Article

Several novel drug- and cell-based potential therapies for spinal
cord injury (SCI) have either been applied or will be considered for future
clinical trials. Limitations on the number of eligible patients require trials be
undertaken in a highly efficient and effective manner. However, this is
particularly challenging when people living with incomplete SCI (iSCI) represent
a very heterogeneous population in terms of recovery patterns and can improve
spontaneously over the first year after injury. OBJECTIVE:
The current study
addresses 2 requirements for designing SCI trials: first, enrollment of as many
eligible participants as possible; second, refined stratification of participants
into homogeneous cohorts from a heterogeneous iSCI population. METHODS: This is a
retrospective, longitudinal analysis of prospectively collected SCI data from the
European Multicenter study about Spinal Cord Injury (EMSCI). We applied
conditional inference trees to provide a prediction-based stratification
algorithm that could be used to generate decision rules for the appropriate
inclusion of iSCI participants to a trial. RESULTS: Based on baseline clinical
assessments and a defined subsequent clinical endpoint, conditional inference
trees partitioned iSCI participants into more homogeneous groups with regard to
the illustrative endpoint, upper extremity motor score. Assuming a continuous
endpoint, the conditional inference tree was validated both internally as well as
externally, providing stable and generalizable results. CONCLUSION: The
application of conditional inference trees is feasible for iSCI participants and
provides easily implementable, prediction-based decision rules for inclusion and
stratification. This algorithm could be utilized to model various trial endpoints
and outcome thresholds.
CI - (c) The Author(s) 2015.

Langue : ANGLAIS