Rowe, Alexander D.

Newborn screening for congenital hypothyroidism remains challenging decades after broad implementation worldwide. Testing protocols are not uniform in terms of targets (TSH and/or T4) and protocols (parallel vs. sequential testing; one or two specimen collection times), and specificity (with or without collection of a second specimen) is overall poor. The purpose of this retrospective study is to investigate the potential impact of multivariate pattern recognition software (CLIR) to improve the post-analytical interpretation of screening results. Seven programs contributed reference data (N = 1,970,536) and two sets of true (TP, N = 1369 combined) and false (FP, N = 15,201) positive cases for validation and verification purposes, respectively. Data were adjusted for age at collection, birth weight, and location using polynomial regression models of the fifth degree to create three-dimensional regression surfaces. Customized Single Condition Tools and Dual Scatter Plots were created using CLIR to optimize the differential diagnosis between TP and FP cases in the validation set. Verification testing correctly identified 446/454 (98%) of the TP cases, and could have prevented 1931/5447 (35%) of the FP cases, with variable impact among locations (range 4% to 50%). CLIR tools either as made here or preferably standardized to the recommended uniform screening panel could improve performance of newborn screening for congenital hypothyroidism.

In 2012, the Norwegian newborn screening program (NBS) was expanded (eNBS) from screening for two diseases to that for 23 diseases (20 inborn errors of metabolism, IEMs) and again in 2018, to include a total of 25 conditions (21 IEMs). Between 1 March 2012 and 29 February 2020, 461,369 newborns were screened for 20 IEMs in addition to phenylketonuria (PKU). Excluding PKU, there were 75 true-positive (TP) (1:6151) and 107 (1:4311) false-positive IEM cases. Twenty-one percent of the TP cases were symptomatic at the time of the NBS results, but in two-thirds, the screening result directed the exact diagnosis. Eighty-two percent of the TP cases had good health outcomes, evaluated in 2020. The yearly positive predictive value was increased from 26% to 54% by the use of the Region 4 Stork post-analytical interpretive tool (R4S)/Collaborative Laboratory Integrated Reports 2.0 (CLIR), second-tier biochemical testing and genetic confirmation using DNA extracted from the original dried blood spots. The incidence of IEMs increased by 46% after eNBS was introduced, predominantly due to the finding of attenuated phenotypes. The next step is defining which newborns would truly benefit from screening at the milder end of the disease spectrum. This will require coordinated international collaboration, including proper case definitions and outcome studies.

Abstract BACKGROUND Urinary concentrations of creatine and guanidinoacetic acid divided by creatinine are informative markers for cerebral creatine deficiency syndromes (CDSs). The renal excretion of these substances varies substantially with age and sex, challenging the sensitivity and specificity of postanalytical interpretation. METHODS Results from 155 patients with CDS and 12 507 reference individuals were contributed by 5 diagnostic laboratories. They were binned into 104 adjacent age intervals and renormalized with Box–Cox transforms (Ξ). Estimates for central tendency (μ) and dispersion (σ) of Ξ were obtained for each bin. Polynomial regression analysis was used to establish the age dependence of both μ[log(age)] and σ[log(age)]. The regression residuals were then calculated as z-scores = {Ξ − μ[log(age)]}/σ[log(age)]. The process was iterated until all z-scores outside Tukey fences ±3.372 were identified and removed. Continuous percentile charts were then calculated and plotted by retransformation. RESULTS Statistically significant and biologically relevant subgroups of z-scores were identified. Significantly higher marker values were seen in females than males, necessitating separate reference intervals in both adolescents and adults. Comparison between our reconstructed reference percentiles and current standard age-matched reference intervals highlights an underlying risk of false-positive and false-negative events at certain ages. CONCLUSIONS Disease markers depending strongly on covariates such as age and sex require large numbers of reference individuals to establish peripheral percentiles with sufficient precision. This is feasible only through collaborative data sharing and the use of appropriate statistical methods. Broad application of this approach can be implemented through freely available Web-based software.