State of the Art: Evaluation and Medical Management of Nonobstructive Coronary Artery Disease in Patients With Chest Pain: A Scientific Statement From the American Heart Association
Bibliographic record
Abstract
Risk stratification of patients with chest pain has traditionally focused on identifying obstructive coronary artery disease (CAD). Using this traditional approach, many symptomatic individuals are found to have nonobstructive CAD. The 2021 American Heart Association/American College of Cardiology/American Society of Echocardiography/American College of Chest Physicians/Society for Academic Emergency Medicine/Society of Cardiovascular Computed Tomography/Society for Cardiovascular Magnetic Resonance chest pain guideline widened the scope of cardiac computed coronary angiography, resulting in increased identification of patients with nonobstructive CAD. In addition, recent advances in artificial intelligence solutions, hardware, and software have allowed identification of microvascular disease and introduced new risk categories within nonobstructive CAD with a risk continuum between primary and secondary prevention. There is thus a growing need for care teams to remain current on the diagnosis, risk stratification, and management of patients with nonobstructive CAD. Whereas only a subset of patients with chest pain are found to have true angina despite nonobstructive CAD, underlying nonobstructive CAD warrants attention. Medical management of nonobstructive CAD plays an essential role in plaque stabilization and regression to decrease the risk of acute coronary syndromes. New pharmacologic therapies and noninvasive plaque evaluation raise the potential for plaque-driven medical interventions. However, data in patients with chest pain who are found to have nonobstructive CAD are limited, and, in clinical practice, multiple factors lead to missed opportunities for precision therapies, with proven disparities in care. We review the current evidence on risk stratification for nonobstructive CAD and discuss its implications and medical management options.
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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 | 0.025 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".