Bibliographic record
Abstract
A brief review is presented by David Conen from McMaster University and areas for further study are discussed During the last 10–15 years, a large amount of evidence has accumulated about the incidence of atrial fibrillation (AF) in the population, its most important risk factors and subsequent outcomes. This tremendous progress has led to a better understanding of the disease and the development of novel therapeutic targets and strategies. I would like to give a brief overview of the most important advances in the next three paragraphs. I will also highlight some of the most important gaps of knowledge that persist. In western societies, the incidence and prevalence of AF has been increasing over time and is expected to further increase in the future. This trend seems to be explained by the increasing age in the general population, the increasing prevalence of obesity, a better survival after a first cardiovascular event and a much-improved technology to detect AF, and other arrhythmias. However, it is important to emphasize that much less data are available for non-western societies. Studies among different ethnicities in the USA found that the risk of AF is higher among whites compared with non-whites (Figure 1). Data adapted from Rodriguez CJ et al. Ann Epidemiol 2015;25:71–76. Data adapted from Rodriguez CJ et al. Ann Epidemiol 2015;25:71–76. Projected number of adults with atrial fibrillation in the European Union between 2000 and 2060. From Krijthe BP et al. Eur Heart J 2013;34:2746–2751. Projected number of adults with atrial fibrillation in the European Union between 2000 and 2060. From Krijthe BP et al. Eur Heart J 2013;34:2746–2751. In future studies, it will be important to quantify the global variation of AF incidence rates, as differences in rates may have an important impact on the planning of AF screening campaigns. It will also be important to assess whether any global differences in AF incidence can be attributed to phenotypic or genotypic differences. Large global studies using standardized assessment tools are needed to gain further insights in this area. While the widespread availability of user-friendly long-term ECG recording devices has facilitated the detection of AF, it has created its own challenges. Advanced long-term monitoring in elderly high-risk individuals and pacemaker patients revealed a high number of individuals who have asymptomatic, ‘subclinical’ AF episodes. It is currently unclear which threshold to use to differentiate clinical from subclinical AF and whether the same treatment algorithms should be applied to patients with subclinical AF. Studies assessing the benefits and risks of oral anticoagulation in these patients are currently ongoing. In western societies, elevated blood pressure and obesity are by far the most important modifiable risk factors for developing AF. Combining the most important modifiable risk factors explains about 50% of the population attributable risk for AF development. There is therefore much room to further increase this number, and to obtain a better understanding on risk factors for AF development. Most data on risk factors for new-onset AF are from the USA or other western populations. It is unclear whether these associations are the same in other parts of the world, where the prevalence of obesity is much lower and where different risk factors for AF development may be present (e.g. Rheumatic heart disease). Unfortunately, very few randomized trials have investigated whether weight loss or intensive blood pressure control prevents the occurrence of new-onset AF in the population. Posthoc data from randomized trials that did not pre-specify AF as an endpoint should be interpreted with caution, as they have a substantial risk of detection bias. There is therefore a clear need to include AF as pre-specified endpoint in future cardiovascular prevention trials. There are specific types of AF, where risk factors clearly differ, and where we need specific prevention and treatment strategies. The incidence of perioperative AF for example is clearly dependent on the type of surgery and may be dependent on the pro-inflammatory environment in the perioperative period, while the impact of hypertension or obesity is less clear. We need specific trials in patients with perioperative AF that take into account these specific issues, similar to the trials that are currently ongoing in patients with subclinical AF. Oral anticoagulation for stroke prevention is the mainstay of treatment in most patients with clinical AF. Other well-known adverse outcomes among patients with AF are death and congestive heart failure. Increasing evidence suggests that patients with AF also face a higher risk of cognitive dysfunction and dementia. While this increased risk seems in part explained by the higher stroke risk among AF patients, the risk of cognitive dysfunction remains increased among AF patients without a history of clinical stroke, and studies are needed to address this important issue. Finally, evidence starts to emerge that AF is not only associated with an increased risk of death and cardiovascular disease, but that it also increases the risk of non-cardiovascular disease, such as malignant cancer. The underlying mechanistic relationships are currently unknown. The causal role of AF in the development of stroke and other adverse outcomes has been questioned. Atrial myopathy is a new concept that describes patients with a diseased left atrium but without known AF. A diseased left atrium with or without the ability to develop or sustain arrhythmia episodes may potentially explain the lack of temporal relationship between arrhythmia episodes and adverse outcomes. Studies aiming to improve outcomes by directly targeting the diseased left atrium will shed more light on this interesting concept. Taken together, we have made substantial progress over the last decade in understanding the significance of AF and its complications. However, there is also a clear need for more evidence in this area. I therefore predict, that the next 10–15 years will be equally exciting and will further improve our understanding of the AF epidemiology. Conflict of interest: none declared.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".