Prevalence, Clinical Manifestations, and Adverse Outcomes of Left Ventricular Noncompaction in Adults: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Left ventricular noncompaction (LVNC) is recognized within the spectrum of adult cardiomyopathies for its unique pathophysiologic features and clinical challenges. This condition exhibits a wide range of clinical manifestations, from asymptomatic states to severe cardiovascular complications, making its diagnosis and management challenging. This study aimed to synthesize current data on the prevalence, diagnostic methods, clinical outcomes, and treatment efficacy of LVNC in adults to address gaps in understanding and management strategies. Methods: A systematic review and meta-analysis of research from 2000 to March 2024 was conducted, focusing on studies involving adults diagnosed with LVNC. This approach aimed to collect data on the prevalence of LVNC, the diagnostic accuracy of different imaging modalities, clinical manifestations, and the impact of different treatment strategies. Results: The study showed a prevalence of LVNC of 0.5%, with cardiovascular magnetic resonance outperforming echocardiography in diagnosis with a detection rate of 1.3%. Mortality and heart transplantation rates were 12% and 7%, respectively. Significant predictors of adverse outcomes included New York Heart Association (NYHA) class III or IV, ventricular tachycardia, and reduced left ventricular ejection fraction (LVEF), guiding a nuanced approach in tailoring therapeutic strategies to optimize patient care and outcomes. Conclusions: This study advances the understanding of LVNC by refining diagnostic criteria and evaluating management strategies, highlighting the superiority of cardiovascular magnetic resonance. It identifies predictors of adverse outcomes and assesses treatment efficacy, urging precision in diagnosis and tailored treatments. Its comprehensive analysis and methodological rigor make it a key resource advocating a multidisciplinary approach to improve patient outcomes in LVNC.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.025 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".