Incremental Value of the Preoperative Echocardiogram to Predict Mortality and Major Morbidity in Coronary Artery Bypass Surgery
Bibliographic record
Abstract
BACKGROUND: Although echocardiography is commonly performed before coronary artery bypass surgery, there has yet to be a study examining the incremental prognostic value of a complete echocardiogram. METHODS AND RESULTS: Patients undergoing isolated coronary artery bypass surgery at 2 hospitals were divided into derivation and validation cohorts. A panel of quantitative echocardiographic parameters was measured. Clinical variables were extracted from the Society of Thoracic Surgeons database. The primary outcome was in-hospital mortality or major morbidity, and the secondary outcome was long-term all-cause mortality. The derivation cohort consisted of 667 patients with a mean age of 67.2±11.1 years and 22.8% females. The following echocardiographic parameters were found to be optimal predictors of mortality or major morbidity: severe diastolic dysfunction, as evidenced by restrictive filling (odds ratio, 2.96; 95% confidence interval, 1.59-5.49), right ventricular dysfunction, as evidenced by fractional area change <35% (odds ratio, 3.03; 95% confidence interval, 1.28-7.20), or myocardial performance index >0.40 (odds ratio, 1.89; 95% confidence interval, 1.13-3.15). These results were confirmed in the validation cohort of 187 patients. When added to the Society of Thoracic Surgeons risk score, the echocardiographic parameters resulted in a net improvement in model discrimination and reclassification with a change in c-statistic from 0.68 to 0.73 and an integrated discrimination improvement of 5.9% (95% confidence interval, 2.8%-8.9%). In the Cox proportional hazards model, right ventricular dysfunction and pulmonary hypertension were independently predictive of mortality over 3.2 years of follow-up. CONCLUSIONS: Preoperative echocardiography, in particular right ventricular dysfunction and restrictive left ventricular filling, provides incremental prognostic value in identifying patients at higher risk of mortality or major morbidity after coronary artery bypass surgery.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".