Polygenic Risk Scores in Myocardial Injury After Noncardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Myocardial injury after noncardiac surgery (MINS) is the most prevalent vascular complication following surgical procedures. Although the revised cardiac risk index (RCRI) is widely used to predict postoperative cardiovascular complications, its predictive accuracy is suboptimal. OBJECTIVES: Considering genetic influences may improve risk prediction. The authors propose integrating polygenic risk scores (PRS) with the RCRI to enhance MINS prediction. Identification of PRS associated with MINS could provide pathophysiological insights. METHODS: This is a case-control study nested within the Vascular Events in Noncardiac Surgery Participants Cohort Evaluation cohort, including patients aged 45 and above who underwent noncardiac surgery. Daily troponin levels were measured preoperatively and on days 1, 2, and 3 postoperatively. PRS was computed for MINS risk factors using publicly available summary statistics. Logistic regression models were used to assess the association between each PRS and MINS. PRS discrimination was assessed independently and in combination with RCRI. RESULTS: = 506). The type II diabetes (T2D) PRS (OR: 1.26; 95% CI: 1.00-1.58; P = 0.047) and the HbA1c PRS (OR: 1.26; 95% CI: 1.03-1.54; P = 0.026) were associated with MINS. No other PRS, including those for coronary artery disease, stroke, and lipid biomarkers, showed significant associations. CONCLUSIONS: The T2D PRS and the HbA1c PRS were associated with an increased risk of MINS. The findings may reflect the multifactorial pathophysiology of MINS. Larger genetic studies and trials evaluating perioperative glucose management warrant consideration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".