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Record W4403808588 · doi:10.1093/eurheartj/ehae666.185

Thrombus attenuation predicts natural history of hypo-attenuating leaflet thickening after transcatheter aortic valve replacement

2024· article· en· W4403808588 on OpenAlexaff
Kajetan Grodecki, Jolien Geers, Vivek Patel, Dhruv Patel, Kazuki Suruga, Guadalupe Flores Tomasino, C Park, Aakriti Gupta, M Nakamura, Tarun Chakravarty, Daniel S. Berman, Piotr J. Slomka, Damini Dey, Hasan Jilaihawi, Raj Makkar

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsMedicineCardiologyThrombusThickeningValve replacementInternal medicineLeaflet (botany)Aortic valveNatural historyAortic valve replacementAttenuation

Abstract

fetched live from OpenAlex

Abstract Background Hypo-attenuating leaflet thickening (HALT) after transcatheter aortic valve replacement (TAVR) occurs in up to 25% of patients and is characterized by diverse temporal dynamics. Several studies investigated the natural history of HALT in transcatheter and surgical cohorts, but little is known about the imaging features of persistent thrombus. Purpose To examine the association of quantitative HALT volume and attenuation from computed tomography angiography (CTA) with its natural history. Methods We prospectively examined patients with HALT present on CTA at 30 days after TAVR and who underwent a follow-up CTA at one year. Hypoattenuation affecting motion (HAM) was defined as the presence of HALT with at least moderately reduced (>50%) leaflet motion. HALT was quantified by segmenting the inner volume of the bioprosthetic frame at the level of the leaflets and extracting voxels between a threshold of -200 to 200 HU. Thrombus volume, mean thrombus attenuation and thrombus heterogeneity measured with standard deviation of attenuation were automatically calculated (Figure 1). Results A total of 37 patients (70% men, 71±7 years old) undergoing TAVR with balloon-expandable SAPIEN3 (86%) and self-expanding EVOLUT (14%) valves were evaluated. Hypoattenuation affecting motion (HAM) at 30 days after TAVR was present in 29 (74%) patients. Patients with HAM had higher thrombus volume than patients without reduced leaflet motion (0.40cm3 [IQR 0.16 – 0.61cm3] vs 0.14 [IQR 0.06 – 0.19 cm3], p=0.009) but comparable mean attenuation values (145HU [IQR 141-151HU] vs 123 [IQR 114 – 141HU], p=0.079). Only 3 patients were switched to oral anticoagulation following HALT detection. At one year, HALT resolution was observed in 18/37 (48%) and HAM resolution in 19/29 (65%). No difference in quantitative thrombus features was noted between patients with persistent and resolved HALT. Notably, patients with persistent HAM had higher mean thrombus attenuation (141HU [IQR 123-156HU] vs 123HU [IQR 110 – 138 HU), p=0.045; Figure 2) and thrombus heterogeneity (58HU [IQR 51-63HU] vs 48HU [IQR 41- 54HU], p=0.021) than resolved HAM. Mean thrombus attenuation (area under the curve of 0.732, p=0.044) and thrombus heterogeneity (area under the curve of 0.763, p=0.022) were predictive of HAM resolution at one year. The optimal cutoff values as determined by Youden index was 140HU for mean attenuation and 52HU for heterogeneity. There was an increase in the mean transvalvular gradients between measurements at 30 days and one year in patients with persistent HAM as compared to patients with HAM resolution (Δ3mmHg [IQR -3 – 11mmHg] vs Δ-1mmHg [IQR -4 – 0mmHg], p=0.049). The volume of thrombus at one year was associated with the magnitude of change in transvalvular gradients (β = 10.3, 95% CI 1.6- 19.0, p=0.025). Conclusion Quantitative evaluation of HALT by CTA may aid the identification of patients at risk of persistent thrombus.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.319
Teacher spread0.288 · 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 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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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