Abstract
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.
Keywords
List of abbreviations:
AIS (American Spinal Injury Association Impairment Scale), ANN (artificial neural network), ASIA (American Spinal Injury Association), AUC (area under the curve), NLR (negative likelihood ratio), PLR (positive likelihood ratio), SCI (spinal cord injury), SCIMS (Spinal Cord Injury Model Systems)To read this article in full you will need to make a payment
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Article Info
Publication History
Published online: May 18, 2016
Footnotes
Supported by the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR) (grant no. H133N10019). NIDILRR is a Center within the Administration for Community Living (ACL), Department of Health and Human Services (HHS). The contents of this article do not necessarily represent the policy of NIDILRR, ACL, HHS, and you should not assume endorsement by the Federal Government.
Disclosures: none.
Identification
Copyright
© 2016 by the American Congress of Rehabilitation Medicine
