Myocardial strain assessment using cardiovascular magnetic resonance imaging in recipients of implantable cardioverter defibrillators
Bibliographic record
Abstract
BACKGROUND: Cardiovascular magnetic resonance (CMR) is increasingly used in the evaluation of patients who are potential candidates for implantable cardioverter-defibrillator (ICD) therapy to assess left ventricular (LV) ejection fraction (LVEF), myocardial fibrosis, and etiology of cardiomyopathy. It is unclear whether CMR-derived strain measurements are predictive of appropriate shocks and death among patients who receive an ICD. We evaluated the prognostic value of LV strain parameters on feature-tracking (FT) CMR in patients who underwent subsequent ICD implant for primary or secondary prevention of sudden cardiac death. METHODS: Consecutive patients from 2 Canadian tertiary care hospitals who underwent ICD implant and had a pre-implant CMR scan were included. Using FT-CMR, a single, blinded, reader measured LV global longitudinal (GLS), circumferential (GCS), and radial (GRS) strain. Cox proportional hazards regression was performed to assess the associations between strain measurements and the primary composite endpoint of all-cause death or appropriate ICD shock that was independently ascertained. RESULTS: Of 364 patients (mean 61 years, mean LVEF 32%), 64(17.6%) died and 118(32.4%) reached the primary endpoint over a median follow-up of 62 months. Univariate analyses showed significant associations between GLS, GCS, and GRS and appropriate ICD shocks or death (all p < 0.01). In multivariable Cox models incorporating LVEF, GLS remained an independent predictor of both the primary endpoint (HR 1.05 per 1% higher GLS, 95% CI 1.01-1.09, p = 0.010) and death alone (HR 1.06 per 1% higher GLS, 95% CI 1.02-1.11, p = 0.003). There was no significant interaction between GLS and indication for ICD implant, presence of ischemic heart disease or late gadolinium enhancement (all p > 0.30). CONCLUSIONS: GLS by FT-CMR is an independent predictor of appropriate shocks or mortality in ICD patients, beyond conventional prognosticators including LVEF. Further study is needed to elucidate the role of LV strain analysis to refine risk stratification in routine assessment of ICD treatment benefit.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".