Prevalence of vascular complications in Ehlers-Danlos syndrome: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Ehlers-Danlos Syndrome (EDS) comprises connective tissue disorders associated with increased vascular complication risks. This meta-analysis assesses the prevalence of vascular complications in among patients with EDS. METHODS: The review was conducted following PRISMA guidelines. A comprehensive literature search was conducted in PubMed, Embase, and Web of Science until November 2024. Observational studies reporting vascular complications in EDS were included. Data extraction included demographics, complication types, and study design, and quality assessment was evaluated using the modified Newcastle-Ottawa Scale (NOS). Random-effects models and I² statistics assessed heterogeneity, while Doi plots evaluated publication bias. RESULTS: Of the 1,772 articles screened, 12 met the inclusion criteria, reporting various vascular complications in EDS. The overall pooled prevalence of vascular complications was 30.03% (95% CI: 15.00-51.07%). The prevalence for the vEDS subtype was 42.36% (95% CI: 12.63-78.88%), for unspecified EDS was 18.65% (95% CI: 5.38-48.03%), and for hEDS was 19.77% (95% CI: 15.09-25.16%). Sensitivity analyses confirmed the stability of the pooled prevalence estimates, and DOI plots indicated minimal publication bias. CONCLUSIONS: This review highlights the high risk of vascular complications in vEDS, with moderate involvement in other EDS subtypes. Regular vascular monitoring, especially in vEDS, is crucial for early detection and intervention. Standardized diagnostic protocols and further research into genetic factors are needed to improve management strategies.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".