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作 者:常剑锋[1] 黄志伟[1] 刘文晴 CHANG Jianfeng;HUANG Zhiwei;LIU Wenqing(Department of Laboratory Medicine,Zhaoqing Second People’s Hospital of Guangdong,Guangdong,Zhaoqing 526060,China)
机构地区:[1]广东省肇庆市第二人民医院检验科,广东肇庆526060
出 处:《中国医药科学》2020年第6期87-89,共3页China Medicine And Pharmacy
摘 要:目的研究新生儿遗传代谢病串联质谱筛查中氨基酸、有机酸和脂肪酸的诊断临界值,为新生儿遗传代谢病的串联质谱筛查提供依据。方法回顾分析2019年1~9月我市收治的患遗传代谢病活产儿98例,采用串联质谱技术进行筛查,采用百分位数法制定氨基酸、有机酸、脂肪酸的cut-off值,第1百分位数(P1)为低值阳性,第99百分位数(P99)为高值阳性,并与上海新华医院进行比较。结果本研究的98例患儿中氨基酸代谢异常55例、有机酸代谢异常26例及脂肪酸代谢异常17例;ARG、GLY、TYR、ORN、LEU、VAL、C5、C12、C0/(C16+C18)的最佳临床诊断临界值分别为5.57、247.32、86.28、82.94、116.82、72.14、0.07、0.03、8.16,其中GLY、TYR、ORN、VAL及C0/(C16+C18)的AUC均>0.9,具有较高的诊断效能。结论本研究建立了适合人群正常参考范围,为临床降低假阳性率、提高阳性预测值提供参考依据。Objective To study the diagnostic thresholds of amino acids,organic acids and fatty acids in tandem mass spectrometry screening of neonatal genetic metabolic disease,and to provide a basis for tandem mass spectrometry screening of neonatal genetic metabolic disease.Methods A retrospective analysis was made on 98 live infants with genetic metabolic disease admitted and treated in our city from January to September 2019.Tandem mass spectrometry was used for screening,and the percentile method was used to determine the cut-off values of amino acids,organic acids and fatty acids.The 1st percentile(P1)was positive for low value,and the 99th percentile(P99)was positive for high value,which was compared with Shanghai Xinhua Hospital.Results Among the 98 infants in this study,55 had abnormal amino acid metabolism,26 had abnormal organic acid metabolism,and 17 had abnormal fatty acid metabolism;the optimal clinical diagnostic thresholds for ARG,GLY,TYR,ORN,LEU,VAL,C5,C12,and C0/(C16+C18)were 5.57,247.32,86.28,82.94,116.82,72.14,0.07,0.03,and 8.16,respectively,wherein the AUCs of GLY,TYR,ORN,VAL,and C0/(C16+C18)were all>0.9,with high diagnostic performance.Conclusion In this study,the appropriate normal reference range of the population was established to provide a reference basis for clinical reduction of false positive rate and improvement of positive predictive value.
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