Cardiac Intervention Improves Heart Disease and Clinical Outcomes in Patients With Muscular Dystrophy in a Multidisciplinary Care Setting
Bibliographic record
Abstract
Background Patients with muscular dystrophy (MD) represent a vulnerable patient population with no clearly defined care model in modern-day clinical practice to manage a high burden of heart disease and comorbidities. We demonstrate the effectiveness of cardiac interventions, namely the initiation and optimization of medical and device therapies, as part of a multidisciplinary care approach to improve clinical outcomes in patients with MD. Methods and Results We conducted a prospective cohort study at the Neuromuscular Multidisciplinary clinic following patients with dystrophinopathies, limb-girdle MD, type 1 myotonic dystrophy, and facioscapulohumeral MD. A negative control group classified as non-MD myopathies without heart disease, was also tracked. Our cohort of 185 patients (median age: 42 years; 79 [42.7%] women), included 145 patients with MD. Cardiomyopathy was present in 65.6% of the patients with dystrophinopathies (21 of 32) and 27.3% of the patients with limb-girdle MD (9 of 33). Conduction abnormalities were common in type 1 myotonic dystrophy (33.3% [20/60] patients). Cardiac intervention reversed systolic dysfunction, with left ventricular ejection fraction improving from 43% to 50.0% over a 3-year period. A sustained reduction in healthcare utilization was also observed. The number of outpatient clinic visits decreased from 3.0 to 1.5 visits per year, the duration of hospitalizations was reduced from 14.2 to 0.9 days per year, and the number of cardiac-related hospitalizations decreased from 0.4 to 0.1 hospitalizations per year associated with low mortality. Conclusions Our study demonstrates that cardiac intervention as part of a comprehensive multidisciplinary care approach to treating patients with MD leads to a sustained improvement in clinical outcomes.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".