hormone (GnRH) agonist protocol. Long-acting gonadotropin-releasing hormone agonist (3.75 mg) was administered on the 2nd to 3rd day of menstruation. After 30–42 days, the down-regulation criteria were reached, and gonadotropin was then administered for controlled ovarian hyperstimulation (COH). Gonadotropin dose was determined according to body mass index (BMI), anti-Müllerian hormone (AMH) and primary follicular condition. Gonadotropin dose was adjusted according to follicle size and hormone level, and human menopausal gonadotrophic hormone was added. When at least one follicle with a mean diameter more than 18 mm, human chorionic gonadotropin (hCG) was given, and 37 h after hCG injection, oocyte retrieval was performed under the guidance of vaginal ultrasound. After co-culture of spermatozoa and oocytes for 5 h, the cumulus cells surrounding the oocytes were removed to observe and record the appearance of the second polar body. Morula formation on Day 3 (D3) and blastocyst formation on Day 5 (D5) and Day 6 (D6) were observed and recorded. After embryo transfer, routine vaginal administration of progesterone gel was performed for luteal support. Fresh embryo transfers were performed on Day 3 or Day 5. The number of embryos transferred varied from one to two based on the recommendation of the Health Ministry of China and the requests of patients.
Blood test and outcome
Hormone and metabolite level analysis, including serum follicle-stimulating hormone (FSH) luteinizing hormone (LH), estradiol (E2), testosterone (T), progesterone (P), and Prolactin (PRL) were evaluated on days 2 to 4 of menstruation using a radioimmunoassay method. This study used ultrasonography to detect the number of antral follicles on day 2. Since P data and T data displays a skewed distribution, the logarithm was converted to normally distributed data named P_log and T_log.
The primary endpoint was the live birth rate. Blood samples were taken at 14 days and 18 days after embryo transfer to determine serum β-hCG. Clinical pregnancy was achieved when the intrauterine gestational sac was recognized by ultrasonography after embryo transfer and a positive serum β-hCG(>50 IU/L)concentration was found. Live birth was defined as any birth event in which at least one baby is born alive.
Development of the model
The endpoint was the live birth rate. All included patients were randomly divided into training cohort and validation cohort in a ratio of 7 to 3 and patients with missing values for predictors were excluded. On the basis of Univariate and Multivariable Logistic Regression (MLR) analysis, a column with important risk factors was established to forecast the probability of a live birth in patients with PCOS using R software. The P-values were based on the Wald test. A P-value <0.05 was considered significant.
The nomogram was developed by MLR using a training group of 835 patients diagnosed with PCOS. Backward variable selection was performed to determine independent covariates. The minimum Akaike information criterion (AIC) model is the optimal model; AIC is a measure of statistical model “goodness of fit.” Increasing the number of free parameters improves the optimization of fit. AIC encourages the optimization of data fit but tries to avoid overfitting. Therefore, a preferred model would be one with the lowest AIC value. Assuming that a choice is made among n models, the AIC value of n models can be calculated, and the model corresponding to the minimum AIC value can be selected.
Validation of the model
The model was validated using data from 358 patients diagnosed with PCOS.
The correction of the model was assessed by discrimination and calibration. Discrimination was assessed using the area under the receiver operating characteristic (ROC) curve and the area under the curve (AUC) to reflect the ability of the test to differentiate between results at all possible positive levels. In addition, the 95% confidence interval (95% CI) was calculated for each AUC.
The calibration curves were graphically assessed by plotting the frequency of observed outcomes against the average predicted outcome problem or risk to increase the estimated probability. The relative corrected C-index was calculated by bootstrapping verification (1000 bootstrap resampling) for internal validation.
IBM SPSS Statistics Premium V22.0 (SPSS Inc., Chicago, IL, USA) and RStudio (Version 1.3.1073) were used for statistical analysis. Differences between groups were compared using Student’s t-test or Chi-squared test. The difference of proportions between groups was compared using false discovery rate. A P-value <0.05 was considered significant. To develop and validate the clinical model and nomogram, we used “regplot,” “pROC,” “ggplot2,” “glmnet,” “riskRegression,” “plotROC,” “ggridges,” “survminer,” “survival,” “ipred,” “MASS,” “VGAM,” “rms,” and some other packages in R-Studio.