Foreword Optimizing the Prevention of Cardiovascular Events
Bibliographic record
Abstract
Patients with cardiovascular disease are fortunate to have available a wide range of proven treatments that have greatly reduced the risk for cardiovascular complications. 1 Clinical trials involving large numbers of subjects have provided confident evidence of benefit. However the application of the findings published from clinical trials does not always reach the bedside. Despite guideline recommendations to use proven treatments, many patients either do not have the therapy prescribed or physicians fail to follow the strategy proven in the clinical trial. 2,3 This special issue of the Canadian Journal of Cardiology provides therapeutic suggestions on the basis of clinical trial evidence for the management of 2 common cardiology scenarios. The first article 4 from the Canadian Heart Research Centre on the management of the patient who has recently been hospitalized for an acute coronary event provides evidence-based step-by-step recommendations for long-term secondary prevention. The second article 5 provides a discussion of strategies for patients with statin-related adverse effects or who are unable to tolerate statin treatment. Hospitalization with an acute coronary syndrome (ACS) provides the opportunity to initiate optimal evidence-based treatment. Treatment goals are to reduce the risk of complications from the acute coronary event, and the chance of recurrent atherosclerotic cardiovascular events. Vascular and cardiac protection are the principles of treatment for these high-risk patients. For patients with an ACS it is often the start of long-term treatment of chronic atherosclerotic disease. Today, to provide optimal treatment to meet these goals during a short duration hospital admission is a challenge. An important role of the coronary care unit has been to provide patient education and review treatment goals. With improved technical care, especially early revascularization, the duration of hospitalization has shortened. Consequently, there is need for aids such as protocol-driven discharge prescriptions to ensure that patients have the opportunity to be considered for all evidence-based treatment that might provide benefit. In the application of clinical trial results to clinical practice the need to follow the protocol used in the study should be recognized. Patient demographic characteristics, drugs and doses used, and duration of treatment should closely follow the proven strategy applied in the clinical trial. Guidelines recommend classes of drugs, yet relatively few individual drugs have been shown to have benefit in robust clinical trials. Consequently, a class effect cannot be assumed. Frequently doses of drugs used in the trials are not achieved. Treatments are often extended to groups of patients for which there is no proven benefit. If optimal benefit of the research findings from a clinical trial is to be achieved, then there is need for close respect of the trial protocol. However it is recognized that special patient groups might not be included in clinical trials. These groups include elderly individuals, and those with chronic kidney or liver disease. In these situations the clinician is often left to make a clinical judgement on the basis of the potential benefits and risks of the treatment. Clinical trials have introduced new treatments at widely different clinical epochs. For example, b-blockers were studied before most of our current ACS treatments, including coronary angioplasty, statins, and angiotensin converting enzyme inhibitors, were available. When there are no recent studies, it remains a challenge to know whether treatment proven in a previouseraremainsbeneficialwhenusedwithcurrenttreatment strategies. Often there are less stringent observational studies to provide clues of contemporary outcomes with the treatment. Several new medications withlikely benefitinpatientswitha history of ACS and chronic cardiovascular disease, have recently become available. The proprotein convertase subtilisin kexin type 9 (PCSK9) inhibitors reduce low-density lipoprotein cholesterol 50% beyond that achieved with statin therapy. Preliminary data suggest the additional low-density lipoproteinlowering achieved with these agents in patients at high risk, is associated with a substantial reduction of cardiovascular adverse outcomes. The sodium glucose co-transporter type 2 (SGLT2) inhibitor empagliflozin was shown to reduce mortality and the incidence of heart failure in patients with diabetes and cardiovascular disease (including patients with a history of myocardial infarction). For patients with heart failure, neprilysin inhibition and If channel inhibition with ivabradine improve outcomes.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.073 | 0.048 |
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".