Healthcare Service Use Patterns Among Patients with Acid Sphingomyelinase Deficiency Type B: A Retrospective US Claims Analysis.

INTRODUCTION

Acid sphingomyelinase deficiency (ASMD) is a rare lysosomal storage disease. Patients with ASMD type B experience multiple morbidities, potentially leading to early mortality. Before the 2022 approval of olipudase alfa for non-neuronopathic ASMD manifestations, only symptom management was offered. Data on healthcare services used by patients with ASMD type B are limited. This analysis used medical claims data to evaluate real-world healthcare service use by patients with ASMD type B in the United States of America (USA).

METHODS

The IQVIA Open Claims patient-level database (2010-2019) was cross-examined. Two patient cohorts were identified: the primary analysis cohort, which included patients with at least two claims associated with ASMD type B (ICD-10 code E75.241) and more total claims with ASMD type B than any other ASMD types, and the sensitivity analysis cohort, which included patients with a high probability of having ASMD type B identified using a validated machine-learning algorithm. Claims for ASMD-associated healthcare services were recorded, including outpatient visits, emergency department (ED) visits, and inpatient hospitalizations.

RESULTS

The primary analysis cohort included 47 patients; a further 59 patients made up the sensitivity analysis cohort. Patient characteristics and healthcare service use were similar in both cohorts and were consistent with established characteristics of ASMD type B. Overall, 70% of the primary analysis cohort from this study were aged < 18 years, and the liver, spleen, and lungs were the most frequently affected organs. Cognitive, developmental, and/or emotional problems and respiratory/lung disorders caused most outpatient visits; respiratory/lung disorders accounted for most ED visits and hospitalizations.

CONCLUSION

This retrospective analysis of medical claims data identified patients with ASMD type B who had characteristics typical of this condition. A machine-learning algorithm detected further cases with a high probability of having ASMD type B. High use of ASMD-related healthcare services and medications was observed in both cohorts.