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Record W3092245181 · doi:10.1093/eurheartj/ehaa699

Towards a better standard for defining high bleeding risk patients: can we now translate this into a better practice?

2020· letter· en· W3092245181 on OpenAlexaffabout
Guillaume Marquis‐Gravel, E. Magnus Ohman

Bibliographic record

VenueEuropean Heart Journal · 2020
Typeletter
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsMedicineIntensive care medicineMEDLINE

Abstract

fetched live from OpenAlex

This editorial refers to ‘Validation of high bleeding risk criteria and definition as proposed by the Academic Research Consortium for High Bleeding Risk’†, by N. Corpataux et al., on page 3743. A decade ago, the Academic Research Consortium (ARC) published a standardized bleeding definition with the aim of simplifying comparisons across clinical studies.1 Recently, the same international group of experts developed a set of high bleeding risk (HBR) criteria to promote consistency across trials evaluating this vulnerable subset of patients.2 The ARC-HBR criteria were not developed as a clinical decision support tool, but rather to simplify the comparisons between studies focusing on patients bearing a HBR phenotype, and to facilitate regulatory decisions. The ARC-HBR criteria represent a multi-stakeholder expert consensus based on the previous literature, but their validation remains a necessary step before they can be widely accepted and put into use. Previous studies have demonstrated that approximations of the ARC-HBR criteria, modified to ‘fit’ with the available datasets, were able to identify high and low bleeding risk patients (Table 1),3–6 but the discriminative accuracy of the complete ARC-HBR criteria has never been evaluated. ARC-HBR validation studies ARC-HBR validation studies In this issue of the European Heart Journal, Corpataux and colleagues present the largest, most comprehensive study so far to evaluate the accuracy of the ARC-HBR criteria to identify post-PCI patients at high vs. low bleeding risk.7 Their analysis leverages a high-quality dataset including 16 850 consecutive all-comer patients encountered in routine clinical practice. They demonstrate that slightly more than a third of all PCI (percutaneous coronary intervention) patients fulfilled the ARC-HBR definition, and that those incurred a three-fold greater risk of BARC 3 or 5 bleeding within 30 days (4.06% vs. 1.18%, respectively), and from 30 days to 1 year (3.96% vs. 1.36%, respectively) after the intervention. Importantly, the 1-year rates of adjudicated BARC 3 or 5 bleeding events among those fulfilling the ARC-HBR criteria was >4%, meeting the intended ARC-HBR threshold. Furthermore, they demonstrate that the risk of BARC 3 or 5 bleeding increased progressively as a function of the number of major or minor criteria present, with roughly a doubling of the risk for every single unit of ARC-HBR score increase. This latter finding cannot be underestimated as it also suggests that a combination of only ‘minor’ risk factors is also associated with a higher risk of bleeding. Another important finding is that patients not meeting the ARC-HBR criteria were at low risk for bleeding. These results were robust and consistent in a number of sensitivity analyses (competing mortality risk modelling, complete follow-up subset, and landmark analysis), and using alternative definitions of bleeding. These findings suggest that we can now use the ARC-HBR criteria for what they were really designed to do: evaluating the comparative efficacy, effectiveness, and/or safety of devices and/or drugs in a standardized HBR population. HBR patients constitute a vulnerable population representing a sizeable proportion of patients referred to cardiac catheterization laboratories in routine clinical practice, but they have been historically excluded from randomized trials evaluating coronary devices. This leaves physicians in a decision conundrum regarding the most appropriate stent platform and which dual antiplatelet therapy (DAPT) duration to use in this subset. More recently, HBR patients have been the intended population of a series of completed and ongoing randomized trials evaluating novel stent designs adapted to their high-risk profile.8–10 These trials specifically excluded patients who were not at HBR to evaluate the safety and efficacy of a variety of novel stent platforms in patients requiring a very short DAPT course (e.g. 30 days) because of a perceived HBR by their physicians. Unfortunately, while sharing many similarities and overlaps, currently available randomized trials that specifically focused on HBR patients have used different sets of eligibility criteria to define what constitutes a true HBR population, which makes comparison across studies and translation of the findings into routine clinical practice difficult. We believe that the ARC-HBR should now be the new standard for those types of trials. Many trials that have tested modified durations of DAPT have already adopted the ARC bleeding definitions,11 and the same should happen for the ARC-HBR criteria. Whether the findings from previous trials that evaluated the safety and efficacy of novel stent designs in heterogeneously defined HBR patients also apply in the ARC-HBR-defined subsets of these trials also needs to be evaluated, especially for pivotal studies such as the LEADERS FREE studies. It is important to recognize that the purpose of the ARC-HBR criteria is not to guide clinical practice to identify patients who might benefit from bleeding prevention strategies involving de-escalation of intensity or duration of antiplatelet therapy (such as aspirin-free antiplatelet regimens or shorter DAPT duration after PCI) unless explicitly studied. This important consideration is reinforced by the study of Corpataux et al., in which the ARC-HBR phenotype was also associated with a higher risk of developing ischaemic events. For example, the 1-year myocardial infarction rates were 6.0% and 3.7% in the ARC-HBR and the non-ARC-HBR subsets, respectively (P < 0.001). Therefore, reducing antiplatelet intensity or duration in patients meeting the ARC-HBR definition would put them at undue risk of subsequent ischaemic events. Tools developed and validated for this purpose, such as the PRECISE-DAPT and the DAPT scores, still remain the best choices to guide clinicians selecting the most appropriate DAPT duration following PCI.12 , 13 The previously mentioned trials focusing on the HBR population may provide an important piece of the bleeding puzzle to further enhance our practice. In this jigsaw of managing bleeding vs. ischaemic outcomes, we need to optimize therapies for those at HBR after PCI as a post-PCI bleeding event confers an adverse prognosis similar to post-PCI myocardial infarction.14 Our goal for antiplatelet therapies should be just a long enough duration to prevent ischaemic events in the acute phase, but short enough to avoid later bleeding. This is a challenging goal that Corpataux and colleagues should be congratulated for having facilitated by validating the best definition for the assessment of patients at high risk of bleeding. Conflict of interest: G.M.-G. reports research grants from the Canadian Institutes of Health Research, and personal fees from Novartis and Servier. E.M.O. reports research grants from Abiomed and Chiesi, and consulting fees from AstraZeneca, Cara Therapeutics, Faculty Connection, Imbria, Impulse Medical, Janssen Pharmaceuticals, Milestone Pharmaceuticals, Xylocor, and Zoll Medical. 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)0.052
metaresearch head score (Gemma)0.214
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: none
Teacher disagreement score0.052
Threshold uncertainty score0.276

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.214
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0040.002
Science and technology studies0.0020.006
Scholarly communication0.0140.013
Open science0.0060.003
Research integrity0.0160.033
Insufficient payload (model declined to judge)0.0100.011

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 head0.021
GPT teacher head0.277
Teacher spread0.256 · 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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Citations3
Published2020
Admission routes2
Has abstractyes

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