Supersilent myocardial ischemia and risk of all-cause mortality in elderly patients
Bibliographic record
Abstract
Purpose: Elderly patients have a higher prevalence of coronary artery disease but also a higher probability of abnormal perception of angina, and the Duke Treadmill Score failed to show significant prognostic value in these patients. The prevalence and clinical significance of echocardiographic evidence of myocardial ischemia in elderly patients with negative exercise electrocardiograms (i.e., supersilent myocardial ischemia [SSMI]) has not been investigated. Our aim was to evaluate the prevalence, predictors and outcome of SSMI, as assessed by exercise echocardiography, in elderly patients with known or suspected coronary artery disease. Methods: SSMI was defined as the development of exercise-induced wall motion abnormalities in the absence of chest pain or ischemic electrocardiographic changes. A total of 1497 consecutive patients aged ≥65 years (50.8% males) with baseline interpretable electrocardiograms underwent treadmill exercise echocardiography and did not develop chest pain or ischemic electrocardiographic changes during the tests. The increase in wall motion score index from rest to peak exercise (ΔWMSI) was used as a quantifier of the degree of SSMI. The end-point was all-cause mortality. Results: SSMI was evident in 318 patients (20%). In logistic regression analysis, male sex (odds ratio [OR] 2.30, 95% CI 1.73-3.06, p <0.001), diabetes mellitus (OR 1.61, 95% CI 1.17-2.21, p = 0.004), prior myocardial infarction (OR 4.07, 95% CI 3.04-5.46, p <0.001), and resting left ventricular ejection fraction <55% (OR 1.44, IC 1.03-2.03, p = 0.036) remained predictors of SSMI in elderly patients. During an average follow-up of 4.3±3.2 years, 197 patients died. Five-year mortality rate was 16.9% in patients with SSMI vs 11% in those without SSMI (p=0.016). In Cox regression analysis, ΔWMSI remained an independent predictor of mortality (hazard ratio 2.58, 95% CI 1.07-6.23, p=0.03). Conclusions: A significant proportion of elderly patients with known or suspected coronary artery disease have echocardiographic evidence of SSMI in the absence of exercise-induced chest pain or ischemic electrocardiographic changes, which in turn identifies a subgroup of patients at a significantly higher risk of mortality.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".