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Record W2981372920 · doi:10.1093/eurheartj/ehz747.0021

92Transcatheter aortic valve implantation improves cerebral blood flow and cognitive function in elderly patients with aortic stenosis - Brain perfusion SPECT imaging study

2019· article· en· W2981372920 on OpenAlexaboutno aff
Satoshi Tsuchiya, Yasuharu Matsumoto, Hideaki Suzuki, Yoku Kikuchi, Jun Sugisawa, Tomohiko Shindo, Kiyotaka Hao, Masashi Takeuchi, Jun Takahashi, Koji Kumagai, Toshihiro Wagatsuma, Yoshikatsu Saiki, Hiroaki Shimokawa

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGeriatric Depression ScaleCerebral blood flowCardiologyInternal medicineCognitionStenosisEffects of sleep deprivation on cognitive performanceBicuspid aortic valvePsychiatryDepressive symptoms

Abstract

fetched live from OpenAlex

Abstract Background Cognitive impairment and depression are commonly noted in elderly frail patients with severe aortic stenosis (AS). However, their mechanisms and reversibility after treatment remain remain to be examined. Purpose In this study, we examined whether transcatheter aortic valve implantation (TAVI) increases cerebral blood flow (CBF) in cognitive/emotional brain areas, such as the hippocampus, in the elderly patients with severe AS. Methods We examined consecutive 15 right-handed patients with severe AS who were eligible for TAVI (median age 83.2 years, 12 (80%) women). We evaluated the following assessments both at baseline and 3 months after TAVI. Frailty was evaluated based on Fried scale, which consists of 5 items, including gait speed, cognitive function, weight loss, exhaustion, and inactivity. Frail and pre-frail were defined as greater than 3 and 1∼2 in the scale, respectively. Cognitive and emotional status were evaluated with Logical Memory (LM) II, Mini Mental State Examination (MMSE), and geriatric depression scale (GDS). Lower LM II (less than 4 points) and MMSE (less than 24 points) and higher GDS (more than 6 points) indicate worse memory, general cognitive function, and depressive symptoms, respectively. CBF images were recorded with 99mTc single-photon emission computed tomography and were analyzed using SPM12. Briefly, CBF images were firstly normalized to the standard Montreal Neurological Institute space. Then, a voxel-wise parametric analysis was conducted between normalized CBF images at baseline and those after TAVI (P<0.005 at each voxel). Continuous variables were presented as mean ± standard error (SE). Normality was assessed using the Shapiro-Wilk test. Continuous variables were compared with the use of paired t test. Linear mixed-model analysis was performed to evaluate changes in neuropsychological tests and CBF over time. Results In the present study, all patients were not robust but pre-frail (47.3%) or frail (53.7%). LM II score was significantly improved at 3 months after TAVI compared with baseline (baseline, 8.7 vs. 3 months, 13.8, P<0.01) (Figure A), whereas no significant changes in MMSE or GDS scores were noted (baseline, 24.6 vs. 3 months, 25.2 for MMSE; baseline, 4.3 vs. 3 months, 4.2 for GDS). Importantly, although no patients showed clinical symptoms or signs for transient ischemic attack or stroke after TAVI, CBF in the local regions, including the right hippocampus, was significantly increased after TAVI compared with baseline (P<0.005 at each voxel) (green arrowheads) (Figure B). Furthermore, CBF in the right hippocampus were positively correlated with LM II scores (P=0.017) (Figure C). Figure 1 Conclusions These results provide the first evidence that TAVI improves cerebral perfusion (especially that in the hippocampus) and cognitive functions in elderly patients with severe AS.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.334
Teacher spread0.304 · 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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Citations1
Published2019
Admission routes1
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