Diagnostic Utility of Biomarkers in Anaphylaxis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Anaphylaxis is a life-threatening allergic reaction commonly triggered by food, venom, or drugs. Clinical criteria are central to diagnosing anaphylaxis. However, laboratory biomarkers could provide valuable confirmation when clinical diagnosis is challenging. OBJECTIVE: We aimed to evaluate key biomarkers including tryptase, histamine, platelet-activating factor (PAF), PAF-acetylhydrolase (PAF-AH), and urinary prostaglandin D2 (PGD2) for their diagnostic utility in anaphylaxis. METHODS: A systematic review was conducted following PRISMA-DTA (Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy studies) guidelines. Studies published between 2004 and 2024 from Embase and MEDLINE were included if they evaluated the diagnostic test accuracy of tryptase, histamine, PAF, PAF-AH, or urinary PGD2 in confirmed anaphylaxis cases. Pooled sensitivity and specificity estimates were calculated using the diagmeta package in R. RESULTS: Twenty-eight studies with 18,749 patients were included, of whom 3329 had anaphylaxis. Tryptase was the most frequently studied biomarker (24 studies), with a pooled sensitivity and specificity of 0.49 and 0.82, respectively. Histamine had a pooled sensitivity of 0.76 and a specificity of 0.69. Limited data were available for PAF, PAF-AH, and urinary PGD2. CONCLUSIONS: Studies suggest that tryptase remains the most widely used and accessible biomarker for diagnosing anaphylaxis mainly using the "Rule of Twos" diagnosis strategy. Histamine and urinary PGD2 show potential, though their application is limited by practical challenges. Further research is needed to establish the diagnostic roles of PAF and PAF-AH, particularly in non-IgE-mediated anaphylaxis pathways.
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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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.042 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".