Severity of SARS-CoV-2 infection in children with inborn errors of immunity (primary immunodeficiencies): a systematic review
Bibliographic record
Abstract
BACKGROUND: Inborn errors of immunity (IEIs) are considered significant challenges for children with IEIs, their families, and their medical providers. Infections are the most common complication of IEIs and children can acquire coronavirus disease 2019 (COVID-19) even when protective measures are taken. OBJECTIVES: To estimate the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children with IEIs and analyse the demographic parameters, clinical characteristics and treatment outcomes in children with IEIs with COVID-19 illness. METHODS: For this systematic review, we searched ProQuest, Medline, Embase, PubMed, CINAHL, Wiley online library, Scopus and Nature through the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guideline for studies on the development of COVID-19 in children with IEIs, published from December 1, 2019 to February 28, 2023, with English language restriction. RESULTS: Of the 1095 papers that were identified, 116 articles were included in the systematic review (73 case report, 38 cohort 4 case-series and 1 case-control studies). Studies involving 710 children with IEIs with confirmed COVID-19 were analyzed. Among all 710 IEIs pediatric cases who acquired SARS-CoV-2, some children were documented to be admitted to the intensive care unit (ICU) (n = 119, 16.8%), intubated and placed on mechanical ventilation (n = 87, 12.2%), suffered acute respiratory distress syndrome (n = 98, 13.8%) or died (n = 60, 8.4%). Overall, COVID-19 in children with different IEIs patents resulted in no or low severity of disease in more than 76% of all included cases (COVID-19 severity: asymptomatic = 105, mild = 351, or moderate = 88). The majority of children with IEIs received treatment for COVID-19 (n = 579, 81.5%). Multisystem inflammatory syndrome in children (MIS-C) due to COVID-19 in children with IEIs occurred in 103 (14.5%). Fatality in children with IEIs with COVID-19 was reported in any of the included IEIs categories for cellular and humoral immunodeficiencies (n = 19, 18.6%), immune dysregulatory diseases (n = 17, 17.9%), innate immunodeficiencies (n = 5, 10%), bone marrow failure (n = 1, 14.3%), complement deficiencies (n = 1, 9.1%), combined immunodeficiencies with associated or syndromic features (n = 7, 5.5%), phagocytic diseases (n = 3, 5.5%), autoinflammatory diseases (n = 2, 3%) and predominantly antibody deficiencies (n = 5, 2.5%). Mortality was COVID-19-related in a considerable number of children with IEIs (29/60, 48.3%). The highest ICU admission and fatality rates were observed in cases belonging to cellular and humoral immunodeficiencies (26.5% and 18.6%) and immune dysregulatory diseases (35.8% and 17.9%) groups, especially in children infected with SARS-CoV-2 who suffered severe combined immunodeficiency (28.6% and 23.8%), combined immunodeficiency (25% and 15%), familial hemophagocytic lymphohistiocytosis (40% and 20%), X-linked lymphoproliferative diseases-1 (75% and 75%) and X-linked lymphoproliferative diseases-2 (50% and 50%) compared to the other IEIs cases. CONCLUSION: Children with IEIs infected with SARS-CoV-2 may experience higher rates of ICU admission and mortality in comparison with the immunocompetent pediatric populations. Underlying immune defects does seem to be independent risk factors for severe SARS-CoV-2 infection in children with IEIs, a number of children with SCID and CID were reported to have prolonged infections-though the number of patients is small-but especially immune dysregulation diseases (XLP1 and XLP2) and innate immunodeficiencies impairing type I interferon signalling (IFNAR1, IFNAR2 and TBK1).
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".