The Value of Epicardial Adipose Tissue for Patients Treated with Percutaneous Coronary Intervention: A Systemic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Recently, some clinical researches have shown epicardial adipose tissue to play a pivotal role in prognosis for patients treated with percutaneous coronary intervention (PCI), but the results are still controversial. A systematic review and meta-analysis was conducted to investigate the value of epicardial adipose tissue for the prognosis of patients treated with PCI. METHOD: A systematic search was performed using PubMed, Web of Science, and the Cochrane Library for studies evaluating the association of EAT and patients treated with PCI published up to January 2023. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the studies. Meta-analysis was performed using Review Manager V.5.3. RESULT: Thirteen studies enrolling 3683 patients were eventually included in our study. The thickness or volume of EAT measured were significantly higher in the ISR group compared to those in the non-ISR group (the standard mean difference of 0.34, 95% CI, 0.18-0.49, p<0.0001; I2=36%). The incidence of no-reflow was significantly higher in the thicker EAT group compared to the thin EAT group (pooled relative ratio 1.52, 95% CI 1.29-1,80, p<0.0001; I2 =0%). Thicker EAT was significantly associated with MACEs (pooled relative ratio 1.50, 95% 1.18-1.90, p=0.008). A lower EAT volume was associated with larger infarct size in STEMI patients treated with primary PCI (standard mean difference -5.45, 95% CI -8.10, -2.80; p<0.0001; I2=0%). CONCLUSION: In summary, our systemic review and meta-analysis suggests that high EAT is related to a significantly increased risk of non-reflow, MACEs, and decreased infarct size in patients with CAD treated with PCI. This paradox phenomenon demonstrates that the quality of EAT may play a more important role than the sole thickness or volume of EAT.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| 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".