Background: The Disease Severity Scoring System (DS3) is a validated measure for evaluating Gaucher disease type 1 (GD1) severity. We developed a new framework, consisting of health states, transition probabilities between those states, and preferences for those states (utilities) based on the DS3 to predict long-term outcomes of patients starting treatment. We defined nine mutually exclusive (alive) health states based on three DS3 categories: mild (0 ≤ DS3 ≤ 3.5) without symptoms of bone disease; mild with bone pain, mild with severe skeletal complications (SSC) defined as lytic lesions, avascular necrosis, or fracture; moderate (3.5 < DS3 ≤ 6.5) without SSC; moderate with SSC; marked (6.5 < DS3 ≤ 9.5) without SSC; marked with SSC; severe (9.5 < DS3 ≤ 19) without SSC; and severe with SSC. Health-state transition probabilities and utilities were estimated from a longitudinal sample of patients with GD1 who started enzyme replacement therapy (the DS3 Score Study). Age dependent GD1-specific mortality was derived from published data. We used a Markov state-transition model to illustrate how to estimate time spent in each health state.

Results: The average predicted utilities for each health state ranged from 0.76 for mild disease with no clinical symptoms of bone disease to 0.52 with severe disease with SSC. Transition probabilities depended on disease severity (DS3 score) at treatment initiation and whether patients had undergone a total splenectomy or had an intact spleen/partial splenectomy prior to starting treatment. Patients who started treatment with intact or residual spleens spent more time in better health states than those who started treatment with total splenectomy.

Conclusions: This new framework, which is based on the DS3, can be used to project the long-term outcomes of GD1 patients starting treatment. The framework could also be used to compare the long-term outcomes of different GD1 treatment options.

Trial Registration: NCT01136304 . Registered: May 31, 2010 (retrospectively registered).

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319149PMC
http://dx.doi.org/10.1186/s13023-017-0592-6DOI Listing

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