Angiotensin converting enzyme inhibitor induced angio‐oedema: a review of the pathophysiology and risk factors
Bibliographic record
Abstract
Angio-oedema (AE) is a known adverse effect of angiotensin converting enzyme inhibitor (ACE-I) therapy. Over the past several decades, evidence of failure to diagnose this important and potentially fatal reaction is commonly found in the literature. Because this reaction is often seen first in the primary care setting, a review was undertaken to analyse and document the keys to both diagnostic criteria as well as to investigate potential risk factors for ACE-I AE occurrence. A general review of published literature was conducted through Medline, EMBASE, and the Cochrane Database, targeting ACE-I-related AE pathomechanism, diagnosis, epidemiology, risk factors, and clinical decision making and treatment. The incidence and severity of AE appears to be on the rise and there is evidence of considerable delay in diagnosis contributing to significant morbidity and mortality for patients. The mechanism of AE due to ACE-I drugs is not fully understood, but some genomic and metabolomic information has been correlated. Additional epidemiologic data and clinical treatment outcome predictors have been evaluated, creating a basis for future work on the development of clinical prediction tools to aid in risk identification and diagnostic differentiation. Accurate recognition of AE by the primary care provider is essential to limit the rising morbidity associated with ACE-I treatment-related AE. Research findings on the phenotypic indicators relevant to this group of patients as well as basic research into the pathomechanism of AE are available, and should be used in the construction of better risk analysis and clinical diagnostic tools for ACE-I AE.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".