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Measurements of Motor Function and Other Clinical Outcome Parameters in Ambulant Children with Duchenne Muscular Dystrophy
Published on: January 12, 2019
Creation of a novel algorithm to identify patients with Becker and Duchenne muscular dystrophy within an
Jonathan H Soslow1, Matthew Hall2, W Bryan Burnette3
11Thomas P Graham Division of Pediatric Cardiology,Department of Pediatrics,Vanderbilt University Medical Center,Nashville,TN,USA.
Insights
A new algorithm accurately identifies patients with Becker and Duchenne muscular dystrophy in administrative data, improving cardiovascular disease analysis and patient outcomes.
Area of Science:
- Medical informatics
- Rare disease research
- Cardiovascular medicine
Background:
- Administrative databases are valuable for rare disease research like Becker and Duchenne muscular dystrophy.
- Lack of specific ICD codes hinders patient identification in these databases.
- A novel algorithm was developed to improve patient identification and cardiovascular morbidity assessment.
Purpose of the Study:
- To develop and validate an algorithm for accurately identifying patients with Becker and Duchenne muscular dystrophy in administrative data.
- To assess the impact of cardiovascular disease on hospitalization outcomes in these patients.
Main Methods:
- Utilized hospital discharge data (n=13,189) for muscular dystrophy patients (ICD-9 code: 359.1).
- Developed and validated an identification algorithm across three institutions.
- Employed multivariable generalized linear mixed-effects models to analyze factors influencing hospital stay, cost, and readmission.
Main Results:
- The algorithm improved patient identification from 55% to 77%.
- Left ventricular dysfunction and arrhythmia were linked to increased hospitalization cost, length of stay, and mortality.
- Adjusted analyses showed significantly increased length of stay and hospitalization costs for patients with cardiovascular conditions.
Conclusions:
- The developed algorithm reliably identifies Becker and Duchenne muscular dystrophy patients for administrative data analysis.
- Cardiovascular disease significantly impacts hospitalization length and cost in this population.
- Earlier recognition and management of cardiovascular disease are crucial for improving patient outcomes.
Background:
Outcome analyses in large administrative databases are ideal for rare diseases such as Becker and Duchenne muscular dystrophy. Unfortunately, Becker and Duchenne do not yet have specific International Classification of Disease-9/-10 codes. We hypothesised that an algorithm could accurately identify these patients within administrative data and improve assessment of cardiovascular morbidity.
Methods:
Hospital discharges (n=13,189) for patients with muscular dystrophy classified by International Classification of Disease-9 code: 359.1 were identified from the Pediatric Health Information System database. An identification algorithm was created and then validated at three institutions. Multi-variable generalised linear mixed-effects models were used to estimate the associations of length of stay, hospitalisation cost, and 14-day readmission with age, encounter severity, and respiratory disease accounting for clustering within the hospital.
Results:
The identification algorithm improved identification of patients with Becker and Duchenne from 55% (code 359.1 alone) to 77%. On bi-variate analysis, left ventricular dysfunction and arrhythmia were associated with increased cost of hospitalisation, length of stay, and mortality (p<0.001). After adjustment, Becker and Duchenne patients with left ventricular dysfunction and arrhythmia had increased length of stay with rate ratio 1.4 and 1.2 (p<0.001 and p=0.004) and increased cost of hospitalization with rate ratio 1.4 and 1.4 (both p<0.001).
Conclusions:
Our algorithm accurately identifies patients with Becker and Duchenne and can be used for future analysis of administrative data. Our analysis demonstrates the significant effects of cardiovascular disease on length of stay and hospitalisation cost in patients with Becker and Duchenne. Better recognition of the contribution of cardiovascular disease during hospitalisation with earlier more intensive evaluation and therapy may help improve outcomes in this patient population.
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