National variation in costs and mortality for leukodystrophy patients in US children's hospitals.

ABSTRACT

BACKGROUND: Inherited leukodystrophies are progressive, debilitating neurological disorders with few treatment options and high mortality rates. Our objective was to determine national variation in the costs for leukodystrophy patients and to evaluate differences in their care. METHODS: We developed an algorithm to identify inherited leukodystrophy patients in deidentified data sets using a recursive tree model based on International Classification of Disease, 9th Edition, Clinical Modification, diagnosis and procedure charge codes. Validation of the algorithm was performed independently at two institutions, and with data from the Pediatric Health Information System (PHIS) of 43 US children's hospitals, for a 7-year period between 2004 and 2010. RESULTS: A recursive algorithm was developed and validated, based on six International Classification of Disease, 9th Edition, Clinical Modification, codes and one procedure code that had a sensitivity up to 90% (range 61-90%) and a specificity up to 99% (range 53-99%) for identifying inherited leukodystrophy patients. Inherited leukodystrophy patients comprise 0.4% of admissions to children's hospitals and 0.7% of costs. During 7 years, these patients required $411 million of hospital care, or $131,000/patient. Hospital costs for leukodystrophy patients varied at different institutions, ranging from two to 15 times more than the average pediatric patient. There was a statistically significant correlation between higher volume and increased cost efficiency. Increased mortality rates had an inverse relationship with increased patient volume that was not statistically significant. CONCLUSIONS: We developed and validated a code-based algorithm for identifying leukodystrophy patients in deidentified national datasets. Leukodystrophy patients account for $59 million of costs yearly at children's hospitals. Our data highlight potential to reduce unwarranted variability and improve patient care.