Risk factors, transcriptomics, and outcomes of myocardial injury following lower extremity revascularization
Bibliographic record
Abstract
Myocardial injury after non-cardiac surgery (MINS) is common. We investigated the incidence and outcomes of MINS, and mechanistic underpinnings using pre-operative whole blood gene expression profiling in a prospective cohort study of individuals undergoing lower extremity revascularization (LER) for peripheral artery disease (PAD). Major adverse cardiovascular and limb events (MACLE) were defined as a composite of death, myocardial infarction, stroke, major lower extremity amputation or reoperation. Among 226 participants undergoing LER, MINS occurred in 53 (23.5%). Patients with MINS had a greater incidence of major adverse cardiovascular events (49.1% vs. 22.0%, adjusted HR 1.87, 95% CI 1.07-3.26) and MACLE (67.9% vs. 44.5%; adjusted HR 1.66, 95% CI 1.08-2.55) at median 20-month follow-up. Pre-operative whole blood transcriptome profiling of a nested matched MINS case-control cohort (n = 41) identified upregulation of pathways related to platelet alpha granules and coagulation in patients who subsequently developed MINS. Thrombospondin 1 (THBS1) mRNA expression was 60% higher at baseline in patients who later developed MINS, and was independently associated with long-term cardiovascular events in the Duke Catheterization Genetics biorepository cohort. In conclusion, pre-operative THBS1 mRNA expression is higher in patients who subsequently develop MINS and is associated with incident cardiovascular events. Pathways related to platelet activity and coagulation associated with MINS provide novel insights into mechanisms of myocardial injury.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".