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Record W2765602292 · doi:10.1093/ehjci/jex258

Outcome of aortic valve replacement in aortic stenosis: the number of valve cusps matters

2017· letter· en· W2765602292 on OpenAlexafffund
Philippe Pîbarot, Marie‐Annick Clavel

Bibliographic record

VenueEuropean Heart Journal - Cardiovascular Imaging · 2017
Typeletter
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersCanadian Institutes of Health Research
KeywordsStenosisCardiologyInternal medicineAortic valve replacementAortic valveMedicineAortic valve stenosisValve replacement

Abstract

fetched live from OpenAlex

Although the prevalence of bicuspid aortic valve (BAV) is only 0.5–1% in children, it accounts for about half of aortic valve replacements (AVRs) for aortic valve stenosis (AS). These findings illustrate that valve remodelling occurs more frequently and more rapidly on bicuspid than on tricuspid aortic valves (TAVs). During their lifetime, most subjects with a BAV develop aortic valve dysfunction, mostly AS, whereas only 1% remains with a normal valve function.1 In both subjects with a BAV and those with a TAV, age is a powerful risk factor for AS, and the prevalence of this disease in the population is 3% and 10%, in the subjects >65 and 80 years of age, respectively.2 Individuals with a BAV, however, develop AS one or two decades earlier than those with a TAV and their lifetime risk of AVR is around 50% (Table 1). In this issue of the journal, Huntley et al.3 present an elegant study in which they compared age-matched cohorts of 198 BAV stenosis and 198 TAV stenosis patients. The authors found that, at a given age, patients with TAV stenosis have higher prevalence of cardiovascular risk factors, greater degree of cardiac impairment and worse survival after AVR compared to those with BAV stenosis (Table 1). Similarities and differences between BAV and TAV stenosis AS, aortic valve stenosis; AVR, aortic valve replacement; BAV, bicuspid aortic valve; Lp(a), lipoprotein(a); TAV, tricuspid aortic valve. Similarities and differences between BAV and TAV stenosis AS, aortic valve stenosis; AVR, aortic valve replacement; BAV, bicuspid aortic valve; Lp(a), lipoprotein(a); TAV, tricuspid aortic valve. The mutations that have been identified in families with BAV are in the NOTCH1 and GATA5 genes2,4 (Table 1). The NOTCH1 mutations are associated with both BAV phenotype and derepression of aortic valve calcium deposition. Hence, these mutations not only predispose to the development of a BAV but they also promote valve mineralization later in life, therefore exacerbating the risk of developing AS. In subjects with TAV, studies using a candidate gene approach have identified several genes (Table 1). However, these associations still need to be confirmed in larger samples. Recent large studies using a Mendelian randomization design have identified the single-nucleotide polymorphism (rs10455872) at the LPA gene locus as the only genome-wide significant single-nucleotide polymorphism associated with the presence of aortic valve calcification and clinical AS.5 The clinical risk factors associated with AS are similar to those associated with atherosclerosis and include older age, male sex, smoking, hypertension, hypercholesterolaemia, obesity, metabolic syndrome, diabetes, and elevated lipoprotein(a) [Lp(a)] (Table 1).2 There is, however, no evidence that these cardiovascular risk factors would have more pronounced effect on the initiation or progression of AS in TAV vs. BAV subjects. By matching the BAV and TAV cohorts, Hunter et al.3 adjusted for the most powerful risk factor of AS, i.e. age. The cohorts were also well matched with respect to sex, which has been shown to have an important effect on the pathobiology and outcome of AS.2,6 The prevalence of cardiovascular risk factors such as obesity, diabetes, hypertension, and hypercholesterolaemia as well as that of atherosclerotic diseases such as coronary artery disease and peripheral vascular disease were much higher in the TAV stenosis cohort than in the BAV stenosis cohort, despite similar age and sex distribution in both cohorts.3 The most striking difference between the two cohorts was for diabetes, which prevalence was 2.4 higher in TAV (46 vs. 19%). Obesity, metabolic syndrome, and diabetes are among the risk factors that exhibit the strongest association with AS incidence, progression, and outcomes.7,8 Furthermore, these cardiometabolic risk factors are also associated with higher risk of structural valve deterioration following AVR with a bioprosthesis.9 Hypertension has also been reported to have an important effect on the pathophysiology and outcomes of AS both prior and after AVR.10,11 The marked over-representation of cardiovascular risk factors in the TAV cohort vs. the BAV cohort after age matching3 provides support to the concept that, in middle-age TAV subjects, the progression rate, and clinical outcome of calcific AS are, in large part, driven by cardiometabolic risk factors (Table 1). In BAV patients, the aforementioned genetic factors as well as mechanical factors related to the BAV configuration (i.e. increased mechanical stress on valve leaflets and turbulent transvalvular flow) likely have a predominant contribution to the pathogenesis of AS and these factors may occult—or compete with—the effects of cardiovascular risk factors on the course of aortic valve disease (Table 1). In their study, Hunter et al.3 did not report the prevalence of metabolic syndrome and Lp(a). One would expect that plasma levels of Lp(a), a potential causal factor of calcific AS, would be higher in the TAV cohort than in the BAV cohort. The differences in the baseline risk profile between the TAV and BAV stenosis cohorts largely explain the worse cardiac function and lower survival rates observed in the TAV cohort (Table 1).3 Interestingly, the 5-year survival rate (79%) in the BAV cohort was comparable to the expected survival in the general population (86%), whereas in the TAV cohort, survival was substantially lower (61%). In the multivariable analysis, the Charlson comorbidity index but not the aortic valve phenotype was associated with increased risk of mortality after AVR. This is consistent with the fact that differences in outcomes between TAV and BAV are, in large part, related to differences in baseline risk profile and comorbidities (Table 1). Compared with subjects with TAV, those with BAV have larger aortic annulus and thus lower prevalence of small prosthetic valves and ensuing prosthesis–patient mismatch following AVR. In this study,3 prosthesis–patient mismatch was indeed more frequent in TAV than in BAV patients (37 vs. 25%; P = 0.019) and was strongly and independently associated with increased risk of mortality. This finding may also contribute to explain the worse survival observed in TAV vs. BAV patients. This also further emphasizes the importance of avoiding prosthesis–patient mismatch in patients with severe AS undergoing AVR. If you are born with a normal TAV, you likely need to have cardiovascular risk factors such as metabolic syndrome, diabetes, hypertension, dyslipidaemia, or high Lp(a), to develop AS at a young or middle age. On the other hand, if you are born with a BAV, you have a high likelihood to develop AS at relatively young age, even in the absence of any of these risk factors. As expected, BAV patients more frequently have concomitant aortopathy, which does not appear to alter their prognosis after AVR. On the other hand, these patients have larger aortic annulus and thus lower risk of prosthesis–patient mismatch following AVR, compared to TAV patients. This difference further contributes to the better survival of BAV vs. TAV patients after AVR. Future interventional studies should focus on aggressive cardiovascular risk factor management to improve outcomes after AVR, particularly in the subset of patients with a TAV. P.P. holds the Canada Research Chair in Valvular Heart disease and his research program is funded by a Foundation grant (FDN-143225) from Canadian Institutes of Health Research (Ottawa, Ontario, Canada). M.-A.C. received a research scholarship from Fonds de Recherche en Santé du Québec. Conflict of interest: None declared.

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)0.008
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 score0.006
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0020.002

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 head0.031
GPT teacher head0.339
Teacher spread0.308 · 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
Published2017
Admission routes2
Has abstractyes

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