Creation of a novel algorithm to identify patients with Becker and Duchenne muscular dystrophy within an administrative database and application of the algorithm to assess cardiovascular morbidity. (26th January 2019)

• Record Type: Journal Article
• Authors: Soslow, Jonathan H; Hall, Matthew; Burnette, W Bryan; Hor, Kan; Chisolm, Joanne; Spurney, Christopher; Godown, Justin; Xu, Meng; Slaughter, James C; Markham, Larry W
• Is Part Of: Cardiology in the young. Volume 29:Number 3(2019)
• Page Start: 290
• Page End: 296
• Publication Date: 2019-01-26
• Subjects: Duchenne muscular dystrophy, -- Becker muscular dystrophy, -- Pediatric Health Information System, -- left ventricular dysfunction, -- arrhythmia, -- cardiomyopathy; Pediatric cardiology -- Periodicals; 618.9212
• DOI: 10.1017/S1047951118002226
• Languages: English
• ISSNs: 1047-9511

Abstract

Abstract: 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.