Incidence of adverse events related to intravenous immunoglobulin therapy in children
Bibliographic record
Abstract
BACKGROUND: Intravenous immunoglobulin (IVIG) therapy is used in the treatment of pediatric diseases, although data about IVIG-related adverse events (IVIG-AEs) are limited. Objectives of this study were to document the incidence of IVIG-AEs in pediatric hospitalized patients and to identify risk factors for IVIG-AEs. METHODS: This retrospective cohort study included patients <18 years old who received IVIG therapy while admitted at a Canadian pediatric tertiary care center between 2016 and 2020. Patients and IVIG-perfusions characteristics were collected, as well as IVIG-AEs. Bivariate and multivariable logistic regressions were used to explore predictors of IVIG-AEs. RESULTS: We included 228 children, totaling 478 IVIG perfusions. Indications included treatment for inflammatory (52.6%), autoimmune disorders (35.5%), and immunoglobulin replacement (11.8%). A total of 213 IVIG-AEs were reported. Fever (13.6%) and headache (6.7%) were the most frequent IVIG-AEs. Most IVIG-AEs were mild (57%) or moderate (31%) in severity, but 12% were severe reactions. The following factors were predictive of IVIG-AEs in univariate analyses: older age (OR 1.14 [95% CI: 1.07-1.21]), dehydration (OR 2.55 [95% CI: 1.43-4.55]), concurrent allergies (OR 2.87 [95% CI: 1.26-6.56]), first perfusion (OR 1.53 [95% CI: 1.02-2.30]), and higher dosage (OR 2.14 [95% CI: 1.39-3.33]). Concurrent steroids decreased the risk of IVIG-AEs (OR 0.43 [95% CI: 0.19-0.96]). Older age and higher IVIG dose remained independent predictors of IVIG-AEs in multivariable analyses. CONCLUSIONS: Mild IVIG-AEs are frequent in children, and serious reactions may occur. Prospective studies are needed to confirm risk factors for IVIG-AEs and to evaluate how to best prevent them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".