Objective criteria for septal fibrosis in non-ischemic dilated cardiomyopathy: validation for the prediction of future cardiovascular events
Bibliographic record
Abstract
BACKGROUND: Expert subjective reporting of mid-wall septal fibrosis on late gadolinium enhancement (LGE) images has been shown to predict major cardiovascular outcomes in patients with non-ischemic dilated cardiomyopathy (NIDCM). This study aims to establish objective criteria for non-experts to report clinically relevant septal fibrosis and compare its performance by such readers versus experts for the prediction of cardiovascular events. METHODS: LGE cardiovascular magnetic resonance (CMR) was performed in 118 consecutive patients with NIDCM (mean age 57 ± 14, 42 % female) and the presence of septal fibrosis scored by expert readers. CMR-naive readers performed signal threshold-based LGE quantification by referencing mean values of remote tissue and applying these to a pre-defined anatomic region to measure septal fibrosis. All patients were followed for the primary composite outcome of cardiac mortality or appropriate implantable cardioverter-defibrillator (ICD) therapy. RESULTS: The mean LVEF was 32 ± 12 %. At a median follow-up of 1.9 years, 20 patients (17 %) experienced a primary composite outcome. Expert visual scoring identified 55 patients with septal fibrosis. Non-expert septal fibrosis quantification was highly reproducible and identified mean septal fibrosis burden for three measured thresholds as follows; 5SD: 2.9 ± 3.6 %, 3SD: 6.9 ± 6.3 %, and 2SD: 11.1 ± 7.5 % of the left ventricular (LV) mass, respectively. By ROC analysis, optimal thresholds for prediction of the primary outcome were; 5SD: 2.74 % (HR 8.7, p < 0.001), 3SD: 6.63 % (HR 5.7, p = 0.001) and 2SD: 10.15 % (HR 6.1, p = 0.001). By comparison, expert visual scoring provided a HR of 5.3 (p = 0.001). In adjusted analysis, objective quantification by a novice reader (>5SD threshold) was the strongest independent predictor of the primary outcome (HR 8.7) and provided improved risk reclassification beyond LVEF alone (NRI 0.54, 95 % CI 0.16-0.92, p = 0.005). CONCLUSIONS: Novice readers were able to achieve superior risk prediction for future cardiovascular events versus experts using objective criteria for septal fibrosis in patients with NIDCM. Patients with a septal fibrosis burden >2.74 % of the LV mass (>5SD threshold) were at a 9-fold higher risk of cardiac death or appropriate ICD therapy versus those not meeting this criteria. As such, this study validates reproducible criteria applicable to all levels of expertise to identify NIDCM patients at high risk of future cardiovascular events.
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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 | 0.008 |
| 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) | 0.000 | 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".