Systematic screening for primary immunodeficiencies in patients hospitalized for severe infection in pediatric intensive care unit
Bibliographic record
Abstract
Over 500 primary immunodeficiency diseases (PID) have been described, but immunological assessment after a severe infection is not routine. We aimed to evaluate the feasibility of a PID screening protocol and calculate PID prevalence in children admitted for severe infection in a pediatric intensive care unit (PICU). This monocentric retrospective study evaluated the feasibility of a PID monitoring protocol after severe infection in children aged 1 month to 16 years-old hospitalized in the Montpellier University Hospital from January 2018 to December 2020. Follow-up consultations at 3 and 12 months included the three main PID screening scores, comprehensive immunological and genetic screenings. Among 1125 children admitted to the PICU, 46 had severe infections and caused by bacterial (48%), viral (39%) or fungal (2%) pathogens. Before infection, none had completed any screening score recommended by dedicated societies (Jeffrey Modell Foundation, German Patients' Organization for Primary Immunodeficiencies, French Reference Center for Hereditary Immunodeficiencies). At 3 months, three patients had a PID diagnosis (6.5% prevalence, 95% CI 1.4-17.9). These were associated with a deletion of chromosomal region 22q11.21 (DiGeorge syndrome), ELANE mutation (Elastase deficiency or Severe Congenital Neutropenia 1), and C5 deficiency Forty children (87%) presented immunological anomalies without a formal PID diagnosis. These persisted in only 4/17 children tested at 12 months. The most frequent abnormalities were low NK lymphocytes (41.18%), and abnormal B lymphocyte population distribution (25%). The observed PID prevalence post-severe infection matches previous reports, even with a high rate of viral infections, often overlooked. Systematic PID investigation after severe infection, regardless of the pathogen, should be implemented to improve early detection and treatment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".