Improving the Dutch Newborn Screening for Central Congenital Hypothyroidism by Using 95% Reference Intervals for Thyroxine-Binding Globulin

Abstract

Introduction: Newborn screening (NBS) for congenital hypothyroidism (CH) in the Netherlands consists of thyroxine (T4), thyroid-stimulating hormone (TSH), and T4-binding globulin (TBG) measurements to detect thyroidal CH and central CH (CH-C). CH-C is detected by T4 or a calculated T4/TBG ratio, which serves as an indirect measure of free T4. TSH and TBG are only measured in the lowest 20 and 5% of daily T4 values, respectively. A recent evaluation of the Dutch NBS for CH showed that the T4 and T4/TBG ratio contribute to the detection of CH-C but also lead to a low positive predictive value (PPV). Dried blood spot (DBS) reference intervals (RIs) are currently unknown and may contribute to improvement of our NBS algorithm. Materials and Methods: RIs of T4, TSH, TBG, and the T4/TBG ratio were determined according to Clinical & Laboratory Standards Institute guidelines in heel puncture cards from routine NBS in both sexes and at the common NBS sampling ages. Scatter plots were used to compare the healthy reference population to previously published data of CH-C patients and false positives. Results: Analyses of 1,670 heel puncture cards showed small differences between subgroups and led to the formulation of total sample DBS RIs for T4 (56–118 nmol/L), TSH (<2.6 mIU/L), TBG (116–271 nmol/L), and the T4/TBG ratio (>20). 46% of false-positive referrals based on T4 alone had a TBG below the RI, indicating preventable referral due to partial TBG deficiency. One case of CH-C also had partial TBG deficiency (TBG 59 and T4 12 nmol/L blood). Discussion/Conclusion: Established DBS RIs provided possibilities to improve the PPV of the Dutch CH NBS algorithm. We conclude that by taking partial TBG deficiency into account, approximately half of T4 false-positive referrals may be prevented while maintaining NBS sensitivity at the current level.

Optimizing the Dutch newborn screening for congenital hypothyroidism by incorporating amino acids and acylcarnitines in a machine learning-based model

“…Data of newborns with a normal CH screening result (hereafter named healthy controls) from a dataset from a recent study to establish neonatal reference intervals for T4, TSH, and TBG concentrations and the T4/TBG ratio were reused as healthy controls ( 3 ). This data ( n = 1926) was collected between February and March 2019 at the Newborn Screening Laboratory of Amsterdam UMC, and for every subject, sex, gestational age, gestational weight, age at NBS sampling, T4, TSH, TBG concentrations, T4_SD, and the T4/TBG ratio were available.…”

“…Pearson’s correlations were used, where the P -values were adjusted for multiple testing using the Benjamini–Hochberg procedure, reported as a false discovery rate (FDR) value. Data from newborns with a TBG concentration ≤105 nmol/L were excluded from the correlation analysis, as these subjects show a (partial) TBG deficiency and are currently not referred anymore since March 1, 2021 ( 3 ).…”

“…In case of a T4_SD ≤ −0.8, TSH is measured (approximately the lowest 20% of the daily mean) and in case of a T4_SD ≤ −1.6, thyroxine binding globulin (TBG) is measured as well (approximately the lowest 5% of the daily T4 concentrations). TBG is used to reduce the number of false-positive referrals (FPRs) due to (partial) TBG deficiency which is associated with reduced total T4 concentrations in combination with normal free T4 (fT4) concentrations ( 2 , 3 ). A calculated T4/TBG ratio serves as an indirect measure of fT4.…”

“…The downside of detecting primary and central CH is a low positive predictive value (PPV) of 21%, determined in the period of 2007–2017 ( 7 ). Our previous study aimed to improve the PPV of the Dutch CH screening algorithm by establishing reference intervals for T4 and TBG and thereby refining the cutoff value for a (partial) TBG deficiency ( 3 ) and by using machine learning ( 8 ). The latter study used a dataset containing almost all children with a referral in the historical CH screening from 2007 to 2017 (CH cases and FPRs) and children with normal CH NBS results from a cohort in 2019 ( 3 ).…”

“…Our previous study aimed to improve the PPV of the Dutch CH screening algorithm by establishing reference intervals for T4 and TBG and thereby refining the cutoff value for a (partial) TBG deficiency ( 3 ) and by using machine learning ( 8 ). The latter study used a dataset containing almost all children with a referral in the historical CH screening from 2007 to 2017 (CH cases and FPRs) and children with normal CH NBS results from a cohort in 2019 ( 3 ). With machine learning (random forest model) using only general information and NBS CH screening results a PPV of 26% was obtained on this dataset, while all known CH cases were indicated as such by the algorithm.…”