Prediction and Stratification of Upper Limb Function and Self-Care in Acute Cervical Spinal Cord Injury With the Graded Redefined Assessment of Strength, Sensibility, and Prehension (GRASSP). (September 2014)

• Record Type: Journal Article
• Authors: Velstra, Inge-Marie; Bolliger, Marc; Tanadini, Lorenzo Giuseppe; Baumberger, Michael; Abel, Rainer; Rietman, Johan S.; Curt, Armin
• Is Part Of: Neurorehabilitation & neural repair. Volume 28:Number 7(2014)
• Page Start: 632
• Page End: 642
• Publication Date: 2014-09
• Subjects: spinal cord injury -- tetraplegia -- prediction -- upper limb function -- activities of daily living -- outcome measures -- ISNCSCI; Nervous system -- Diseases -- Patients -- Rehabilitation -- Periodicals; Brain damage -- Patients -- Rehabilitation -- Periodicals; Spinal cord -- Wounds and injuries -- Patients -- Rehabilitation -- Periodicals; Nervous system -- Regeneration -- Periodicals; Neuroplasticity -- Periodicals; 616.804305
• DOI: 10.1177/1545968314521695
• Languages: English
• ISSNs: 1545-9683

Abstract

Background . There is inherent heterogeneity within individuals suffering from cervical spinal cord injury (SCI), and early prediction of upper limb function and self-care is challenging. As a result, considerable uncertainty exists regarding the prediction of functional outcome following cervical SCI within 1 year of injury. Objective . To evaluate the value of Graded Redefined Assessment of Strength, Sensibility, and Prehension (GRASSP) in predicting upper limb function and self-care outcomes in individuals with cervical SCI. Method . A prospective longitudinal multicenter study was performed. Data from the GRASSP, the Spinal Cord Independence Measure (SCIM III), and the American Spinal Injury Association (ASIA) Impairment Scale were recorded at 1, 6, and 12 months after cervical SCI. For prediction of functional outcome at 6 and 12 months, a logistic regression model, receiver operating characteristics (ROC), and unbiased recursive partitioning conditional inference tree (URP-CTREE) were used with 8 different predictor variables. Results . Logistic regression analysis, ROC analysis, and URP-CTREE all revealed that the strength subtest within GRASSP is the strongest predictor for upper limb function and self-care outcomes. URP-CTREE provides useful information on the distribution of different outcomes in acute cervical SCI and can be used to predict cohorts with homogeneous outcomes. Conclusion . The GRASSP at 1 month can accurately predict upper limb function and self-care outcomes even in a heterogeneous group of individuals across a wide spectrum of neurological recovery. The application of URP-CTREE can reveal the distribution of outcome categories and, based on this, inform trial protocols with respect to outcomes analysis and patient stratification.