Abstract 16938: Late Gadolinium Enhancement on Cardiac Magnetic Resonance as Predictor of Overall Mortality in Patients With Non-Ischemic Cardiomyopathy
Bibliographic record
Abstract
Background: Patients with heart failure due to non-ischemic cardiomyopathy (NICM) constitute a heterogenous group of patients with diverse prognosis. The presence of late gadolinium enhancement (LGE) by cardiac magnetic resonance imaging represents a measure of myocardial damage and can provide prognostic information. Objective: This study aimed to evaluate the prognostic role of the presence and pattern of LGE in patients with NICM . Methods: Using the NICM registry, we conducted a retrospective cohort study of adult patients with NICM, undergoing CMRI between 1999–2016. Using multivariable Cox regression model and Fine and Gray’s competing risk regression model, we evaluated the association between LGE presence and patterns with all-cause mortality, heart transplant, and ventricular assist device (VAD). We adjusted the model for clinically important covariates. Results: Our cohort of 1842 patients showed a mean age of 56 ± 14, 73% male sex, 30% NYHA class III-IV, 15% diabetic and 89% had idiopathic dilated cardiomyopathy. During a median follow-up of 2.3 years, we observed 215 composite outcome of all-cause mortality, transplant, or VAD. LGE was present in 56% of our cohort: 18% subendocardial, 18% mid-wall, 17% diffuse pattern, and 3% subepicardial. LGE was significantly associated with a 2-fold increased risk of events (HR 2.0, 95%CI 1.4-3.0) (figure). Of all LGE patterns, subendocardial, midwall, and diffuse LGE patterns were associated with the worst prognosis. (table). Conclusions: In patients with NICM, LGE in significantly associated with increased risk of death or need for advanced heart failure therapies. The use of CMR in patients with NICM provides both diagnostic and prognostic information.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".