Evidence map›Paper›PMID 42418231›Full record

ArticlePediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology2026

Optimizing TREC- and KREC-based newborn screening: Risk-stratified algorithms significantly reduce referrals.

Maarja Soomann, Seraina Prader, Susanna Sluka, Jana Pachlopnik Schmid, Johannes Trück

Abstract read
In one paragraph

Article in Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Optimizing TREC- and KREC-based newborn screening: Risk-stratified algorithms significantly reduce referrals.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Maarja SoomannDivision of Immunology and The Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-2237-5327
Seraina PraderDivision of Immunology and The Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland.
Susanna SlukaSwiss Newborn Screening Laboratory, University Children's Hospital Zurich, Zurich, Switzerland.
Jana Pachlopnik SchmidDivision of Immunology and The Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-6653-9047
Johannes TrückDivision of Immunology and The Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-0418-7381

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 320030_205097Universität Zürich
6 · The paper itself

Abstract

backgroundNewborn screening (NBS) quantifying T-cell receptor excision circles with or without kappa-deleting recombination excision circles (TREC, KREC) enables early detection of severe T- and/or B-cell lymphopenia. However, both markers have limited specificity, often resulting in unnecessary referrals. We compared the effects of alternative risk-stratification strategies to reduce referrals while avoiding missed cases and unnecessary delays.

methodsWe modeled the effects of multiple TREC and KREC-based NBS algorithms using a comprehensive real-life dataset from the first 6 years of the Swiss NBS program. Stratification approaches included adjustment of cut-offs and integration of clinical data such as gestational age (GA), postmenstrual age (PMA), inpatient vs. outpatient status, family history, and maternal immunosuppression.

resultsLowering cut-offs alone reduced abnormal results by 42% for TREC and 64% for KREC. The most efficient TREC algorithm, based on exact TREC levels and post-menstrual age in preterm infants, reduced referrals by 61% (p < .0001) but missed nearly half of non-SCID T-cell lymphopenias and delayed referral in 2/3 of outpatients who eventually required immunological evaluation. Integration of family history and clinical signs mitigated delays in some cases. For KREC, combining information on gestational age, maternal immunosuppression, and inpatient status enabled a >10-fold reduction in referrals (p < .0001) while still identifying all confirmed agammaglobulinemia cases except two with λ5 deficiency.

conclusionRisk-stratified, multistep NBS algorithms incorporating readily available clinical data can substantially reduce unnecessary referrals while preserving detection of target conditions. For KREC, simple algorithmic adjustments allow marked improvement in specificity with minimal diagnostic loss.

Indexed as

AlgorithmsB-LymphocytesLymphopeniaNeonatal ScreeningReceptors, Antigen, T-CellT-LymphocytesFemaleHumansInfant, NewbornMaleReferral and ConsultationReceptors, Antigen, T-CellagammaglobulinemiaKRECNBSNBS algorithmsnewborn screeningSCIDTREC

Identifiers

PMID42418231
PMCPMC13344459

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.