2023
DOI: 10.3390/ijns9040063
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NBSTRN Tools to Advance Newborn Screening Research and Support Newborn Screening Stakeholders

Kee Chan,
Zhanzhi Hu,
Lynn W. Bush
et al.

Abstract: Rapid advances in the screening, diagnosis, and treatment of genetic disorders have increased the number of conditions that can be detected through universal newborn screening (NBS). However, the addition of conditions to the Recommended Uniform Screening Panel (RUSP) and the implementation of nationwide screening has been a slow process taking several years to accomplish for individual conditions. Here, we describe web-based tools and resources developed and implemented by the newborn screening translational … Show more

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Cited by 8 publications
(9 citation statements)
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References 20 publications
(23 reference statements)
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“…The refined list of CDEs was provided to clinicians through the Research Electronic Data Capture (REDCap ® ) web application. REDCap ® is widely used by academic, public health, clinical, and research teams to collect health information [ 22 , 23 , 24 , 25 , 26 , 27 ]. Clinicians provided an initial user testing of the tool, and feedback from this testing phase was collected.…”
Section: Methodsmentioning
confidence: 99%
“…The refined list of CDEs was provided to clinicians through the Research Electronic Data Capture (REDCap ® ) web application. REDCap ® is widely used by academic, public health, clinical, and research teams to collect health information [ 22 , 23 , 24 , 25 , 26 , 27 ]. Clinicians provided an initial user testing of the tool, and feedback from this testing phase was collected.…”
Section: Methodsmentioning
confidence: 99%
“…Research indicates that the ongoing management of persons identified through newborn screening, along with the continuous collection of health information, are critical foundations for an effective newborn screening system in clinical settings. The Newborn Screening Translational Research Network (NBSTRN) has established a consensus-driven approach involving clinical care specialists to produce, maintain, and enhance sets of questions and answers grouped into common data elements (CDEs) [ 202 ]. This endeavor led to the identification of key data components for the long-term follow-up (LTFU) initiatives carried out by state NBS programs.…”
Section: Longitudinal Studies For Outcome Assessmentmentioning
confidence: 99%
“…Low [11] High [12] Relative public acceptability High [13] Mixed [14][15][16] Opportunities DBS 3 for retrospective epidemiological studies [17] Building on existing NBS workflows [18][19][20] Closer to phenotype [21,22] Ability to screen multiple conditions at once [23] Possibility of new biomarker discovery [24,25] Applicable to any condition type as a single test [26][27][28][29] Up-front molecular diagnosis [30][31][32] Lifetime re-use of data (WGS) [31,33] Enabling research into genedisease associations, treatment developments, population variation, and pharmacogenetic variation [31,[33][34][35][36] Challenges Feature characterization and data interpretation [37,38] May not be suitable for all condition types [23] Custom validation for each targeted condition [39][40][41] Need for sufficiently large validation cohorts [42] Consensus of which genes/variants to report [28,29,43] Possibility of identifying adultonset conditions/variants [44][45]…”
Section: Comparative Screening Costmentioning
confidence: 99%