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Record W7008342745

Carotid artery biomechanical parameters as measured with ultrasound elastography in HIV individuals – an assessment of the association to coronary atherosclerosis and comparison to traditional cardiovascular risk factors

2023· dissertation· en· W7008342745 on OpenAlexaboutno aff

Bibliographic record

VenuePapyrus : Institutional Repository (Université de Montréal) · 2023
Typedissertation
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCoronary atherosclerosisFramingham Risk ScoreUltrasoundPoisson regressionReceiver operating characteristicCohortCoronary artery diseaseAtherosclerosis Risk in CommunitiesProspective cohort studyUnivariate analysis
DOInot available

Abstract

fetched live from OpenAlex

Aim: This study aims to assess the association of biomechanical characteristics of carotid walls and carotid intima-media thickness (IMT), as assessed by ultrasound, when incorporated into prediction models for coronary CT plaque burden in both people living with HIV (PLWH) and HIV-negative control individuals. Methods: In this cross-sectional study, 164 participants (mean age 57 years ± 8 years; 134 males) with low to intermediate cardiovascular risk were recruited from the ongoing prospective Canadian HIV and Aging Cohort Study (CHACS). Among the 164 recruited participants, a total of 154 individuals (mean age, 56.5 years ± 7.55 years; 83 PLWH, 54%; 137 males; 88%) were evaluated. Ten participants were excluded due to unavailable coronary plaque data. The mean time interval between coronary CT and carotid ultrasound per participant was 7.69 ± 20.1 months. Using ultrasound, cumulated axial strain, cumulated shear strain, cumulated axial translation, cumulated lateral translation, and IMT of the common and internal carotid arteries were measured. Participants also underwent cardiac CT for coronary plaque assessment. Univariate and multivariate Poisson regression analyses with robust variance were performed to identify independent associations of cardiovascular risk factors, IMT, and elastography parameters with coronary plaque presence. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to compare different prediction models for coronary plaque presence. Results: The study included 83 PLWH and 71 controls (N=154). The median 10-year Framingham risk score was 12% [IQR, 8 - 16] in PLWH and 9% [IQR, 7 - 15] in controls (p = 0.045). In the PLWH group, coronary plaques were observed in 55 participants (61.1%) compared to 42 (56.8%) in the non-HIV control group (p = .46). Carotid IMT and all elastography features for both the internal and common carotid arteries were similar between PLWH and healthy volunteers. 4 After adjusting for cardiovascular risk using multivariate Poisson regression, smoking exposure was significantly associated with coronary plaque presence on CT (prevalence ratio 1.10, 95% CI 1.04 – 1.13, p < 0.001). No significant associations were found with other coronary artery disease risk factors or HIV status in multivariate analysis. Carotid elastography parameters and carotid intima-media thickness were not associated with coronary atherosclerosis after adjustment. AUC analyses did not reveal any significant differences in predictive accuracy between models when adding either elastography parameters, IMT, or both elastography parameters and IMT results to the cardiovascular risk factor model, with AUC ranging from 0.647 to 0.681 in all models. Conclusion: In our study, models incorporating carotid elastography and IMT did not enhance the prediction of coronary plaque presence in PLWH or controls, compared to models including only traditional cardiovascular risk factors. Key words: HIV, computed tomography, angiography, us elastography, atherosclerosis

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)0.002
metaresearch head score (Gemma)0.004
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.237
Teacher spread0.219 · 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
Published2023
Admission routes1
Has abstractyes

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