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Record W3109379693 · doi:10.1186/s12968-020-00680-6

Layer-specific strain in patients with heart failure using cardiovascular magnetic resonance: not all layers are the same

2020· article· en· W3109379693 on OpenAlexafffundabout
Lingyu Xu, Joseph J. Pagano, Mark J. Haykowksy, Justin A. Ezekowitz, Gavin Y. Oudit, Yoko Mikami, Andrew G. Howarth, James A. White, Jason R.B. Dyck, Todd J. Anderson, D. Ian Paterson, Richard B. Thompson, Jennifer Beck, Lisa M.H. Tanguay, Beverly Armstrong, Marleen Irwin

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsLibin Cardiovascular Institute of AlbertaCanadian VIGOUR CentreUniversity of CalgaryUniversity of Alberta
FundersAlberta Health ServicesAlberta Heritage Foundation for Medical ResearchCanadian Institutes of Health ResearchLi Ka Shing FoundationUniversity of Texas at ArlingtonAlberta InnovatesHeart and Stroke Foundation of Canada
KeywordsAngiologyMedicineHeart failureCardiologyCardiac magnetic resonanceMagnetic resonance imagingInternal medicineStrain (injury)Nuclear magnetic resonanceRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: Global longitudinal strain (GLS), most commonly measured at the endocardium, has been shown to be superior to left ventricular (LV) ejection fraction (LVEF) for the identification of systolic dysfunction and prediction of outcomes in heart failure (HF). We hypothesized that strains measured at different myocardial layers (endocardium = ENDO, epicardium = EPI, average = AVE) will have distinct diagnostic and predictive performance for patients with HF. METHODS: Layer-specific GLS, layer-specific global circumferential strain (GCS) and global radial strain (GRS) were evaluated by cardiovascular magnetic resonance imaging (CMR) feature tracking in the Alberta HEART study. A total of 453 subjects consisted of healthy controls (controls, n = 77), at-risk for HF (at-risk, n = 143), HF with preserved ejection fraction (HFpEF, n = 87), HF with mid-range ejection fraction (HFmrEF, n = 88) and HF with reduced ejection fraction (HFrEF, n = 58). For outcomes analysis, CMR-derived imaging parameters were adjusted with a base model that included age and N-terminal prohormone of b-type natriuretic peptide (NT-proBNP) to test their independent association with 5-year all-cause mortality. RESULTS: GLS_EPI distinguished all groups with preserved LVEF (controls - 16.5 ± 2.4% vs. at-risk - 15.5 ± 2.7% vs. HFpEF - 14.1 ± 3.0%, p < 0.001) while GLS_ENDO and all GCS (all layers) were similar among these groups. GRS was reduced in HFpEF (41.1 ± 13.8% versus 48.9 ± 10.7% in controls, p < 0.001) and the difference between GLS_EPI and GLS_ENDO were significantly larger in HFpEF as compared to controls. Within the preserved LVEF groups, reduced GRS and GLS_EPI were significantly associated with increased LV mass (LVM) and LVM/LV end-diastolic volume EDV (concentricity). In multivariable analysis, only GLS_AVE and GRS predicted 5-year all-cause mortality (all ps < 0.05), with the strongest association with 5-year all-cause mortality by Akaike Information Criterion analysis and significant incremental value for outcomes prediction beyond LVEF or GLS_ENDO by the likelihood ratio test. CONCLUSION: Global strains measured on endocardium, epicardium or averaged across the wall thickness are not equivalent for the identification of systolic dysfunction or outcomes prediction in HF. The endocardium-specific strains were shown to have poorest all-around performance. GLS_AVE and GRS were the only CMR parameters to be significantly associated with 5-year all-cause mortality in multivariable analysis. GLS_EPI and GRS, as well as the difference in endocardial and epicardial strains, were sensitive to systolic dysfunction among HF patients with normal LVEF (> 55%), in whom lower strains were associated with increased concentricity.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.221
Teacher spread0.199 · 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.

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

Citations37
Published2020
Admission routes3
Has abstractyes

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