Systematic Data Collection to Inform Policy Decisions: Integration of the Region 4 Stork (R4S) Collaborative Newborn Screening Database to Improve MS/MS Newborn Screening in Washington State

Abstract

In the past 50 years, newborn screening (NBS) has grown significantly in the breadth of screening programs and the number of conditions tested for each baby. The adaptation of tandem mass spectrometry (MS/ MS) technology to detect inherited metabolic diseases is arguably one of the most impactful advancements in NBS testing. The addition of new conditions to the screening panel and the rarity of these conditions pose challenges for NBS program development, improvement, and evaluation. The Region 4 Stork (R4S) project is an international collaborative NBS database and a resource for programs across the world to overcome these challenges. By pooling true-positive case and laboratory testing data, the R4S database provides insight into complex MS/MS profiles for these rare conditions. The Washington State NBS Program is integrating aspects of the R4S web application and utilizing R4S resources to examine current protocols, identify improvements, implement changes, and review outcomes. Washington uses R4S resources to choose informative analytes and evaluate cutoffs. The program also examines the performance of R4S tools that are designed to aid in evaluating a baby's MS/MS screening results. This article documents these efforts in utilizing a subset of the R4S tools to improve their program, demonstrating the flexibility of the application. Other NBS programs can use the knowledge Washington has gained to strengthen their ability to correctly identify babies with metabolic disorders and mitigate the impact of screening on babies and their families.

Independent Component Analysis for Compositional Data

“…In order to demonstrate the above described methods, the data from a neonatal screening program in the Czech Republic was analyzed. Anonymous data were obtained from a retrospective study approved by the Ethics Committee of the University Hospital Olomouc which was part of a larger international study described in [32]. Newborn screening is a preventive program that allows for an early detection of a selected spectrum of inborn metabolic diseases.…”

Is Our Newborn Screening Working Well? A Literature Review of Quality Requirements for Newborn Blood Spot Screening (NBS) Infrastructure and Procedures

“…However, because of the rarity of the diseases screened, data for a sufficient number of confirmed cases to achieve optimal adjustments of all cut-off values are not always available to each laboratory or even to entire countries. Therefore, in recent years, post-analytical multivariate digital interpretation tools that combine data from numerous laboratories, such as the US-based “Collaborative Laboratory Integrated Reports” (CLIR) [ 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 ] or similar procedures [ 92 , 109 , 110 , 111 ], have been increasingly used worldwide with the aim of achieving “precision NBS” with “near-zero false positive rates” [ 107 ]. These tools are based on big-data analyses with machine-learning modelling from the digital reports of test results, covariates (such as gestational age, birth weight, and age at blood collection), and final diagnoses.…”

“…Our results illustrate that the challenges of NBS infrastructure are complex as NBS goes far beyond a “simple blood test”, and these organisational and ethical challenges can hardly be overcome without systematic coordination and quality management [ 2 , 5 , 7 , 21 , 31 , 36 , 81 , 174 , 180 , 182 , 183 ]. The potential offered by digital systems to facilitate, accelerate, and secure the NBS process were particularly highlighted [ 2 , 20 , 49 , 92 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 164 , 171 , 186 , 187 , 188 ]. In our view, the aspects presented—although partly seeming rather formal and less fascinating than new perspectives—are essential for existing NBS programmes and grow in importance with further NBS expansions.…”

Utilizing augmented artificial intelligence for aminoacidopathies using collaborative laboratory integrated reporting- A cross-sectional study

“…CLIR tools are meant to recognize the different patterns in PAA to improve sensitivity and specificity. This post-analytical software does not rely on the traditional definition of “abnormal” as merely a deviation from a normal reference range [ 18 , 19 ]. Rather, it places patients within condition-specific disease ranges and evaluates how consistent a result is with the analyte disease range established separately for each condition [ 10 , 16 ].…”