A retrospective analysis of haemolytic reactions to intravenous immunoglobulin using data from the Transfusion‐Transmitted Injuries Surveillance System (Ontario)
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Haemolysis can occur following intravenous immunoglobulin (IVIG) infusion. Haemovigilance data were analysed using a novel approach for including two control groups with no haemolysis to IVIG. Objectives included a summary of all reactions to IVIG, rate estimates and analysis of haemolytic reactions including risk factors. MATERIALS AND METHODS: Canadian haemovigilance data from Ontario (2013-2021), IVIG distribution and transfusion data from the blood supplier, and data from a large local transfusion registry were used. An 'other-reactions' control group included patients with IVIG reactions that were not haemolytic, and registry patients with no-reaction were the 'no-reaction controls'. Descriptive analysis and two logistic regression models for the different control groups were performed. RESULTS: One thousand one hundred and seventy reactions were included. Most common were febrile non haemolytic (26.1%), minor allergic (24.5%) and IVIG headache (15.3%) followed by haemolytic 10.9% (128/1170). Haemolytic reaction rates decreased over time: rates since 2020 estimated between 1.5 and 2.9/1000 kg IVIG used. The regression model for other-reaction controls identified two risk factors for haemolysis: non-O blood group recipients compared with group O recipients (p value = 0.0106) and IVIG dose per 10 g increase (OR 1.359; 95% CI 1.225-1.506). The model using no-reaction controls gave similar results and also showed no pre-medication was associated with a higher risk of haemolysis (OR 29.084; 95% CI 1.989-425.312). CONCLUSION: The frequency of haemolytic reactions has decreased over time. We confirmed non-O blood group recipients and IVIG dose as risk factors for haemolysis and raise the hypothesis that no pre-medication may increase the risk of haemolysis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".