, Melissa Yssel, MB ChB, FC Path(SA) Chem 139, and Wendy M. Zakowicz, BS 79 Purpose: To achieve clinical validation of cutoff values for newborn screening by tandem mass spectrometry through a worldwide collaborative effort. Methods: Cumulative percentiles of amino acids and acylcarnitines in dried blood spots of approximately 25-30 million normal newborns and 10,742 deidentified true positive cases are compared to assign clinical significance, which is achieved when the median of a disorder range is, and usually markedly outside, either the 99th or the 1st percentile of the normal population. The cutoff target ranges of analytes and ratios are then defined as the interval between selected percentiles of the two populations. When overlaps occur, adjustments are made to maximize sensitivity and specificity taking all available factors into consideration.

BACKGROUND: Newborn screening (NBS) for inborn errors of propionate, methionine, and cobalamin metabolism relies on finding abnormal concentrations of methionine and propionylcarnitine. These analytes are not specific for these conditions and lead to frequent false-positive results. More specific markers are total homocysteine (tHCY), methylmalonic acid (MMA), and methylcitric acid (MCA), but these markers are not detected by current NBS methods. To improve this situation, we developed a method for the detection of tHCY, MMA, and MCA in dried blood spots (DBSs) by liquid chromatography-tandem mass spectrometry (LC-MS/MS).

The continued expansion of newborn screening programmes to include additional conditions increases the responsibility of newborn screening laboratories to provide testing with the highest sensitivity and specificity to allow for identification of affected patients while minimizing the false-positive rate. Some assays and analytes are particularly problematic. Over recent years, our laboratory tried to improve this situation by developing second-tier tests to reduce false-positive results in the screening for congenital adrenal hyperplasia (CAH), tyrosinaemia type I, methylmalonic acidaemias, homocystinuria, and maple syrup urine disease (MSUD). Beginning in 2004, this approach was applied to Mayo's newborn screening programme and resulted in a false-positive rate of 0.09%, a positive predictive value of 41%, and a positive detection rate of 1 affected case in 1672 babies screened.

Background: Tyrosinemia type I (TYR 1) is a disorder causing early death if left untreated. Newborn screening (NBS) for this condition is problematic because determination of the diagnostic marker, succinylacetone (SUAC), requires a separate first-tier or only partially effective second-tier analysis based on tyrosine concentration. To overcome these problems, we developed a new assay that simultaneously determines acylcarnitines (AC), amino acids (AA), and SUAC in dried blood spots (DBS) by flow injection tandem mass spectrometry (MS/MS). Methods: We extracted 3/16-inch DBS punches with 300 μL methanol containing AA and AC stable isotope-labeled internal standards. This extract was derivatized with butanol-HCl. In parallel, we extracted SUAC from the residual filter paper with 100 μL of a 15 mmol/L hydrazine solution containing the internal standard 13C5-SUAC. We combined the derivatized aliquots in acetonitrile for MS/MS analysis of AC and AA with additional SRM experiments for SUAC (m/z 155–137) and 13C5-SUAC (m/z 160–142). Analysis time was 1.2 min. Results: SUAC was increased in retrospectively analyzed NBS samples of 11 TYR 1 patients (length of storage, 52 months to 1 week; SUAC range, 13–81 μmol/L), with Tyr concentrations ranging from 65 to 293 μmol/L in the original NBS analysis. The mean concentration of SUAC in 13 521 control DBS was 1.25 μmol/L. Conclusion: The inclusion of SUAC analysis into routine analysis of AC and AA allows for rapid and cost-effective screening for TYR 1 with no tangible risk of false-negative results.

Purpose: To improve quality of newborn screening by tandem mass spectrometry with a novel approach made possible by the collaboration of 154 laboratories in 49 countries. Methods: A database of 767,464 results from 12,721 cases affected with 60 conditions was used to build multivariate pattern recognition software that generates tools integrating multiple clinically significant results into a single score. This score is determined by the overlap between normal and disease ranges, penetration within the disease range, differences between conditions, and weighted correction factors. Results: Ninety tools target either a single condition or the differential diagnosis between multiple conditions. Scores are expressed as the percentile rank among all cases with the same condition and are compared to interpretation guidelines. Retrospective evaluation of past cases suggests that these tools could have avoided at least half of 279 false-positive outcomes caused by carrier status for fatty-acid oxidation disorders and could have prevented 88% of known false-negative events. Conclusions: Application of this computational approach to raw data is independent from single analyte cutoff values. In Minnesota, the tools have been a major contributing factor to the sustained achievement of a false-positive rate below 0.1% and a positive predictive value above 60%