Intravenous Immunoglobulin Use In Critically Ill Children
Bibliographic record
Abstract
Purpose: The use of intravenous immunoglobulins (IVIG) has increased significantly in the last decade causing challenges for blood suppliers to respond to the demand. Indications for which IVIG infusion should be given to critically ill children remain unclear. The objective of this study is to characterize the epidemiology of IVIG use in this population. Methods: We performed a single-center retrospective cohort study of all patients aged between 3 days and 18 years who received at least one IVIG infusion while hospitalized in the pediatric intensive care unit of the Centre hospitalier universitaire (CHU) Sainte-Justine, Montréal Quebec (Canada) between January 1, 2013 and December 31, 2018. Results: One hundred and seventy-two patients received a total of 342 IVIG infusions over the study period. Most common indications for IVIG infusions were staphylococcal or streptococcal toxic shock syndrome (n=53/342, 15.5%), immunoglobulin replacement in chylothorax (n=37/342, 10.9%), prophylaxis following bone marrow transplantation (n=31/342, 9.1%), myocarditis (n=25/342, 7.3%) and post-solid organ transplant complications (n=21/342, 6.1%). The median dose of IVIG per infusion was 0.95 g/kg (IQR 0.5-1.0) and median number of IVIG infusions per patient was one (IQR: 1-2). Seventy-nine percent of IVIG infusions given were administrated for off-label indications with regards to Health Canada recommendations. Conclusion: This study identified the most common indications for IVIG infusion in critically ill children in a tertiary care pediatric intensive care unit. Given the costs, the known adverse events associated with IVIG and the pressure that blood suppliers are facing to meet the demands, clinical trials are needed to evaluate the efficacy and safety of IVIG in conditions where use is significant.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".