Optimizing TREC- and KREC-based newborn screening: Risk-stratified algorithms significantly reduce referrals
Maarja Soomann1, Seraina Prader1, Susanna Sluka2
1Division of Immunology and The Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland.
Summary
Improving newborn screening (NBS) with risk-stratified algorithms using T-cell receptor excision circles (TREC) and kappa-deleting recombination excision circles (KREC) reduces unnecessary referrals. Integrating clinical data enhances specificity while maintaining detection of primary immunodeficiencies.
Area of Science:
- Immunology
- Genetics
- Pediatrics
Background:
- Newborn screening (NBS) uses T-cell receptor excision circles (TREC) and kappa-deleting recombination excision circles (KREC) to detect T- and B-cell lymphopenia.
- Current TREC/KREC assays have limited specificity, leading to unnecessary referrals and delayed diagnoses.
Purpose of the Study:
- To compare risk-stratification strategies for NBS to reduce referrals without missing cases.
- To evaluate the impact of integrating clinical data into TREC/KREC algorithms.
Main Methods:
- Modeled TREC/KREC NBS algorithms using a 6-year Swiss NBS dataset.
- Applied stratification approaches including cut-off adjustments and integration of gestational age (GA), postmenstrual age (PMA), and clinical data.
Main Results:
- Lowering cut-offs reduced abnormal results by 42% (TREC) and 64% (KREC).
- An optimized TREC algorithm reduced referrals by 61% but missed some T-cell lymphopenias.
- A KREC algorithm incorporating clinical data reduced referrals >10-fold, identifying most agammaglobulinemia cases.
Conclusions:
- Risk-stratified, multistep NBS algorithms using clinical data significantly reduce unnecessary referrals.
- Algorithmic adjustments for KREC improve specificity with minimal diagnostic loss, enhancing NBS effectiveness.
