Predictors of Disease Progression in Patients with Left Ventricular Nondilated Cardiomyopathy
Bibliographic record
Abstract
BACKGROUND: Nondilated left ventricular cardiomyopathy (NDLVC) is a highly heterogeneous and unexplored category of cardiomyopathies introduced in the 2023 European Society of Cardiology (ESC) guidelines. This study aims to identify factors associated with adverse remodeling in a large multicentre cohort of patients with NDLVC. METHODS: A total of 432 patients with NDLVC (63.4% male, mean age 39.4 ± 14.6 years) with multiparametric characterization were enrolled from January 2010 to May 2023 in 2 high-volume Italian centres. Diagnosis followed ESC criteria. Endpoints were worsening of left ventricular ejection fraction (LVEF) ≥ 10% from the baseline value (or group W), evolution to dilated cardiomyopathy (DCM) (or group E), and the concomitant occurrence of both (or group W + E), assessed at the latest available echocardiographic evaluation. RESULTS: During a median follow-up of 77 months (interquartile range [IQR]: 48-109), 27.3% of patients experienced worsening LVEF, 28.9% progressed to DCM, and 18.3% reached the combined W + E endpoint. A multivariable model including positive genetic testing associated to myocardial inflammation, family history of cardiomyopathies or sudden cardiac death, intraventricular conduction delay, baseline echocardiographic LVEF < 45%, a ring-like late gadolinium enhancement on cardiac magnetic resonance, and nonsustained ventricular tachycardia at baseline had the strongest discrimination power for predicting worsening LVEF (area under the curve [AUC] of 0.8; 95% confidence interval [CI], 0.75-0.86), evolution to DCM (AUC 0.78; 95% CI, 0.73-0.84) and the combined W + E (AUC 0.84; 95% CI, 0.79-0.89). Negative genetic testing reduced the risk across all the endpoints (odds ratio [OR], 0.2; 95% CI, 0.1-0.4; P < 0.001; OR, 0.2; 95% CI, 0.1-0.4; P < 0.001 and OR, 0.1; 95% CI, 0.04-0.3; P < 0.001, respectively). CONCLUSIONS: In this large NDLVC cohort, in which 38% of patients showed an adverse remodelling over long-term follow-up, a multiparametric approach integrating deep phenotyping and genetics effectively identified high-risk patients.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".