Article

OBJECTIVE: To develop mathematical models for predicting level of independence
with specific functional outcomes 1 year after discharge from inpatient
rehabilitation for spinal cord injury. DESIGN: Statistical analyses using
artificial neural networks and logistic regression.
SETTING: Retrospective
analysis of data from the national, multicenter Spinal Cord Injury Model Systems
(SCIMS) Database. PARTICIPANTS: Subjects
(N=3142; mean age, 41.5y)
with traumatic
spinal cord injury who contributed data for the National SCIMS Database
longitudinal outcomes studies. INTERVENTIONS: Not applicable. MAIN OUTCOME
MEASURES: Self-reported ambulation ability and FIM-derived indices of level of
assistance required for self-care activities (ie, bed-chair transfers, bladder
and bowel management, eating, toileting). RESULTS: Models for predicting
ambulation status were highly accurate (>85% case classification accuracy; areas
under the receiver operating characteristic curve between .86 and .90). Models
for predicting nonambulation outcomes were moderately accurate (76%-86% case
classification accuracy; areas under the receiver operating characteristic curve
between .70 and .82). The performance of models generated by artificial neural
networks closely paralleled the performance of models analyzed using logistic
regression constrained by the same independent variables. CONCLUSIONS: After
further prospective validation, such predictive models may allow clinicians to
use data available at the time of admission to inpatient spinal cord injury
rehabilitation to accurately predict longer-term ambulation status, and whether
individual patients are likely to perform various self-care activities with or
without assistance from another person.
CI - Copyright (c) 2016 American Congress of Rehabilitation Medicine. Published by
Elsevier Inc. All rights reserved.

Langue : ANGLAIS