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Record W2744127303 · doi:10.1002/mp.12507

Sensitive three‐dimensional ultrasound assessment of carotid atherosclerosis by weighted average of local vessel wall and plaque thickness change

2017· article· en· W2744127303 on OpenAlexaff
Jieyu Cheng, Eranga Ukwatta, Shadi Shavakh, Tommy W. S. Chow, Grace Párraga, J. David Spence, Bernard Chiu

Bibliographic record

VenueMedical Physics · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsThrombosis and Atherosclerosis Research InstituteRobarts Clinical TrialsCarleton University
FundersNational Natural Science Foundation of ChinaCity University of Hong Kong
KeywordsMedicineUltrasoundRandomized controlled trialPlaceboAtorvastatinLumen (anatomy)RadiologyInternal medicineNuclear medicineCardiologyUrologyPathology

Abstract

fetched live from OpenAlex

PURPOSE: Vitamin B deficiency has been identified as a risk factor for vascular events. However, the reduction of vascular events was not shown in large randomized controlled trials evaluating B-Vitamin therapy. There is an important requirement to develop sensitive biomarkers to be used as efficacy targets for B-Vitamin therapy as well as other dietary treatments and lifestyle regimes that are being developed. Carotid vessel-wall-plus-plaque thickness change (VWT-Change) measured from 3D ultrasound has been shown to be sensitive to atorvastatin therapies in previous studies. However, B-Vitamin treatment is expected to confer a smaller beneficial effect in carotid atherosclerosis than the strong dose of atorvastatin. This paper introduces a sensitive atherosclerosis biomarker based on the weighted mean VWT-Change measurement from 3D ultrasound with a purpose to detect statistically significant effect of B-Vitamin therapy. METHODS: Of the 56 subjects analyzed in this study, 27 were randomized to receive a B-Vitamin tablet daily and 29 received a placebo tablet daily. Participants were scanned at baseline and 1.9 ± 0.8 yr later. The 3D VWT map at each scanning session was computed by matching the outer wall and lumen surfaces on a point-by-point basis. The 3D annual VWT-Change maps were obtained by first registering the 3D VWT maps obtained at the baseline and follow-up scanning sessions, and then taking the point-wise difference in VWT and dividing the result by the years elapsed from the baseline to the follow-up scanning session. The 3D VWT-Change maps constructed for all patients were mapped to a 2D carotid template to adjust for the anatomic variability of the arteries. A weight at each point of the carotid template was assigned based on the degree of correlation between the VWT-Change measurements exhibited at that point and the treatment received (i.e., B-Vitamin or placebo) quantified by mutual information. The weighted mean of VWT-Change for each patient, denoted by ΔVWT¯Weighted, was computed according to this weight. T-tests were performed to compare the sensitivity of ΔVWT¯Weighted with existing biomarkers in detecting treatment effects. These biomarkers included changes in intima-media thickness (IMT), total plaque area (TPA), vessel wall volume (VWV), unweighted average of VWT-Change (ΔVWT¯) and a previously described biomarker, denoted by ΔVWT¯S, that quantifies the mean VWT-Change specific to regions of interest identified by a feature selection algorithm. RESULTS: Among the six biomarkers evaluated, the effect of B Vitamins was detected only by ΔVWT¯Weighted in this cohort (P=4.4×10-3). The sample sizes per treatment group required to detect an effect as large as exhibited in this study were 139, 178, 41 for ΔVWV, ΔVWT¯ and ΔVWT¯Weighted respectively. CONCLUSION: The proposed weighted mean of VWT-Change is more sensitive than existing biomarkers in detecting treatment effects. This measurement tool will allow for many proof-of-principal studies to be performed for various novel treatments before a more costly study involving a larger population is held to validate the results.

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)0.000
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 score0.263
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.022
GPT teacher head0.297
Teacher spread0.275 · 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".

Quick stats

Citations19
Published2017
Admission routes1
Has abstractyes

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