Preoperative heart rate and myocardial injury after non-cardiac surgery: results of a predefined secondary analysis of the VISION study
Bibliographic record
Abstract
BACKGROUND: Increased baseline heart rate is associated with cardiovascular risk and all-cause mortality in the general population. We hypothesized that elevated preoperative heart rate increases the risk of myocardial injury after non-cardiac surgery (MINS). METHODS: We performed a secondary analysis of a prospective international cohort study of patients aged ≥45 yr undergoing non-cardiac surgery. Preoperative heart rate was defined as the last measurement before induction of anaesthesia. The sample was divided into deciles by heart rate. Multivariable logistic regression models were used to determine relationships between preoperative heart rate and MINS (determined by serum troponin concentration), myocardial infarction (MI), and death within 30 days of surgery. Separate models were used to test the relationship between these outcomes and predefined binary heart rate thresholds. RESULTS: Patients with missing outcomes or heart rate data were excluded from respective analyses. Of 15 087 patients, 1197 (7.9%) sustained MINS, 454 of 16 007 patients (2.8%) sustained MI, and 315 of 16 037 patients (2.0%) died. The highest heart rate decile (>96 beats min(-1)) was independently associated with MINS {odds ratio (OR) 1.48 [1.23-1.77]; P<0.01}, MI (OR 1.71 [1.34-2.18]; P<0.01), and mortality (OR 3.16 [2.45-4.07]; P<0.01). The lowest decile (<60 beats min(-1)) was independently associated with reduced mortality (OR 0.50 [0.29-0.88]; P=0.02), but not MINS or MI. The predefined binary thresholds were also associated with MINS, but more weakly than the highest heart rate decile. CONCLUSIONS: Preoperative heart rate >96 beats min(-1) is associated with MINS, MI, and mortality after non-cardiac surgery. This association persists after accounting for potential confounding factors. CLINICAL TRIAL REGISTRATION: NCT00512109.
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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 | 0.004 | 0.000 |
| 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".