The importance of characteristics of angina symptoms for the prediction of coronary artery disease in a cohort of stable patients in the modern era
Bibliographic record
Abstract
OBJECTIVE: Angina is an important clinical symptom indicating underlying coronary artery disease (CAD). Its characteristics are important for the diagnosis and risk stratification of patients with CAD. Currently, we aimed to investigate the association of chest pain characteristics with the presence of obstructive CAD in a contemporary cohort of patients undergoing coronary angiography for suspected stable CAD. METHODS: Consecutive patients undergoing coronary angiography for suspected stable CAD (n = 686) in a single university hospital cardiology department were enrolled. Chest pain was classified as typical angina, atypical angina, nonangina chest pain, and lack of symptoms. The presence of significant angiographic CAD was diagnosed by standard coronary angiography. RESULTS: Typical angina symptoms were associated with a higher prevalence of CAD (odds ratio [OR], 3.47, p < 0.001), whereas atypical angina symptoms were associated with a lower prevalence of CAD (OR, 0.49, p = 0.003) than the nonangina symptoms/or asymptomatic status. In multivariate analysis, typical angina symptoms remained an independent predictor of CAD (OR, 2.54, p < 0.001), with a greater predictive accuracy than other clinical risk factors (area under the curve [AUC], 0.715, p < 0.001) and similar to the accuracy of the high-sensitivity C-reactive protein (AUC, 0.712, p < 0.001). In a multivariate model, the combination of all studied factors further improved the predictive accuracy (AUC, 0.81, p < 0.001). CONCLUSION: In a contemporary cohort of patients referred for coronary angiography for stable CAD, the presence of typical angina symptoms was the most important independent predictor of obstructive CAD. The association of atypical angina symptoms with low CAD prevalence compared to nonangina chest pain or absence of significant symptoms probably reflects different management and referral strategies in these groups of patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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, 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".