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Neonatal Screening for Hyperthyroidism proof of concept

医学 先天性甲状腺功能减退 新生儿筛查 促甲状腺激素 接收机工作特性 激素 儿科 甲状腺 内科学 回顾性队列研究 内分泌学 预测值
作者
Maïa Banigé,Dulanjalee Kariyawasam,Valérie Gauthereau,Dominique Luton,Michel Polak
出处
期刊:The Journal of Clinical Endocrinology and Metabolism [The Endocrine Society]
被引量:2
标识
DOI:10.1210/clinem/dgab890
摘要

Early treatment is essential to avoid the cardiac complication of neonatal hyperthyroidism (NH). Our results have direct implications for clinical care.NH can cause potentially fatal neonatal thyrotoxicosis. Here, we have evaluated the feasibility of neonatal hyperthyroidism screening using the thyroid-stimulating hormone value in dried blood collected routinely on filter paper on the third postnatal day of life for congenital hypothyroidism screening.Retrospective case-control study. Cases were identified using data from our previously published study of 280 000 infants born in 10 maternity units in France in 2007-2014. Controls were identified among the 1 362 564 infants born in the Ile-de-France region during the same period.A screening thyroid-stimulating hormone level below 0.18 mIU/L on the third postnatal day had 71% (95% CI 44-90%) sensitivity, 99% (95% CI 99-100%) specificity, 81% (95% CI 74-86%) positive predictive value, and 98% (95% CI 97-99%) negative predictive value for detecting severe NH. By univariate regression analysis, the screening thyroid-stimulating hormone value was the strongest predictor of NH (P < .00001), with an area under the receiver-operating characteristics curve of 0.98 (95% CI 0.95-1.0). Expected frequencies were not significantly different from observed frequencies (Hosmer-Lemeshow test, P = .99).The screening thyroid-stimulating hormone test can be used to detect severe NH, the optimal cut-off being 0.18 mIU/L. The additional cost compared with screening for congenital hypothyroidism would be small. Infants with neonatal hyperthyroidism would benefit from an earlier diagnosis with treatment initiation at the presymptomatic stage in many cases, ensuring optimal outcomes.
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