Evaluating the diagnostic and prognostic value of biomarkers for heart disease and major adverse cardiac events in patients with muscular dystrophy
Bibliographic record
Abstract
AIMS: Heart disease is recognized as the leading cause of morbidity and mortality in patients with muscular dystrophy (MD). Our study demonstrates the clinical utility of cardiac biomarkers to improve the diagnosis of cardiomyopathy and prognostication of major adverse cardiac events (MACE) in these vulnerable patients. METHODS AND RESULTS: We prospectively followed 117 patients [median age, 42 [interquartile range (IQR), 26-50) years; 49 (41.9%) women] at the Neuromuscular Multidisciplinary clinic diagnosed with a dystrophinopathy, limb-girdle MD, type 1 myotonic dystrophy, or facioscapulohumeral MD. We determined that B-type natriuretic peptide (BNP) and high-sensitive troponin I (hsTnI) were effective diagnostic markers of cardiomyopathy [area under the curve (AUC), 0.64; P = 0.017; and AUC, 0.69; P = 0.001, respectively]. Patient risk stratification for MACE was based on cut-off values of BNP and hsTnI defined a priori as 30.5000 pg/mL and 7.6050 ng/L, respectively. Over a median follow-up period of 2.09 (IQR, 1.17-2.81) years there were 36 confirmed MACE. Multivariate regression analyses showed that patients with BNP and hsTnI levels above the respective cut-off values had a 3.70-fold (P = 0.001) and 3.24-fold (P = 0.002) greater risk of MACE, respectively, compared with patients with biomarker levels below. Furthermore, patients with biomarker levels above both cut-off values had a 4.08-fold (P = 0.001) greater risk of MACE. Inflammatory biomarkers did not show clinical utility for heart disease in these patients. CONCLUSION: Our study demonstrates important diagnostic and prognostic value of BNP and hsTnI as part of a comprehensive cardiac assessment to augment the management and treatment of heart disease in patients with MD.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".