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Record W2568353100 · doi:10.1177/2047487316686633

The left atrium: An overlooked prognostic tool

2017· letter· en· W2568353100 on OpenAlexaff
Mariëlle Kloosterman, Michiel Rienstra, Harry J.G.M. Crijns, Jeff S. Healey, Isabelle C. Van Gelder

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2017
Typeletter
Languageen
FieldMedicine
TopicPericarditis and Cardiac Tamponade
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMedicineLeft atriumCardiologyInternal medicineAtrial fibrillation

Abstract

fetched live from OpenAlex

The increasing incidence and prevalence of chronic kidney disease (CKD) is associated with a parallel rise in atrial fibrillation (AF). The main reason for this epidemiological coupling is an increasing elderly population and shared risk factors, such as diabetes mellitus, hypertension and heart failure.1 However, more and more data are becoming available to suggest that both diseases likely share underlying pathophysiological mechanisms (Figure 1).2–4 Chronic kidney disease (CKD) and atrial fibrillation (AF) share many collective risk factors. Currently, growing evidence suggests that underlying pathophysiological mechanisms cause vascular disease that can give rise to atrial cardiomyopathy and vascular events.2 We hypothesise that AF, just as CKD, is a manifestation and marker of vascular disease burden. Monitoring of left atrium (LA) function and form may provide prognostic information and allow earlier detection of LA involvement in individuals with CKD. COPD: chronic obstructive pulmonary disease; HFpEF: heart failure with preserved ejection fraction; HFrEF: heart failure with reduced ejection fraction; OSAS: obstructive sleep apnoea syndrome. Cardiovascular events, rather than renal failure itself, are the most common cause of mortality and morbidity in patients with CKD. The presence of both CKD and AF exacerbates vascular-related adverse events (including stroke, systemic thromboembolism, heart failure and myocardial infarction).5 Unsurprisingly, structural and functional cardiac abnormalities are already present in CKD patients without overt cardiac disease. Diastolic dysfunction has a prevalence of 29% in patients with non-dialysis CKD.6 This may be one of the triggers of left atrial (LA) enlargement, which is an established predictive marker of AF and cardiovascular events.7 In this issue of the European Journal of Preventive Cardiology, Nakanishi et al. used real time 3-D echocardiography to study the association between CKD and LA volume and function in 358 patients from a community-based cohort study without overt cardiac disease. CKD (estimated glomerular filtration rate (eGFR)) <60 ml/min/1.73 m2) was present in 69 patients (19%). These were patients early in the disease process: kidney function was relatively preserved and LA volumes were within the normal range. However, patients with CKD (mean eGFR 50 ± 9 ml/min/1.73 m2) had a higher prevalence of diastolic dysfunction and reduced LA emptying fraction (42.7 ± 11.4 versus 47.8 ± 11.5%). Multivariate regression analysis showed that eGFR was associated with LA emptying fraction, independent of age, left ventricular mass index and diastolic dysfunction, but not with LA volume. Whereas LA maximum volume remained unchanged between the groups, early CKD was independently associated with impaired LA function. LA enlargement may eventually develop as renal dysfunction progresses.8 The authors are to be congratulated on this elegant and timely study. However, the results must be interpreted in light of limitations that are inherent to its design and small study population. Furthermore, patients with CKD were older, more often had hypertension, and received different pharmacological treatment, possibly influencing LA parameters. Additionally, information on aetiology and duration of CKD, and outcome parameters such as AF occurrence, are missing. Nevertheless, the observations are in line with data from Kadappu et al. who showed that patients with CKD have altered LA function and LA enlargement compared with risk factor-matched control subjects and healthy subjects.9 Indeed, AF often occurs in the setting of CKD. In the Atherosclerosis Risk in Communities study, new-onset AF was increasingly prevalent as GFR declined. Patients with GFR of 60–89, 30–59 and 15–29 ml/min/1.73 m2 had, compared to patients with GFR ≥ 90 ml/min/1.73 m2, hazard ratios (HRs) of 1.3, 1.6 and 3.2, respectively, for developing AF during a follow-up of 10 years.10 In 8265 individuals included in the Prevention of Renal and Vascular End-stage Disease (PREVEND) study, microalbuminuria, as a measure of renal vascular dysfunction, was related to incidence of new-onset AF during a follow-up of almost 10 years, independent of cardiovascular risk factors.11 Likewise, patients with AF have a higher incidence of CKD. In a UK cohort of 4.3 million adults, linked electronic health records were used to examine time to diagnosis of AF and associated vascular events. The presence of AF at baseline was associated with the occurrence of vascular events including CKD, especially in those not treated with antithrombotic therapy (HR 1.42, confidence interval 1.31–1.54).12 In a meta-analysis consisting of more than 9.6 million patients from 104 studies, AF was present in 587,867 patients. These patients also showed a higher risk of having CKD (HR 1.64, confidence interval 1.41–1.91).13 These studies, and the observations by Nakanishi et al. in this issue, contribute to the growing evidence that indicates that AF may be a marker of vascular disease rather than the mechanism. Structural, architectural, contractile or electrophysiological changes induce atrial remodelling, i.e. atrial cardiomyopathy. This is regarded as an important risk marker for ischaemic stroke, death and vascular events, including CKD, independent of AF.2 Atrial cardiomyopathy likely results from progressive atrial remodelling due to aging, stretch from pressure and volume overload, inflammation, endothelial dysfunction and oxidative stress.2 This causes atrial fibrosis leading to contractile dysfunction, dilation and an arrhythmogenic and thrombogenic substrate.2–4 Atrial cardiomyopathy may mirror vascular disease progression and consequently may reveal the risk of vascular events including CKD and AF occurrence. As a result of the ageing population, the prevalence of AF with concurrent CKD will increase. Imaging techniques, including echocardiography, may provide prognostic information and allow detection of LA involvement in individuals with CKD. Once initial abnormalities in LA function, or subsequent increases in size are identified, physicians might be more vigilant in initiating strategies to prevent progression and cardiovascular events, including stroke.2,3 However, the prognostic role of the LA in risk prediction and stratification requires prospective testing. This knowledge is paramount to optimise the benefits of personalised treatment and minimise potential harm in this high-risk and growing population. The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge the support from the Netherlands Cardiovascular Research Initiative: an initiative with support of the Dutch Heart Foundation, CVON 2014-9: Reappraisal of Atrial Fibrillation: interaction between hyperCoagulability, Electrical remodeling, and Vascular destabilisation in the progression of AF (RACE V).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0030.005
Open science0.0010.001
Research integrity0.0120.013
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.019
GPT teacher head0.273
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations5
Published2017
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