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Abstract 4144554: Revascularization of Patients with Low-Density Non-Calcified Plaque was Associated with Lower Occurrence of Acute Coronary Syndrome

2024· article· en· W4404246446 on OpenAlexaff
Shant Malkasian, Logan Hubbard, Nick S. Nurmohamed, Hyuk‐Jae Chang, Andrew Choi, Hugo Marques, Alexander van Rosendael, Benjamin J.W. Chow, Daniele Andreini, Edoardo Conte, Erica Maffei, Fabian Plank, Gianluca Pontone, Gudrun Feuchtner, Habib Samady, Hyung‐Bok Park, Ibrahim Danad, Iksung Cho, Jeroen Bax, Ji Hyun Lee, Lohendran Baskaran, Martin Hadamitzky, Matthew J. Budoff, Peter H. Stone, Ran Heo, Ricardo C. Cury, Sang‐Eun Lee, Subhi J. Al’Aref, Wijnand J. Stuijfzand, Yao Lu, Yong‐Jin Kim, Filippo Cademartiri, Renu Virmani, Jagat Narula, Asim Rizvi, Kavitha M. Chinnaiyan, Todd C. Villines, Jonathon Leipsic, Leslee Shaw, Sabee Molloi

Bibliographic record

VenueCirculation · 2024
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsUniversity of British ColumbiaSt. Paul's HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineAcute coronary syndromeCardiologyRevascularizationInternal medicineVulnerable plaqueMyocardial infarction

Abstract

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INTRODUCTION: Coronary CT angiography (CCTA) is a powerful noninvasive tool for identifying high-risk plaque, such as low-density non-calcified plaque (LD-NCP). Though, the optimal treatment of patients with LD-NCP remains unclear. This study explored the association of revascularization in the setting of LD-NCP with the occurrence of acute coronary syndrome (ACS). Methods: This was a post-hoc analysis of the ICONIC study. A subset of 234 patients that underwent CCTA with subsequent ACS were matched to 234 control patients who also underwent CCTA but did not have ACS during follow-up. Patients were also followed for occurrence of revascularization, either coronary artery bypass graft or percutaneous coronary intervention. Atherosclerosis imaging-enabled quantitative CT (AI-QCT) was used to measure diameter stenosis, and LD-NCP, non-calcified plaque, and calcified plaque volumes from each CCTA. LD-NCP was defined as plaque with -190 to 30 Hounsfield Units. Patients were stratified based on the presence of LD-NCP. Subgroup analysis was conducted to compare the occurrence of ACS with the rate of revascularization. Kaplan-Meier survival curves and extended Cox regression analysis were used to evaluate the effect size of revascularization and LD-NCP on occurrence of ACS. Results: AI-QCT was completed in 448/468 subjects (follow-up time [MEAN±SD] 2.44±2.48 years). The median of LD-NCP was 1.2 mm 3 for patients with >0 mm 3 LD-NCP. There were 85 patients with LD-NCP >1.2 mm 3 and 363 patients with LD-NCP ≤1.2 mm 3 . In patients with LD-NCP >1.2 mm 3 , the rate of revascularization in patients with and without ACS was 3/52 (5.8%) versus 14/33 (42.4%) (p<0.001). In patients with LD-NCP ≤1.2 mm 3 , the rate of revascularization in patients with and without ACS was 36/170 (21.2%) versus 39/193 (20.2%) (p=0.897). In comparison to patients without revascularization and LD-NCP ≤1.2 mm 3 , patients with LD-NCP >1.2 mm 3 and revascularization were less likely to have ACS during follow-up (adjusted HR: 0.20 [0.07, 0.61]; p=0.005). Additionally, patients with LD-NCP >1.2 mm 3 who did not undergo revascularization were more likely to have ACS (adjusted HR: 1.47 [1.03, 2.12]; p=0.036). Hazard ratios were adjusted for diameter stenosis, and non-calcified and calcified plaque volume. Time-dependent coefficients were included for diameter stenosis. Conclusion: Revascularization of patients with LD-NCP >1.2 mm 3 identified on CCTA with AI-QCT was associated with less risk for ACS.

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)0.001
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.008
GPT teacher head0.227
Teacher spread0.218 · 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".

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Citations0
Published2024
Admission routes1
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