Prognostic value of left ventricular systolic function before vascular surgery: a systematic review
Bibliographic record
Abstract
BACKGROUND: Vascular surgery carries a high risk of post-operative cardiac complications. Recent studies have shown an association between asymptomatic left ventricular systolic dysfunction and increased risk of major adverse cardiovascular events (MACE). This systematic review aims to evaluate the prognostic value of left ventricular function as determined by left ventricular ejection fraction (LVEF) measured by resting echocardiography before vascular surgery. METHODS: This review conformed to PRISMA and MOOSE guidelines. PubMed, OVID Medline and Cochrane databases were searched from inception to 27 October 2022. Eligible studies assessed vascular surgery patients, with multivariable-adjusted or propensity-matched observational studies measuring LVEF via resting echocardiography and providing risk estimates for outcomes. The primary outcomes measures were all-cause mortality and congestive heart failure at 30 days. Secondary outcome included the composite outcome MACE. RESULTS: Ten observational studies were included (4872 vascular surgery patients). Studies varied widely in degree of left ventricular systolic dysfunction, symptom status, and outcome reporting, precluding reliable meta-analysis. Available data demonstrated a trend towards increased incidence of all-cause mortality, congestive heart failure and MACE in patients with pre-operative LVEF <50%. Methodological quality of the included studies was found to be of moderate quality according to the Newcastle Ottawa Checklist. CONCLUSION: The evidence surrounding the prognostic value of LVEF measurement before vascular surgery is currently weak and inconclusive. Larger scale, prospective studies are required to further refine cardiac risk prediction before vascular surgery.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".