Prognostic value of cardiovascular magnetic resonance left ventricular volumetry and geometry in patients receiving an implantable cardioverter defibrillator
Bibliographic record
Abstract
Current indications for implantable cardioverter defibrillator (ICD) implantation for sudden cardiac death prevention rely primarily on left ventricular (LV) ejection fraction (LVEF). Currently, two different contouring methods by cardiovascular magnetic resonance (CMR) are used for LVEF calculation. We evaluated the comparative prognostic value of these two methods in the ICD population, and if measures of LV geometry added predictive value. In this retrospective, 2-center observational cohort study, patients underwent CMR prior to ICD implantation for primary or secondary prevention from January 2005 to December 2018. Two readers, blinded to all clinical and outcome data assessed CMR studies by: (a) including the LV trabeculae and papillary muscles (TPM) (trabeculated endocardial contours), and (b) excluding LV TPM (rounded endocardial contours) from the total LV mass for calculation of LVEF, LV volumes and mass. LV sphericity and sphere-volume indices were also calculated. The primary outcome was a composite of appropriate ICD shocks or death. Of the 372 consecutive eligible patients, 129 patients (34.7%) had appropriate ICD shock, and 65 (17.5%) died over a median duration follow-up of 61 months (IQR 38–103). LVEF was higher when including TPM versus excluding TPM (36% vs. 31%, p < 0.001). The rate of appropriate ICD shock or all-cause death was higher among patients with lower LVEF both including and excluding TPM ( p for trend = 0.019 and 0.004, respectively). In multivariable models adjusting for age, primary prevention, ischemic heart disease and late gadolinium enhancement, both LVEF (HR per 10% including TPM 0.814 [95%CI 0.688–0.962] p = 0.016, vs. HR per 10% excluding TPM 0.780 [95%CI 0.639–0.951] p = 0.014) and LV mass index (HR per 10 g/m 2 including TPM 1.099 [95%CI 1.027–1.175] p = 0.006; HR per 10 g/m 2 excluding TPM 1.126 [95%CI 1.032–1.228] p = 0.008) had independent prognostic value. Higher LV end-systolic volumes and LV sphericity were significantly associated with increased mortality but showed no added prognostic value. Both CMR post-processing methods showed similar prognostic value and can be used for LVEF assessment. LVEF and indexed LV mass are independent predictors for appropriate ICD shocks and all-cause mortality in the ICD population.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".