Non-invasive endothelial function testing and the risk of adverse outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: We performed a systematic review and meta-analysis to understand the role of flow-mediated dilatation (FMD) of the brachial artery (BA) and peripheral arterial tonometry (PAT) in predicting adverse events, including cardiovascular (CV) events and all-cause mortality. BACKGROUND: FMD of the BA and PAT are non-invasive measures of endothelial function. Impairment of endothelial function is associated with increased CV events. While FMD is the more widely used and studied technique, PAT offers several advantages. The purpose of this systematic review and meta-analysis is to determine whether brachial FMD and PAT are independent risk factors for future CV events and mortality. METHODS: Multiple electronic databases were searched for articles relating FMD or PAT to CV events. Data were extracted on study characteristics, study quality, and study outcomes. Relative risks (RRs) from individual studies were combined and a pooled multivariate RR was calculated. RESULTS: Thirty-six studies for FMD were included in the systematic review, of which 32 studies consisting of 15, 191 individuals were meta-analysed. The pooled RR of CV events and all-cause mortality per 1% increase in brachial FMD, adjusting for potential confounders, was 0.90 (0.88-0.92). In contrast, only three studies evaluated the prognostic value of PAT for CV events, and the pooled RR per 0.1 increase in reactive hyperaemia index was 0.85 (0.78-0.93). CONCLUSION: Brachial FMD and PAT are independent predictors of CV events and all-cause mortality. Further research to evaluate the prognostic utility of PAT is necessary to compare it with FMD as a non-invasive endothelial function test in clinical practice.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".