The correlation between cardiac magnetic resonance imaging and echocardiography in patients with severe ischaemic cardiomyopathy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".