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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".