ECG changes after non-cardiac surgery: a prospective observational study in intermediate-high risk patients
Bibliographic record
Abstract
BACKGROUND: Efforts to mitigate the risk for perioperative cardiac events focus on both patient's and operation's risk and often include a preprocedural electrocardiogram (ECG). The merits of postprocedural ECG for detection of occult cardiac events occurring during surgery are unknown. We aim to explore the incidence of pre, and new postprocedural ECG pathologies in an intermediate-high risk population undergoing non-cardiac surgery. METHODS: This single-center, prospective, observational study, included patients older than 18 years with at least two cardiovascular risk factors who were scheduled for non-cardiac surgery. All patients had pre, and postprocedural ECG. The ECG was analyzed and coded according to the Minnesota criteria. A multivariable logistic regression analysis was performed for indices associated with new postoperative ECG pathologies. RESULTS: A total of 217 patients were enrolled. Preoperative pathologic ECG changes were recorded in 62.2% of the patients. Postoperatively, new ECG pathologies were documented in 49.8% of patients, most commonly T-wave changes (36.4% of changes). Pathologic ECG changes at baseline (OR 3.15, 95% CI [1.61-6.17]; P<0.01), diabetes (OR 1.93, 95% CI [1.02-3.64]; P=0.04), history of ischemic heart disease (OR 2.14, 95% CI [1.03-4.47]; P=0.04), higher volumes of fluid replacement (OR 1.70, 95% CI [1.10-2.61]; P=0.01) and higher levels of preoperative hemoglobin (OR 1.24, 95% CI [1.04-1.47]; P=0.01) were all independently associated with postoperative ECG changes. CONCLUSIONS: Pre-, but most importantly, postoperative ECG changes are common in intermediate-high risk surgical patients. Postoperative ECG may be valuable to disclose silent cardiovascular events that occurred during surgery.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".