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Record W4283172400 · doi:10.1093/ejcts/ezac333

Aortic valve fenestrations: supporting character also deserves spotlight!

2022· letter· en· W4283172400 on OpenAlexaff
Veronica F Chan, Ming Hao Guo, Munir Boodhwani

Bibliographic record

VenueEuropean Journal of Cardio-Thoracic Surgery · 2022
Typeletter
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsContext (archaeology)Competence (human resources)Aortic valveAutopsyMedicineSurgeryCardiologyInternal medicineHistoryPsychology

Abstract

fetched live from OpenAlex

Aortic valve fenestrations (AVF) are a common finding in patients being considered for aortic valve (AV) repair for aortic insufficiency (AI); yet, little is known about the pathogenesis of AVFs and the manner in which they contribute to valve dysfunction. In this study by Shraer et al. [1], the authors undertake a detailed examination of AVFs in the context of AV repair and offer some important new insights. This topic is an important one for all AV repair surgeons for a number of reasons. Firstly, AVFs are common. From a review of 919 examined hearts from 4 autopsy studies, Zhu et al. [2] found that 55.9% of individuals presented with AVFs in their AV leaflets. The authors also found that fenestrations rarely manifested clinically or affected AV competence, through a review of 39 case reports and 3 case series with 63 patients. When involved in the mechanism of AI, AVFs tended to be larger, more central, and spontaneously ruptured. It is likely that AVFs are underreported due to limitations in identification methods, as they are not well seen on echocardiography [3, 4]. Despite being common, AVFs are infrequent contributors to AI. In a retrospective cohort study of 382 patients, Yang et al. [3] reported that 12 patients (3.1%) had fenestration-related AI. Furthermore, in a case series report of 1133 AV surgery patients, Cheruvu et al. [4] reported that 42 had AVFs and 26 had surgery for AI primarily. The frequent presence of AVFs in normal and regurgitant AVs, and their uncommon involvement in AI invokes a fundamental question: What is the role of AVFs in normal valve function and what is their contribution to the natural history of AI? It also creates an important conundrum for surgeons: Does the presence of AVFs signal a weakness in cusp tissue and thus represent a risk factor for late failure? Should AVFs be repaired even though they are not contributory to valvular insufficiency? In this issue of EJCTS, Shraer et al. [1] published a large, single-centre, cohort study that examines the impact of AVFs, respected or fixed, on AV repair surgery outcomes. From 2003 to 2019, 618 patients underwent AV repair and after propensity score matching, 167 pairs of patients in the non-AVF group and AVF group displayed similar survival (90.3% vs 95.8%), reoperation (6.7% vs 5.2%), grade ≥3 AI (6.4% vs 4.4%) and grade ≥2 AI (28.9% vs 37.1%) over the follow-up period. The authors concluded that the presence of AVFs did not impact survival, reoperation or significant AI. The authors used their clinical judgement and some guiding principles in deciding which AVFs to repair, which to leave alone and which AVFs warranted valve replacement. The guidelines that they followed were: fenestrations were respected if they (i) involved <1/2 of the free margin length and (ii) were thick (>1 mm) and has a solid free margin (large base of implantation). If larger, elongated or thin, the fenestrations were repaired using a running suture or decellularized bovine pericardial patch. Despite some of the limitations of this study including its single-centre design, incompleteness of follow-up, small numbers of repaired fenestrations, the conclusion of the study are important and support the notion that AVFs should generally not be a contraindication for AV repair, if other characteristics for potential successful repair is favourable. Our experiences and those from other groups also corroborate the principles articulated by the authors. In the majority of cases, AVFs are ‘innocent bystanders’ to the pathophysiology of AI and therefore, need not be repaired. However, what is yet unclear is whether certain characteristics of AVFs may be related to late failure. Future studies of AVFs should therefore, go beyond their mere presence or absence, but perhaps characterize in more detail the number of fenestrations, number of cusps involved, the cusp quality, their location, size and involvement within the coapting surface of the leaflets. With video capabilities being more readily available in operating rooms, there is a greater opportunity to visually capture many of these elements in a systematic manner and evaluate their impact on outcomes. These additional parameters would then need to be combined with other known predictors of recurrent AI (e.g. coaption length, effective height, preoperative left ventricular end-systolic diameter, post-repair annular diameters) to fully appreciate their impact [5]. As the field of AV repair matures, clinicians will need to focus their efforts further on these issues in order to further understand pathophysiology, standardize technical approaches and improve outcomes. Shraer et al. take an important step towards this process. It’s time to place AVFs in the spotlight!

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)0.009
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 score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0130.011
Insufficient payload (model declined to judge)0.0140.010

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 head0.039
GPT teacher head0.338
Teacher spread0.298 · 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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Citations0
Published2022
Admission routes1
Has abstractyes

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