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Record W4403822508 · doi:10.1093/eurheartj/ehae666.307

The correlation between cardiac magnetic resonance imaging and echocardiography in patients with severe ischaemic cardiomyopathy

2024· article· en· W4403822508 on OpenAlexaff
Tesfamariam Betemariam, Holly Morgan, Jennifer Mâncio, Ebraham Alskaf, M. J. Ryan, Jenny Draper, Alexandros Papachristidis, Roxy Senior, Stamatis Kapetanakis, Amedeo Chiribiri, Divaka Perera

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineMagnetic resonance imagingCardiologyCardiomyopathyInternal medicineCardiac magnetic resonanceCardiac magnetic resonance imagingCorrelationCardiac imagingRadiologyHeart failure

Abstract

fetched live from OpenAlex

Abstract Background Left ventricular ejection fraction is the single metric that guides therapeutic decision-making, risk stratification and prognosis in patients with severely impaired systolic function. Although cardiac magnetic resonance imaging (CMR) is the current reference standard for quantification of ventricular volume and function, echocardiography is used more often as it is more widely available and less expensive.(1) Aims To evaluate the correlation between CMR and echocardiography in quantifying left ventricular (LV) volume and function and to determine whether these measurements are associated with clinical outcomes in patients with severe ischemic cardiomyopathy (ICM). Methods Participants recruited to the REVIVED-BCIS2 trial who had undergone baseline CMR or echocardiography were included in this analysis. All scans were analyzed in blinded core laboratories as previously reported.(2,3) All volumes were indexed to body surface area. Spearman correlation, Bland Altman analysis and coverage probability methods were performed to assess the correlation between the two modalities. A Cox proportional hazards model was used to assess the association between imaging metrics and the primary outcome, a composite of all-cause death and aborted sudden death. A sensitivity analysis was conducted a priori, restricted to patients undergoing both scans within 60 days of each other. Results A total of 373 participants with paired imaging data were included: mean age 69±9 years, 87% male, BMI 28±5. A moderate correlation was detected for volume measurements between the modalities (end diastolic volume index (EDVI) r=0.66 end systolic volume index (ESVI) r=0.68), with a weaker correlation for LV ejection fraction (EF) (r=0.41). Bland-Altman plots showed moderate agreement in LVEF measurements (Table 1). The sensitivity analysis included 199 patients, and showed similar results (EDVI r=0.72, ESVI r=0.73 LVEF r=0.43, all p<0.000). Only 7.5% of the paired LVEF measurements were within 5% of each other. 30% of participants were misclassified by echocardiography as having an LVEF>35%. (Figure 1) CMR ESVI was associated with the occurrence of the primary outcome (HR 1.05 per 10ml increment, 95% CI 1.00-1.10) however echo ESVI (HR 1.05 per 10ml increment 95% CI 0.99 – 1.10) and all other imaging metrics were not. Conclusion Left ventricular volumes and ejection fraction on CMR and echocardiography were only modestly correlated in this population with severe ICM. When using the binary EF threshold of 35% that is commonly used to select patients of implantable defibrillator therapy, almost a third of patients at risk would have been missed by echocardiography alone. The additional insights provided by CMR, including scar burden, further support preferential use of this modality in the assessment such patients.

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)0.006
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
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.007
GPT teacher head0.230
Teacher spread0.222 · 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
Has abstractyes

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