Article

A statistical orthosis selection model was developed using the Random Forest
Algorithm (RFA). The model's performance and potential clinical benefit was
evaluated. The model predicts which of five orthosis designs - solid (SAFO),
posterior leaf spring (PLS), hinged (HAFO), supra-malleolar (SMO), or foot
orthosis (FO) - will provide the best gait outcome for individuals with diplegic
cerebral palsy (CP). Gait outcome was defined as the change in Gait Deviation
Index (GDI) between walking while wearing an orthosis compared to barefoot
(DeltaGDI=GDIOrthosis-GDIBarefoot). Model development was carried out using
retrospective data from 476 individuals who wore one of the five orthosis designs
bilaterally. Clinical benefit was estimated by predicting the optimal orthosis
and DeltaGDI for 1016 individuals (age: 12.6 (6.7) years), 540 of whom did not
have an existing orthosis prescription. Among limbs with an orthosis, the model
agreed with the prescription only 14% of the time. For 56% of limbs without an
orthosis, the model agreed that no orthosis was expected to provide benefit.
Using the current standard of care orthosis (i.e. existing orthosis
prescriptions), DeltaGDI is only +0.4 points on average. Using the orthosis
prediction model, average DeltaGDI for orthosis users was estimated to improve to
+5.6 points. The results of this study suggest that an orthosis selection model
derived from the RFA can significantly improve outcomes from orthosis use for the
diplegic CP population. Further validation of the model is warranted using data
from other centers and a prospective study.
CI - Copyright (c) 2014 Elsevier B.V. All rights reserved.

Langue : ANGLAIS