Assessment of Vascular Endothelial Dysfunction in Septic Patients Using Brachial Flow-Mediated Dilation: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background/Objective: Sepsis remains a major cause of morbidity and mortality worldwide, making early risk stratification and prognosis critical. Vascular endothelial dysfunction is a hallmark of sepsis pathogenesis, with evidence suggesting that endothelial injury may occur early, preceding organ failure. Brachial flow-mediated dilation (FMD), a validated noninvasive ultrasound technique measuring endothelium-dependent vasodilation, serves as a surrogate marker of endothelial function, where lower FMD values reflect impaired function. This systematic review and meta-analysis aimed to evaluate the validity and quality of evidence on using FMD to measure vascular endothelial dysfunction in septic patients by comparing FMD (i) between septic patients and non-septic controls and (ii) between sepsis non-survivors and survivors. Methods: PubMed, Embase, Scopus, and Web of Science were searched until November 2024 for clinical studies assessing FMD in septic patients. A random-effects model was used for the meta-analysis, and quality of studies was assessed using the Newcastle–Ottawa Scale. Results: Eight studies were included, and seven underwent quantitative synthesis (385 septic patients, 106 non-survivors and 217 survivors). Compared with non-septic controls, septic patients demonstrated significantly lower FMD (pooled standardized mean difference (SMD) = −2.1617; 95% CI −3.8349 to −0.4885; p = 0.0113; I2 = 98.2939%). Within the sepsis cohort, non-survivors showed significantly attenuated FMD compared to survivors (pooled SMD = −0.7003; 95% CI −1.1133 to −0.2873; p = 0.001; I2 = 60.5593%). Conclusions: FMD shows potential as a surrogate marker of endothelial dysfunction for sepsis risk assessment, as evident by lower FMD in septic patients, particularly non-survivors.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".