Estimating the rate of acute adverse reactions to non-ionic low-osmolar contrast media: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: This systematic review and meta-analysis aimed to assess and compare acute adverse reactions (AAR) rates among non-ionic low-osmolar contrast media (LOCM), examining administration routes and severity-specific impact on AAR rates. MATERIALS AND METHODS: A PubMed and Cochrane Library search identified studies published between January 1989 and March 2024. Inclusion criteria focused on studies with > 100 adult patients who received intra-arterial or intravenous LOCM (iobitridol, iohexol, iomeprol, iopamidol, iopromide, and ioversol). Duplicate reports and studies with insufficient information were excluded. Data extraction and quality assessment followed PRISMA guidelines and the Newcastle Ottawa Scale. Statistical analyses were performed using R software, including random effects, meta-regression, and sub-group analysis. RESULTS: After excluding duplicates and non-compliant studies, 32 peer-reviewed articles of initially 6701 identified studies, were included in the final analysis. The pooled overall AAR rate was 0.73%, with ioversol showing the lowest rate (0.34%). From all studies, pooled rates (random effects model) of moderate and severe AARs were 0.10% and 0.014% (p < 0.01), with the lowest rates for iohexol (0.05% and 0.008%, respectively). The highest overall, moderate, and severe AAR rates were seen with iomeprol (1.38%, 0.27%, and 0.040%, respectively). LOCM type (p < 0.0001), study design (p = 0.0001), and injection route (p = 0.034) significantly influenced the overall AAR rate. In contrast, the study center number (p = 0.698), the country where the study was performed (p = 0.808), and the type of reaction (hypersensitivity vs hypersensitivity plus physiological reactions; p = 0.178) did not. CONCLUSION: AAR rates were low but indicated significant differences between LOCM; iohexol and ioversol demonstrated the overall most favorable safety profiles. KEY POINTS: Question Knowledge about AAR is crucial for patient safety, but comprehensive data on the safety profiles of non-ionic LOCM is lacking. Findings Ioversol showed the lowest overall AAR rate; iohexol demonstrated the lowest moderate/severe AAR. Study design, LOCM type, and injection route influenced AAR rates. Clinical relevance This meta-analysis provides evidence for differences in non-ionic LOCM safety profiles, particularly for moderate and severe AARs. These can guide clinicians in selecting contrast agents, aiming to further reduce risks, and improve patient safety in diagnostic imaging.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".