Propensity score-matched analysis in isolated left ventricular dilation and non-ischaemic dilated cardiomyopathy
Bibliographic record
Abstract
Abstract Introduction The presence and extent of late gadolinium enhancement (LGE) assessed by cardiac magnetic resonance imaging (CMR) are strong prognosticators of death in patients with non-ischaemic dilated cardiomyopathy (DCM), defined as left ventricular (LV) dilation and left ventricular ejection fraction (LVEF)<50%. Although the current guidelines defined the concept of "isolated LV dilation" (ILVD) as LV dilation with preserved LVEF≥ 50%, the prognostic value of the "LGE granularity" is not established in this population. Purpose To assess the prognostic value of the concept of "LGE granularity" including its extent, location, and pattern for predicting all-cause death above traditional prognosticators in patients with DCM or ILVD, separately. Methods Between 2008 and 2021, all consecutive patients with DCM and ILVD without implantable cardioverter-defibrillator or history of sustained ventricular arrhythmia referred for CMR were included in two centres. The primary outcome was all-cause death using the French National Registry of Death. A propensity score matching was performed to balance characteristics in patients with DCM vs. those with ILVD. Cox regressions were performed to determine the prognostic value of each LGE findings. Results Of 2,752 patients analysed (age 52±8 years, 56% male), 15% patients died after a median (interquartile range) follow-up of 9 (7-12) years. A total of 737 (27%) patients had LGE. In the propensity-score matched population (N=1,084 in DCM subgroup and N=1,084 in isolated LV dilation), the LGE presence was associated with death (HR=2.98, 95%CI: 1.97-4.50, p<0.001). In ILVD patients with LGE (N=265), the LGE extent (HR=1.41, 95%CI:1.09-1.83, p=0.009), the presence of LGE in multiple areas (HR=3.86, 95%CI: 1.73-8.61, p<0.001) and the septal location (HR=2.97, 95%CI:1.37-6.46, p=0.006) were strong prognosticators of death after adjustment for traditional prognosticators. Similarly, in DCM patients with LGE (N=268), the LGE extent (HR=1.42, 95% CI:1.07-1.89, p=0.014), the presence of LGE in multiple areas (HR=8.41, 95%CI: 3.32-21.3, p<0.001) and the septal location (HR=6.65, 95% CI: 3.02-14.6, p<0.001) were strongly associated with death. Conclusion The concept of "LGE granularity" was independently associated with all-cause death after adjustment for all traditional prognosticators in both DCM and ILVD. These results suggest that CMR assessment could improve the risk stratification in this population. Survival curves Multivariable analysis
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".