85 Incidence of Intravenous Immunoglobulins-related Adverse Events in Children
Bibliographic record
Abstract
Abstract Background Intravenous immunoglobulins (IVIG) therapy is increasingly used in the treatment of various paediatric diseases. Data about frequency and underlying mechanisms of IVIG-related adverse events (IVIG-AEs) in children are limited. Objectives Objectives of this study were to document the incidence of IVIG-AEs in paediatric hospitalized patients and identify risk factors for IVIG-AEs. Design/Methods In this retrospective cohort study, we included patients aged < 18 years old who received IVIG therapy, for any indication, as inpatients at a Canadian paediatric tertiary care centre between 2016-2020. Patients and IVIG-perfusion characteristics were collected, as well as any AEs occurring during the perfusion or up to 10 days after. AEs were graded according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. Bivariate and multivariate logistic regressions were used to explore the associations between patients’ characteristics and the incidence of IVIG-AEs. Results We included 228 children in this study (mean age: 4 years), receiving 478 IVIG infusions. Indications for IVIG infusions included inflammatory (120/228, 52.6%), replacement (27/228, 11.8%) and auto-immune (81/228, 35.5%) disorders. A total of 211 IVIG-AEs were reported in 89/228 (39%) of patients, and 125/478 (26.2%) of infusions. Fever (65/478, 13.6%) was the most frequent AE, followed by headache (32/478, 6.7%) and tachycardia (31/478, 6.5%). Most IVIG-AEs were of mild (121/211, 57%) or moderate (66/211, 31%) severity, but 12% (24/211) were severe reactions (serious AEs or grade 3-4), and no fatal reactions (grade 5) were encountered. The highest proportion of IVIG-AEs occurred when administered for inflammatory conditions. Older age, dehydration, first infusion, higher IVIG dosage and concurrent allergies were significantly associated with IVIG-AEs, while treatment with a steroid was significantly associated with a reduced risk of AEs. Conclusion IVIG-AEs are frequent in children. While most of them are self-limited, more serious complications may occur. Adequate hydration, low concentrations of IVIG products and the administration of glucocorticoid prior to the infusion could minimize these AEs. Further research is necessary to prevent IVIG-AEs in high-risk children.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".