染色体核型分析联合微阵列技术应用于胎儿软指标异常产前诊断的结果分析
Outcome analysis of karyotyping combined with microarray technology in prenatal diagnosis of fetal soft index abnormalities
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作者信息
- 1. 内蒙古科技大学包头医学院,包头 014000
- 2. 内蒙古自治区妇幼保健院,呼和浩特 010010
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摘要
目的 通过分析超声软指标异常胎儿产前诊断的结果,探讨染色体核型分析联合微阵列技术的应用价值.方法 研究对象为2021年1月至2023年3月于内蒙古自治区妇幼保健院因胎儿软指标异常同时行核型分析及微阵列(chromosomal microarray analysis,CMA)检测的253例孕妇,对胎儿染色体异常的检出率及结果进行分析.结果 多项软指标组染色体异常检出率高于单项软指标组,差异有统计学意义(P＜0.05),两项技术检出率差异比较无统计学意义(P＞0.05),两项技术联合应用检出率较核型分析组及CMA组分别提高了5.93％及1.58％.结论 染色体核型分析与微阵列技术联合应用,可提高软指标异常胎儿染色体异常的检出率,二者优势互补、互为验证,为产前遗传咨询提供重要依据,进而减少和预防新生儿出生缺陷.
Abstract
Objective To explore the value of chromosomal karyotyping combined with microarray technology by analyzing the results of prenatal diagnosis of fetuses with abnormal ultrasound soft indexes.Methods A total of 253 pregnant women who underwent both karyotyping and chromosomal microarray analysis(CMA)testing for fetal soft index abnormalities in Inner Mongolian Maternal and Health Care Hosipital from January 2021 to March 2023,and the detection rate and results of fetal chromosomal abnormalities were analyzed.Results The detection rate of chromosomal abnormalities in the multiple soft index group was higher than that in the single soft index group,and the difference was statistically significant(P＜0.05),there was no significant difference in detection rate between the two techniques(P＞0.05),and the detection rate of the two techniques in combination with the karyotyping group and the CMA group was increased by 5.93％and 1.58％,respectively.Conclusion The combined application of karyotyping and microarray technology can improve the detection rate of chromosomal abnormalities in fetuses with abnormal soft index,and the two complement each other's strengths and validate each other,so as to provide an important basis for prenatal genetic counseling,thus reducing and preventing birth defects in newborns.
关键词
微阵列/核型分析/软指标/产前诊断Key words
microarray/karyotype analysis/soft index/prenatal diagnosis引用本文复制引用
基金项目
内蒙古自治区自然科学基金(2018LH08019)
出版年
2024
NETL
NSTL
万方数据