Acute Coronary Care in the Elderly, Part I
Bibliographic record
Abstract
BACKGROUND: Age is an important determinant of outcomes for patients with acute coronary syndromes (ACS); however, community practice reveals a disproportionately lower use of cardiovascular medications and invasive treatment even among elderly patients with ACS who would stand to benefit. Reasons include limited trial data to guide the care of older adults and uncertainty about benefits and risks, particularly with newer medications or invasive treatments and in the setting of advanced age or complex health status. METHODS AND RESULTS: This 2-part American Heart Association scientific statement summarizes evidence on patient heterogeneity, clinical presentation, and treatment of non-ST-elevation ACS in relation to age (< 65, 65 to 74, 75 to 84, and > or = 85 years). In addition, we review methodological issues that influence the acquisition and application of evidence to the elderly patients treated in community practice. A writing group combining international cardiovascular and geriatric perspectives convened to summarize available data from trials (5 combined Virtual Coordinating Center for Global Collaborative Cardiovascular Research [VIGOUR] trials) and 3 registries (Global Registry of Acute Coronary Events, National Registry of Myocardial Infarction, and the Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the American College of Cardiology/American Heart Association guidelines national quality improvement initiative [CRUSADE]) to provide a conceptual framework for future work in the care of the elderly with acute cardiac disease. Treatment for non-ST-segment-elevation ACS (Part I) and ST-segment-elevation myocardial infarction (Part II) are reviewed. In addition, ethical considerations pertaining to acute care and secondary prevention are considered (Part II). The primary goal is to identify the areas in which sufficient evidence is available to guide practice, as well as to determine areas that warrant further study. Although treatment-related benefits should rise in an elderly population with high disease risk, data to assess these benefits are limited, outcomes of importance vary, and heterogeneity among the elderly increases treatment-related risks. Although a uniform approach to care in the oldest of the old is unlikely, understanding the major contributors to benefits and risks from treatment will advance the ability to apply guideline-based care in this subset of patients. CONCLUSIONS: Although a few recent trials have described treatment effects in older patients, others continue to exclude patients on the basis of age. Going forward, prospective trials should enroll elderly subjects proportionate to their prevalence among the treated population to define risk and benefit. Findings from age subgroup analyses should be reported in a consistent manner across trials, including absolute and relative risks for efficacy and safety. Outcomes of particular relevance to the elderly, such as quality of life, physical function, and independence, should also be considered. Creatinine clearance should be calculated for every elderly patient to enable appropriate dosing. In addition, physicians need an understanding of conditions unique to older patients (eg, frailty, cognitive impairment) that influence treatment goals and outcomes. With these efforts, treatment risks can be minimized, and benefits can be placed in the health context of the elderly patient with ACS.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".