Objective:To investigate the independent clinical factors of live birth rate of the first frozen-thawed embryo transfer (FET) cycle after transcervical resection of adhesion (TCRA).Methods:A retrospective case-control study was conducted to analyze the clinical data of patients with intrauterine adhesion (IUA) who received FET in Reproductive Center of Reproductive and Genetic Hospital of CITIC-XIANGYA from January 2019 to June 2022 ( n=6 154). According to the severity of intrauterine adhesions in patients, they were classified into mild adhesions ( n=172), moderate adhesions ( n=5 723), and severe adhesions ( n=259). Based on the FET outcome, the patients were divided into live birth group and non-live birth group. The risk factors and protective factors of live birth were analyzed by multivariate logistic regression. Results:1) No independent factor of live birth was found in the mild IUA group. 2) In the moderate IUA group, the protective factors of live birth included secondary infertility ( OR=1.39, 95% CI: 1.07-1.80, P=0.015), hysteroscopic polypectomy ( OR=1.38, 95% CI: 1.05-1.83, P=0.023), No. of high-quality embryos transferred (one embryo: OR=1.58, 95% CI: 1.37-1.82, P<0.001; two embryos: OR=2.55, 95% CI: 1.80-3.64, P<0.001), two embryos transferred ( OR=1.77, 95% CI: 1.48-2.12, P<0.001), embryo stage (blastocyst transferred, OR=4.93, 95% CI: 3.68-6.63, P<0.001; blastocyst+cleavage transferred OR=1.90, 95% CI: 1.11-3.21, P=0.021), preimplantation genetic testing embryo ( OR=1.42, 95% CI: 1.19-1.69, P<0.001), endometrial thickness before transplantation ( OR=1.11, 95% CI: 1.07-1.15, P<0.001). Risk factors of live birth included female age ( OR=0.94, 95% CI: 0.92-0.96, P<0.001), infertility due to male factor ( OR=0.83, 95% CI: 0.71-0.96, P=0.011), combined repeated implantation failure ( OR=0.60, 95% CI: 0.42-0.87, P=0.007), combined unicornuate uterus/uterus didelphys ( OR=0.25, 95% CI: 0.06-0.79, P=0.033), American Fertility Society score ( OR=0.94, 95% CI: 0.89-0.98, P=0.010), No. of TCRA ( OR=0.83, 95% CI: 0.77-0.90, P<0.001), gonadotropin-releasing hormone agonists down-regulation combined with artificial cycle ( OR=0.56, 95% CI: 0.45-0.69, P<0.001), artificial cycle ( OR=0.62, 95% CI: 0.51-0.76, P<0.001). 3) In the severe IUA group, the risk factor of live birth was artificial cycle ( OR=0.25, 95% CI: 0.07-0.80, P=0.027). Conclusion:The clinical factors that affect the live birth outcome of the first FET cycle after TCRA have different results in patients with different degrees of adhesion. In patients with moderate adhesions, there are 17 clinical indicators that affect the live birth rate. In patients with severe adhesions, the artificial cycle is an independent factor affecting the live birth rate.

Objective:To investigate the independent clinical factors of live birth rate of the first frozen-thawed embryo transfer (FET) cycle after transcervical resection of adhesion (TCRA).Methods:A retrospective case-control study was conducted to analyze the clinical data of patients with intrauterine adhesion (IUA) who received FET in Reproductive Center of Reproductive and Genetic Hospital of CITIC-XIANGYA from January 2019 to June 2022 ( n=6 154). According to the severity of intrauterine adhesions in patients, they were classified into mild adhesions ( n=172), moderate adhesions ( n=5 723), and severe adhesions ( n=259). Based on the FET outcome, the patients were divided into live birth group and non-live birth group. The risk factors and protective factors of live birth were analyzed by multivariate logistic regression. Results:1) No independent factor of live birth was found in the mild IUA group. 2) In the moderate IUA group, the protective factors of live birth included secondary infertility ( OR=1.39, 95% CI: 1.07-1.80, P=0.015), hysteroscopic polypectomy ( OR=1.38, 95% CI: 1.05-1.83, P=0.023), No. of high-quality embryos transferred (one embryo: OR=1.58, 95% CI: 1.37-1.82, P<0.001; two embryos: OR=2.55, 95% CI: 1.80-3.64, P<0.001), two embryos transferred ( OR=1.77, 95% CI: 1.48-2.12, P<0.001), embryo stage (blastocyst transferred, OR=4.93, 95% CI: 3.68-6.63, P<0.001; blastocyst+cleavage transferred OR=1.90, 95% CI: 1.11-3.21, P=0.021), preimplantation genetic testing embryo ( OR=1.42, 95% CI: 1.19-1.69, P<0.001), endometrial thickness before transplantation ( OR=1.11, 95% CI: 1.07-1.15, P<0.001). Risk factors of live birth included female age ( OR=0.94, 95% CI: 0.92-0.96, P<0.001), infertility due to male factor ( OR=0.83, 95% CI: 0.71-0.96, P=0.011), combined repeated implantation failure ( OR=0.60, 95% CI: 0.42-0.87, P=0.007), combined unicornuate uterus/uterus didelphys ( OR=0.25, 95% CI: 0.06-0.79, P=0.033), American Fertility Society score ( OR=0.94, 95% CI: 0.89-0.98, P=0.010), No. of TCRA ( OR=0.83, 95% CI: 0.77-0.90, P<0.001), gonadotropin-releasing hormone agonists down-regulation combined with artificial cycle ( OR=0.56, 95% CI: 0.45-0.69, P<0.001), artificial cycle ( OR=0.62, 95% CI: 0.51-0.76, P<0.001). 3) In the severe IUA group, the risk factor of live birth was artificial cycle ( OR=0.25, 95% CI: 0.07-0.80, P=0.027). Conclusion:The clinical factors that affect the live birth outcome of the first FET cycle after TCRA have different results in patients with different degrees of adhesion. In patients with moderate adhesions, there are 17 clinical indicators that affect the live birth rate. In patients with severe adhesions, the artificial cycle is an independent factor affecting the live birth rate.

OBJECTIVE: To realize batch processing of univariate Cox regression analysis for great database by SAS marco program. METHODS: We wrote a SAS macro program, which can filter, integrate, and export P values to Excel by SAS9.2. The program was used for screening survival correlated RNA molecules of ovarian cancer. RESULTS: A SAS marco program could finish the batch processing of univariate Cox regression analysis, the selection and export of the results. CONCLUSION The SAS macro program has potential applications in reducing the workload of statistical analysis and providing a basis for batch processing of univariate Cox regression analysis.
Proportional Hazards Models ; Regression Analysis ; Software