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Predictive value of upper limb muscles and grasp patterns on functional outcome in cervical spinal cord injury


Velstra, Inge-Marie; Bolliger, Marc; Krebs, Jörg; Rietman, Johan S; Curt, Armin (2016). Predictive value of upper limb muscles and grasp patterns on functional outcome in cervical spinal cord injury. Neurorehabilitation and Neural Repair, 30(4):295-306.

Abstract

OBJECTIVE: To determine which single or combined upper limb muscles as defined by the International Standards for the Neurological Classification of Spinal Cord Injury (ISNCSCI); upper extremity motor score (UEMS) and the Graded Redefined Assessment of Strength, Sensibility, and Prehension (GRASSP), best predict upper limb function and independence in activities of daily living (ADLs) and to assess the predictive value of qualitative grasp movements (QlG) on upper limb function in individuals with acute tetraplegia.
METHOD: As part of a Europe-wide, prospective, longitudinal, multicenter study ISNCSCI, GRASSP, and Spinal Cord Independence Measure (SCIM III) scores were recorded at 1 and 6 months after SCI. For prediction of upper limb function and ADLs, a logistic regression model and unbiased recursive partitioning conditional inference tree (URP-CTREE) were used.
RESULTS: Logistic regression and URP-CTREE revealed that a combination of ISNCSCI and GRASSP muscles (to a maximum of 4) demonstrated the best prediction (specificity and sensitivity ranged from 81.8% to 96.0%) of upper limb function and identified homogenous outcome cohorts at 6 months. The URP-CTREE model with the QlG predictors for upper limb function showed similar results.
CONCLUSION: Prediction of upper limb function can be achieved through a combination of defined, specific upper limb muscles assessed in the ISNCSCI and GRASSP. A combination of a limited number of proximal and distal muscles along with an assessment of grasping movements can be applied for clinical decision making for rehabilitation interventions and clinical trials.

Abstract

OBJECTIVE: To determine which single or combined upper limb muscles as defined by the International Standards for the Neurological Classification of Spinal Cord Injury (ISNCSCI); upper extremity motor score (UEMS) and the Graded Redefined Assessment of Strength, Sensibility, and Prehension (GRASSP), best predict upper limb function and independence in activities of daily living (ADLs) and to assess the predictive value of qualitative grasp movements (QlG) on upper limb function in individuals with acute tetraplegia.
METHOD: As part of a Europe-wide, prospective, longitudinal, multicenter study ISNCSCI, GRASSP, and Spinal Cord Independence Measure (SCIM III) scores were recorded at 1 and 6 months after SCI. For prediction of upper limb function and ADLs, a logistic regression model and unbiased recursive partitioning conditional inference tree (URP-CTREE) were used.
RESULTS: Logistic regression and URP-CTREE revealed that a combination of ISNCSCI and GRASSP muscles (to a maximum of 4) demonstrated the best prediction (specificity and sensitivity ranged from 81.8% to 96.0%) of upper limb function and identified homogenous outcome cohorts at 6 months. The URP-CTREE model with the QlG predictors for upper limb function showed similar results.
CONCLUSION: Prediction of upper limb function can be achieved through a combination of defined, specific upper limb muscles assessed in the ISNCSCI and GRASSP. A combination of a limited number of proximal and distal muscles along with an assessment of grasping movements can be applied for clinical decision making for rehabilitation interventions and clinical trials.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Balgrist University Hospital, Swiss Spinal Cord Injury Center
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2016
Deposited On:25 Nov 2015 10:05
Last Modified:15 Apr 2016 01:01
Publisher:Sage Publications Ltd.
ISSN:1545-9683
Publisher DOI:https://doi.org/10.1177/1545968315593806
PubMed ID:26156192

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