Typical angina during exercise stress testing improves the prediction of future acute coronary syndrome
Bibliographic record
Abstract
INTRODUCTION: The prognostic value of angina during exercise stress testing is controversial, possibly due to previous studies not differentiating typical from non-typical angina. We aimed to assess the prognostic value of typical angina alone, or in combination with ST depression, during exercise stress testing for predicting cardiovascular events. METHODS: We conducted a prospective observational cohort study including all patients who performed a clinical exercise stress test at the department of Clinical Physiology, Kalmar County Hospital between 2005 and 2012. The association between typical angina/ST depression and incident acute coronary syndrome (ACS) and cardiovascular mortality were analysed using Cox regression for long-term and 1-year follow-up. RESULTS: Out of 11605 patients (median follow-up 6.7 years), 623 (5.4%) developed ACS and 319 (2.7%) died from cardiovascular causes. Compared to patients with no angina and no ST depression, typical angina and ST depression were associated with increased risk of future ACS; hazard ratio (HR) 3.5 ([95%CI] 2.6-4.7). This association was even stronger for ACS within one year (typical angina with and without concomitant ST depression; HR 20.8 (13.9-31.3) and 9.7 (6.1-15.4), respectively). Concordance statistics for ST depression in predicting ACS during long-term follow-up was 0.58 (0.56-0.60) and 0.69 (0.65-0.73) for ACS within one year, and 0.64 (0.62-0.66) and 0.77 (0.73-0.81), respectively, when typical angina was added to the model. CONCLUSIONS: Typical angina during exercise stress testing is predictive of future ACS, especially in combination with ST depression, and during the first year after the test.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".