Limitations of exercise treadmill testing in evaluating patients for the presence of atherosclerosis
Bibliographic record
Abstract
Abstract Background As subclinical atherosclerosis is increasingly recognized as a key predictor of cardiovascular events, there is growing interest in cost-effective and reliable screening tools for its detection. While exercise treadmill testing (ETT) is commonly used to assess ischemia, its sensitivity and specificity in detecting coronary artery disease (CAD) remain uncertain. Purpose This study compares ETT results with coronary CT angiography (CCTA) to evaluate its diagnostic accuracy in detecting both obstructive CAD and any presence of atherosclerosis, with implications for risk stratification. Methods A retrospective analysis was conducted on 1,326 patients without known CAD who underwent both ETT and CCTA. ETT results (normal, abnormal, or equivocal) were compared to CAD-RADS classifications. Two models were evaluated: the first assessed ETT’s ability to detect obstructive CAD (CADRADS ≥3), and the second assessed its ability to detect any atherosclerosis (CADRADS 1-5). Sensitivity, specificity, PPV, and NPV were calculated for both models. Results For detecting obstructive CAD, ETT had a sensitivity of 36%, specificity of 76%, PPV of 17%, and NPV of 89%. Disease (CADRADS ≥3) was correctly identified in 4% of patients, while 9% were missed. 67% of patients were appropriately categorized as not having obstructive disease, whereas 24% underwent unnecessary repeat testing despite having a CAD-RADS of 0-2. When evaluating ETT’s ability to detect any atherosclerosis, sensitivity was 27%, specificity 76%, PPV 61%, and NPV 43%. Atherosclerosis was correctly identified in 16% of patients, while 8% of patients with obstructive disease (CAD-RADS ≥3) and 35% of those with early atherosclerosis (CAD-RADS 1-2) were not detected. Appropriate reassurance was provided in 32% of cases, while 10% of patients underwent unnecessary repeat testing despite having no detectable disease (CAD-RADS 0). Conclusion ETT demonstrates limited accuracy in detecting both obstructive and non-obstructive CAD, with almost a third of patients misclassified in both models, leading to missed diagnoses and unnecessary testing. These findings highlight the limitations of ETT as a screening tool and suggest that coronary imaging may offer superior risk stratification by improving early atherosclerosis detection and reducing unnecessary testing.
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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 | 0.023 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".