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Record W4200324868 · doi:10.2459/jcm.0000000000001240

Sarcopenia detected by computed tomography: a simple tool for screening transcatheter aortic valve implantation candidates

2021· article· en· W4200324868 on OpenAlexaff

Bibliographic record

VenueJournal of Cardiovascular Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsSarcopeniaSimple (philosophy)Aortic valveAortic Valve InsufficiencyMEDLINE

Abstract

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Frailty, defined as a clinical state that makes the individual more vulnerable to the effects of stressors, is common in elderly patients affected by aortic stenosis and/or heart failure with a wide range of prevalence based on the tools used.1–4 The assessment of frailty is crucial in the management of patients with severe aortic stenosis, as it can drive the choice between surgery, transcatheter aortic valve implantation (TAVI) or conservative care.5 Indeed, international guidelines strongly recommend to objectively evaluate frailty before planning either surgical or percutaneous valve interventions using the Katz activities of daily living score and the gait speed by the 5-min walking test.6 Other tools for frailty screening and assessment have been even proposed.2,7 Sarcopenia and frailty are strongly related and both are associated with poor outcomes in several cardiovascular settings.8–13 The assessment of sarcopenia by psoas muscle area using computed tomography (CT) recently emerged as a possible tool for screening TAVI recipients. However, evidence regarding its prognostic impact diverges.14,15 In the current issue, Walpot et al.16 sought to retrospectively evaluate the role of psoas muscle attenuation (PMA) assessed by CT in predicting long-term all-cause mortality after TAVI. They analysed 94 consecutive TAVI patients with a median age of 81 years and at intermediate surgical risk (median STS score 4.7%). Common clinical frailty scores were also evaluated. Assessment of PMA was expressed by three variables: psoas mean Hounsfield Units (HU), circumferential surface area low-density muscle (CSA LDM%) and high-density muscle over low-density muscle ratio (HDM/LDM). These measurements were performed using postprocessing CT images routinely obtained during the preprocedural planning. They were reproducible with a low interobserver and intraobserver variability. Psoas muscle attenuation was found to be a strong and independent predictor of clinical outcome after TAVI being associated with an up-to five-fold increased risk of 5-year all-cause mortality. The results of this study clarify and confirm the role of sarcopenia assessed by CT in patients undergoing TAVI. Specific measurements of PMA [mean HU, CSA LDM (%) and HDL/LDM ratio], rather than a mere evaluation of psoas muscle area may help to identify patients more likely to be frail and to have a poor outcome. Another important finding is that PMA was sex-independent and not subject to normalization to body surface area, further increasing its appeal for routine clinical use. Moreover, as well stated by the authors, the advantage of this tool is that it is simple and reproducible and can be obtained by postprocessing the standard pre-TAVI CT images with no additional radiation load. The authors need to be congratulated also for the long follow-up reported, of almost 5 years, which is definitely longer compared with other similar studies. On the contrary, the findings of Walpot et al.16 are limited by the retrospective nature of the study and by the small sample size, which make them hypothesis-generating only. Larger and prospective studies are needed to confirm these interesting results and to identify possible thresholds that may further help in the stratification of TAVI candidates according to their frailty status. Moreover, it would be interesting to investigate the role of nutritional treatment and physiotherapy in this setting.17 The unexplored synergic effect of intervention on aortic valve disease and therapies for frailty/sarcopenia might perhaps be the in the management of these patients. Conflicts of interest There are no conflicts of interest.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.007
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.295
Teacher spread0.284 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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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Citations2
Published2021
Admission routes1
Has abstractyes

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