The Value of Routine Preoperative Electrocardiography in Predicting Myocardial Infarction After Noncardiac Surgery
Bibliographic record
Abstract
OBJECTIVE: The added value of a preoperative electrocardiogram (ECG) in the prediction of postoperative myocardial infarction (POMI) and death was compared with clinical risk factors identified from the patient's history. SUMMARY OF BACKGROUND DATA: An ECG is frequently performed before surgery to screen for asymptomatic coronary artery disease. However, the value of ECG abnormalities to predict POMI has been questioned. METHODS: The study included 2967 noncardiac surgery patients >50 years of age from 2 university hospitals, who were expected to stay in the hospital for >24 hours. All data were obtained from electronic record-keeping systems. Patient history and ECG abnormalities were considered as potential predictors. Multivariate logistic regression analysis was used to obtain the independent predictors of POMI and all-cause in-hospital mortality. The area under the receiver operating characteristic curve (ROC area) was estimated to evaluate the ability of different models to discriminate between patients with and without the outcome. RESULTS: A preoperative ECG was available in 2422 patients (80%) and 1087 (45%) of the ECGs showed at least one abnormality. The ROC area of the model that included the independent predictors of POMI obtained from patient history, ie, ischemic heart disease and high-risk surgery, was 0.80. ECG abnormalities that were associated with POMI were a right and a left bundle branch block. After adding these abnormalities in the regression model, the ROC area remained 0.80. Similar results were found for all-cause mortality. CONCLUSIONS: Bundle branch blocks identified on the preoperative ECG were related to POMI and death but did not improve prediction beyond risk factors identified on patient history.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".