10 Optimizing implementation and continuous monitoring of a newborn screening program for severe combined immunodeficiency
Bibliographic record
Abstract
Abstract Background Severe Combined Immunodeficiency (SCID) is an inborn error of immunity characterized by severely low T cell levels and function. Early diagnosis is essential to prevent serious infections and improve outcomes. Newborn screening (NBS) for SCID provides a way to detect SCID and other conditions associated with T cell lymphopenia in the neonatal period. Objectives 1) Optimize the implementation of a NBS program for SCID in British Columbia (BC), Canada; and 2) create a systematic method for monitoring the diagnoses and outcomes of infants with a positive screen to facilitate continuous improvement of the NBS program. Design/Methods An interdisciplinary working group comprised of immunologists, biochemical geneticists, and hematopathologists met at regular intervals prior to and after implementing NBS for SCID in BC. We developed an algorithm for the evaluation and initial management of infants with an abnormal NBS for SCID based on literature review and local expert consensus. Using a REDCap database, we tracked the prevalence, evaluation, diagnoses and outcomes of infants with a positive NBS. The database also monitored diagnoses and outcomes of children referred to Immunology outside of the NBS program. Plan-do-study-act cycles included creation of clinical and educational guidelines on SCID, optimization of abnormal NBS cut-off values, and reflex chromosomal microarray (CMA) testing of infants with confirmed T cell lymphopenia. Results Between October 3, 2022 and June 16, 2023, 28,816 initial samples were tested, and 30 infants had a positive screen for SCID. Of the 30 infants, 10 were ultimately diagnosed with conditions associated with T cell lymphopenia. Diagnosis led to avoidance of live viral vaccines and implementation of infection precaution measures. No infants experienced complications related to live viral vaccine administration, compared to 1 case in the preceding 12 months prior to NBS implementation. In February 2023, the threshold for an abnormal NBS was adjusted to achieve the goal positive screen frequency of 0.1%. Performing reflex CMA testing on infants with confirmed T cell lymphopenia led to rapid diagnosis of chromosomal microdeletion syndromes, avoiding unnecessary second tier testing. Since implementing NBS for SCID in BC, there have been no cases of SCID diagnosed outside of the NBS program. Conclusion Establishing an interdisciplinary working group and clinical database facilitated the smooth integration and continuous improvement of NBS for SCID in BC. This model can be applied to other healthcare systems to support teams and ensure the best possible outcomes for affected patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".