Abstract
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.
Highlights
Newborn screening (NBS) for congenital hypothyroidism (CH) has been performed globally since the 1970s, but despite broad worldwide implementation and a limited range of analytical methods, there is surprisingly little consensus around the testing protocols in place for reporting abnormal results [1,2]
After normalization of the moving percentiles by min-max score of more than 1 million data points, it becomes evident that even small increments of covariate (1 h up to 1 week of age and 25 g up to 5000 g) result in noticeable variations of the peripheral percentiles, meaning that a given result could be misinterpreted, especially in samples collected before 24 h of age and in premature cases born less than 2500 g of weight
The process to create a Single Condition Tool has been described previously [20,32]. It consists of a sequential selection of: (a) configuration parameters; (b) location; (c) high and low markers; (d) adjustments
Summary
Newborn screening (NBS) for congenital hypothyroidism (CH) has been performed globally since the 1970s, but despite broad worldwide implementation and a limited range of analytical methods, there is surprisingly little consensus around the testing protocols in place for reporting abnormal results [1,2]. Each algorithm has advantages and disadvantages, but all have a significant recall rate due to false positive results [2,9]. The false positive results obtained in newborn screening for CH are mainly due to the variability of T4 and TSH depending on time of specimen collection and prematurity. Due to the immaturity of the hypothalamic-pituitary-thyroid axis, preterm infants have smaller increases in serum TSH and free T4 than do term infants leading to a disproportionate number of false positive results for preterm infants who are tested by an algorithm that includes T4. In addition to time of specimen collection, birth weight, and prematurity, other factors that could influence T4 and TSH values include ethnicity, sex, maternal thyroid disease, maternal iodine status, and medication [11,12,13].
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