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Record W2987758445 · doi:10.1093/eurheartj/ehz808

All for one, but not one for all

2019· letter· en· W2987758445 on OpenAlexaff
Justin A. Ezekowitz

Bibliographic record

VenueEuropean Heart Journal · 2019
Typeletter
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsCanadian VIGOUR CentreUniversity of Alberta
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

This editorial refers to ‘Nurse-led vs. usual-care for atrial fibrillation’†, by E.P.J.P. Wijtvliet et al., on page 634. The quote from Alexandre Dumas, made famous in the Three Musketeers to represent unity and solidarity as a strength (‘tous pour un, un pour tous’), may have relevance in medical science and clinical care.1 As clinicians and healthcare systems consider how to deliver care for individuals and populations, we often find that some systems of care seem to benefit nearly all (e.g. facilitating timely emergent care for ST-elevation myocardial infarction) and some should be reserved for the very few with clear evidence of benefit (e.g. mitral clip). Globally, atrial fibrillation (AF) affects millions of individuals and results in a high burden of death and disability-adjusted life years.2 As the most common arrhythmia in clinical practice and on the rise in both lower and higher income countries, especially with newer detection methods and ageing populations, it is imperative to understand how and where to manage patients. Like other disease states, a concerted effort is required in order to care for patients with AF. The experience from multidisciplinary heart function clinics, set up to help patients with heart failure and tested in randomized controlled trials (RCTs), demonstrated that many different care providers working as a team can alter the morbidity and mortality associated with heart failure (HF).3 This example has been established and adapted in many different environments, with bespoke solutions for the health systems and patients they serve. As such, counselling and treatment have evolved to include different healthcare professionals and settings in HF, but does it work for other disease states? Indeed, AF clinics have sprung up to provide the best care and to ensure emerging strategies are provided to patients—built upon this concept. Against this backdrop, organization of care for patients with AF has emerged as a potential avenue to manage the burden of disease from a healthcare system perspective and provide an individual patient with optimal care. For example, in a previous single-centre RCT, 712 patients were allocated to either nurse-led care or usual care, and a marked reduction in both cardiovascular (CV) death and CV hospitalization was seen.4 With few total events and as a single-centre study, confirmatory results were required. In this issue of the European Heart Journal, Wijtvliet and colleagues (the RACE-4 investigators) present an RCT of nurse-led care compared with usual care for 1375 outpatients with AF, referred onwards for specialist care in the Netherlands.5 Patients were allocated to either usual care, which in the Netherlands included follow-up with a cardiologist or nurse-led care supported by a cardiologist. In the nurse-led care arm, the key additional strategies included more frequent visits, additional computer-based decision support, and additional psychosocial support. The hypothesis was that nurse-led care would lead to a lower rate of a composite of CV death, or hospitalization for clinically relevant causes (HF, AF, thrombo-embolic or bleeding events, or drug-related effects). Over 3 years, there was no significant difference in the primary composite outcome despite a 15% relative risk reduction or the key quality indicator, i.e. prescription of an oral anticoagulant. There was no difference in CV death or other parts of the composite, with the exception of AF-related hospitalizations which were lower by an absolute 2.4% of annualized rate in the nurse-led care arm than in the usual-care arm. So what led to this result, what can we learn from a neutral trial, and how should clinicians interpret this trial? First, this was a lower risk population (mean age 64 years old, lower prevalence of cardiac and non-cardiac disease) than studied in many of the recent RCTs involving patients with AF. Had the trial enrolled patients with higher risk, greater burden of disease, or, more recently, after a hospitalization, there may have been greater opportunity for nurse-led care to demonstrate a meaningful difference in the composite outcome. By enrolling patients in the Netherlands, where the standards and integration of care are quite high, it may be difficult to demonstrate a meaningful difference, and the trial was probably underpowered due to secular trends in the Netherlands. Secondly, nurse-led care was enhanced by an information technology (IT) solution. It is unclear whether the IT solution was additive or simply neutral, and the trial would have come to the same eventual result. This is a reminder that often an intervention is the sum of its parts—but each part should have additive value. Thirdly, the authors highlight a pre-specified subgroup of experienced vs. non-experienced centres. In the nurse-led group, the four experienced centres outperformed the four less experienced centres. This difference in outcomes was striking and on a single outcome: arrhythmic hospitalizations (9.9% per year in experienced and 26.4% per year in less experienced centres). This is reminiscent of procedure volume and may be a ‘true’ finding, but should be considered hypothesis generating. As with HF, we and others have demonstrated that simply seeing the usual-care physician (physician continuity) has an association with outcome,6 and it would not be a stretch so consider the same for AF. The trial was neutral on other fronts that have bearing for those considering starting an AF clinic. For example, quality of life as measured by the Atrial Fibrillation Severity Scale was no different and neither was patient-assessed knowledge or patient self-mangement, despite the additional time and resources spent educating patients in the nurse-led care arm. Therefore, it is hard to see how the intervention made any difference, at either experienced or less experienced centres. It is also a good reminder that for an intervention to be cost-effective, it also has to be effective as measured by any domain (e.g. quality of life or clinical outcomes). In this setting, the nurse-led care was not cost-effective and was more resource intensive. Do the outcomes of quality indicators matter? Both arms achieved an acceptable level of the use of oral anticoagulation (81%) but in a cohort of patients at this stroke risk, in an RCT centred around the care of AF, one could ask why is it so low? Approximately 24% of patients were CHADS2-VASC = 0, and the authors outline overtreatment (∼4.5%) and undertreatment (∼14%), as well identifying that a third of patients were on a vitamin K antagonist. To date, the use of an oral anticoagulant is the only one of the seven identified quality of care measures coupled with a meaningful clinical outcome (e.g. stroke reduction) and was done equally well in both arms. As such, little can be meaningfully gleaned from the presentation of these performance measure achievements as primary care surveillance has demonstrated equal or better concordance with oral anticoagulation guidelines.7 Where do we go from here? First, AF clinics may have a potential benefit to optimize and harmonize the care provided in order to integrate new information. This can be exemplified by the optimization of oral anticoagulants and the reduction in toxic medications without demonstrable benefit. Secondly, as new treatments arrive (e.g. pulmonary vein isolation/ablation) with mixed evidence, it is best placed in the care of a comprehensive clinic to best advise which patients should or should not be selected to receive this. Thirdly, there is a need to optimize all CV risk factors and, done in conjunction with primary care, may serve to reduce a patient’s longer term outcome risk. Fourthly, many patients do need greater understanding and assistance and, as such, AF clinics may serve in this function. Finally, and as done by the RACE-4 investigators, it is imperative to conduct RCTs testing the intervention to ensure patients and the health system are suitable for this resource-intensive intervention. However, clearly one size does not fit all, not all patients need a specialist, and effective triage needs to occur for both the intake of patients and the ongoing management of patients.8 Figure 1 highlights some of the potential patient concepts to be integrated, with location of initial diagnosis, risk for clinical outcomes, and need for advanced care, all with key central triage. Effective triage and care of patients with atrial fibrillation. Patients are often diagnosed in alternative settings, have differing needs, and have different risk for clinical outcomes. Effective triage requires patient assessment and core information. 1Indicates a heart rhythm specialist in conjunction with nursing, pharmacy, dietician, and other healthcare professionals. 2Indicates guideline-based therapies including stroke prevention, heart rate or rhythm control, and symptom management. 3Indicates that risk for clinical events should be assessed using a risk score appropriate for the situation, and patient preferences and goals of care should be included in the overall assessment. In the sentiment as written by Alexandre Dumas, the clinicians working together with their patient may consider ‘all for one, one for all’,1 but the evidence and health system may better reflect ‘all for one, but not one clinic for all’. Conflict of interest: J.A.E. has received clinical trial support, honorarium or served in an advisory role with Bayer and BMS/Pfizer, and was on the steering committee of ARISTOTLE. The opinions expressed in this article are not necessarily those of the Editors of the European Heart Journal or of the European Society of Cardiology.

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)0.003
metaresearch head score (Gemma)0.032
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.044
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0440.033
Insufficient payload (model declined to judge)0.0110.008

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 head0.248
GPT teacher head0.374
Teacher spread0.126 · 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
GenreCommentary

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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Citations1
Published2019
Admission routes1
Has abstractno

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