Bibliographic record
Abstract
The population of the United States is aging, with estimates indicating there will be 72.1 million people older than 65 years by the year 2030.1 Age is a risk factor for atrial fibrillation (AF), with individuals older than 60 years accounting for 64% of stroke incidence.2 The oldest old are at highest risk, with individuals older than 80 years having a 4- to 5-fold increased risk of stroke,2 an increased risk of myocardial infarction, increased hospitalization rate,3 and increased mortality.4 The American Heart Association identified paroxysmal, persistent, and permanent AF as a modifiable independent risk factor for stroke because the risk decreases significantly with the use of anticoagulants.5 Optimal use of anticoagulation to prevent stroke in the population with AF and to reduce the severity of the stroke, degree of disability, and mortality in this population is an important prevention strategy.6,7 Current published guidelines provide treatment recommendations based on accurate risk stratification to guide interventions to reduce the risk of stroke and minimize bleeding risk.8–10 Although risk stratification instruments are available to assess the stroke risk and bleeding risk among individuals with AF, a Canadian study that examined the usual practice of 438 general practitioners found that stroke risk tools were used only 50% of the time and bleeding risk tools only 25% of the time.11 A task force was organized under the leadership of the Alliance for Aging Research to determine optimal treatment for AF, resulting in a roundtable composed of experts from the United States and United Kingdom.12 The consensus recommendations were to (a) assess stroke risk using an established scoring instrument and (b) record the value in the electronic health record annually to capture changes in risk that may occur over time.12 Moderate- and high-risk patients with AF should be placed on oral anticoagulant treatment with an evaluation of bleeding risk completed using an available scoring instrument.12 For these moderate- to high-risk patients, the task force suggested the benefit of oral anticoagulants will outweigh the risk of bleeding. The expert panel highlighted the fact that risk assessment tools are underutilized, resulting in high-risk patients having strokes without having received oral anticoagulation. Furthermore, they suggest that the use of any evidence-based instrument to stratify risk will be superior to not using a tool at all.12 To facilitate the translation of evidence to practice, the American College of Cardiology convened the Anticoagulation Consortium Roundtable in September 2013, which resulted in recommendations for increasing (a) clinician knowledge about AF stroke risk stratification and anticoagulation treatment, (b) access to relevant clinical tools to facilitate risk stratification, and (c) access to toolkits for clinicians to use at the point-of-care to facilitate implementation of stroke risk reduction therapies (personal communication, September 21, 2013).13 There is an urgent need to equip providers with evidence-based information and tools to optimize AF management in the elderly.14 The American College of Cardiology Web site provides open access to an AF toolkit with online tools for risk stratification, treatment and management, and patient education at http://www.acc.org/tools-and-practice-support/clinical-toolkits/atrial-fibrillation-afib. Studies have varied regarding the most reliable instruments, leading to the suggestion that providers select an instrument and use it consistently.15–17 Once the diagnosis of AF is confirmed, the next step should be risk stratification to determine the appropriate evidence-based intervention. Risk stratification and the use of evidence-based treatment protocols reassure clinicians who are often reluctant to prescribe antiplatelet and/or anticoagulant therapy because of the individual’s age or risk of falls. Of note, studies have reported that fewer than 50% of clinicians adhere to guideline concordant care in older patients with AF.11 It has been reported that for every 10% increase in guideline adherence by providers, there is a 10% decrease in mortality among patients with AF.18 There is consensus that completion of risk stratification for stroke and serious bleeding adds critical information for the prescribing providing when selecting the most appropriate stroke risk reduction treatment. Selection of an easily accessible instrument that is provider-friendly to use may increase stratification in the clinical setting. The Table displays some risk stratification instruments, their features, and Web link to access the instruments.TABLE: Resources for Risk Stratification and Guideline Based InterventionsBroad dissemination of evidence-based protocols, guidelines, and toolkits that emphasize the importance of risk stratification will likely increase the number of individuals with nonvalvular AF who are risk stratified. In turn, accurate and reliable risk stratification has the potential to enhance realization of stroke prevention goals, improve guideline concordant care, and decrease the burden of stroke and stroke recurrence in the United States. There is a need for continued efforts to ensure immediate access to time efficient stratification tools to guide the selection of anticoagulation options including warfarin, dabigatran, rivaroxaban, or apixaban.10 Nurses across the continuum of care have an important role in the prevention of stroke in the elderly patient with AF. Cardiovascular nurses are positioned to complete risk stratification for bleeding and stroke, document and communicate the risk scores to prescribing providers, and provide patient and family education about nonvalvular AF, stroke risk, bleeding risk, and guideline-based recommendations. Older patients with AF, and their caregivers, need to be engaged as full partners in their care to understand and optimize their stroke and bleeding risk care plan. Cardiovascular nurses can assess and discuss patient and caregiver preferences related to stroke risk reduction interventions, an important strategy to engage patients in the process. As prescribing clinicians, advanced practice nurses are poised to provide guideline concordant interventions based on accurate risk stratification and patient preferences. Access to tools that stratify stroke and bleeding risk of older patients is crucial to guideline concordant prescribing, treatment and patient education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".