Predicting risk of blastocyst aneuploidy among women with previous aneuploid pregnancy loss: a multicenter-data-based multivariable model

Author:

Zhang Limei12ORCID,Yang Yuanyuan34,Wang Wenjun5,Luo Lu12,Zhang Zhewei6,Wu Jingya7,Ou Songbang5,Mai Jiayi5,Guo Lan8,Wan Jianxin9,Yuan Guangwei10,Ding Chenhui12,Xu Yan12,Zhou Canquan12ORCID,Gong Fei34ORCID,Wang Qiong12ORCID

Affiliation:

1. Department of Obstetrics and Gynecology, Reproductive Medicine Center, The First Affiliated Hospital of Sun Yat-sen University , Guangzhou, China

2. Guangdong Provincial Key Laboratory of Reproductive Medicine , Guangzhou, China

3. Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University , Changsha, China

4. Reproductive and Genetic Hospital of CITIC-Xiangya , Changsha, China

5. Reproductive Medicine Center, Department of Obstetrics and Gynecology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University , Guangzhou, China

6. Division of Information and Computational Sciences, School of Mathematical Sciences, Fudan University , Shanghai, China

7. Department of Gynecology, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University) , Zhuhai, China

8. Department of Medical Statistics, School of Public Health, Sun Yat-sen University , Guangzhou, China

9. Department of Laboratory Medicine, The First Affiliated Hospital of Sun Yat-sen University , Guangzhou, China

10. College of Professional Studies, Northeastern University , Boston, MA, USA

Abstract

Abstract STUDY QUESTION Can blastocyst aneuploidy be predicted for patients with previous aneuploid pregnancy loss (PAPL) and receiving preimplantation genetic testing for aneuploidy (PGT-A)? SUMMARY ANSWER Multivariable logistic regression models were established to predict high risk of blastocyst aneuploidy using four identified factors, presenting good predictive performance. WHAT IS KNOWN ALREADY Aneuploidy is the most common embryonic chromosomal abnormality leading to pregnancy loss. Several studies have demonstrated a higher embryo aneuploidy rate in patients with PAPL, which has suggested that PGT-A should have benefits in PAPL patients intending to improve their pregnancy outcomes. However, recent studies have failed to demonstrate the efficacy of PGT-A for PAPL patients. One possible way to improve the efficacy is to predict the risk of blastocyst aneuploidy risk in order to identify the specific PAPL population who may benefit from PGT-A. STUDY DESIGN, SIZE, DURATION We conducted a multicenter retrospective cohort study based on data analysis of 1119 patients receiving PGT-A in three reproductive medical centers of university affiliated teaching hospitals during January 2014 to June 2020. A cohort of 550 patients who had one to three PAPL(s) were included in the PAPL group. In addition, 569 patients with monogenic diseases without pregnancy loss were taken as the non-PAPL group. PARTICIPANTS/MATERIALS, SETTING, METHODS PGT-A was conducted using single nucleotide polymorphism microarrays and next-generation sequencing. Aneuploidy rates in Day 5 blastocysts of each patient were calculated and high-risk aneuploidy was defined as a rate of ≥50%. Candidate risk factors for high-risk aneuploidy were selected using the Akaike information criterion and were subsequently included in multivariable logistic regression models. Overall predictive accuracy was assessed using the confusion matrix, discrimination by area under the receiver operating characteristic curve (AUC), and calibration by plotting the predicted probabilities versus the observed probabilities. Statistical significance was set at P < 0.05. MAIN RESULTS AND THE ROLE OF CHANCE Blastocyst aneuploidy rates were 30 ± 25% and 21 ± 19% for PAPL and non-PAPL groups, respectively. Maternal age (odds ratio (OR) = 1.31, 95% CI 1.24–1.39, P < 0.001), number of PAPLs (OR = 1.40, 95% CI 1.05–1.86, P = 0.02), estradiol level on the ovulation trigger day (OR = 0.47, 95% CI 0.30–0.73, P < 0.001), and blastocyst formation rate (OR = 0.13, 95% CI 0.03–0.50, P = 0.003) were associated with high-risk of blastocyst aneuploidy. The predictive model based on the above four variables yielded AUCs of 0.80 using the training dataset and 0.83 using the test dataset, with average and maximal discrepancies of 2.89% and 12.76% for the training dataset, and 0.98% and 5.49% for the test dataset, respectively. LIMITATIONS, REASONS FOR CAUTION Our conclusions might not be compatible with those having fewer than four biopsied blastocysts and diminished ovarian reserves, since all of the included patients had four or more biopsied blastocysts and had exhibited good ovarian reserves. WIDER IMPLICATIONS OF THE FINDINGS The developed predictive model is critical for counseling PAPL patients before PGT-A by considering maternal age, number of PAPLs, estradiol levels on the ovulation trigger day, and the blastocyst formation rate. This prediction model achieves good risk stratification and so may be useful for identifying PAPL patients who may have higher risk of blastocyst aneuploidy and can therefore acquire better pregnancy outcomes by PGT-A. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the National Natural Science Foundation of China under Grant (81871159). No competing interest existed in the study. TRIAL REGISTRATION NUMBER N/A.

Funder

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Obstetrics and Gynecology,Rehabilitation,Reproductive Medicine

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