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Identifying Homogeneous Subgroups in Neurological Disorders : Unbiased Recursive Partitioning in Cervical Complete Spinal Cord Injury

The reliable stratification of homogeneous subgroups and the
prediction of future clinical outcomes within heterogeneous neurological
disorders is a particularly challenging task. Nonetheless, it is essential for
the implementation of targeted care and effective therapeutic interventions.
This study was designed to assess the value of a recently developed
regression tool from the family of unbiased recursive partitioning methods in
comparison to established statistical approaches
(eg, linear and logistic
regression) for predicting clinical endpoints and for prospective patients'
stratification for clinical trials. Methods.
A retrospective, longitudinal
analysis of prospectively collected neurological data from the European
Multicenter study about Spinal Cord Injury (EMSCI) network was undertaken on
C4-C6 cervical sensorimotor complete subjects. Predictors were based on a broad
set of early (<2 weeks) clinical assessments. Endpoints were based on later
clinical examinations of upper extremity motor scores and recovery of motor
levels, at 6 and 12 months, respectively. Prediction accuracy for each
statistical analysis was quantified by resampling techniques. Results. For all
settings, overlapping confidence intervals indicated similar prediction accuracy
of unbiased recursive partitioning to established statistical approaches. In
addition, unbiased recursive partitioning provided a direct way of identification
of more homogeneous subgroups. The partitioning is carried out in a data-driven
manner, independently from a priori decisions or predefined thresholds.
Conclusion. Unbiased recursive partitioning techniques may improve prediction of
future clinical endpoints and the planning of future SCI clinical trials by providing easily implementable, data-driven rationales for early patient
stratification based on simple decision rules and clinical read-outs.
CI - (c) The Author(s) 2014.

Langue : ANGLAIS

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