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Stratifying stroke patients in rehabilitation and recovery trials

Key questions in stroke rehabilitation research are who responds to rehabilitation interventions, who doesn’t and why? 

Aims

The outcome of this project will be development of a clinical stratification tool for use in future clinical trials.

The heterogeneity of the stroke population is well known. Most commonly, rehabilitation researchers approach this problem by applying highly selective entry criteria for trials. These criteria are largely based on clinical judgment. This approach, while reasonable, fails to advance our understanding. If the characteristics of individual responders and non-responders were identified, this would fundamentally change the focus of rehabilitation research and practice. The absence of large, prospective, long term studies of stroke patients undergoing rehabilitation in which patients and interventions are well characterised, has hampered efforts to address this important question. We have access to a potential pool of over 2000 stroke patients whose characteristics and interventions are well characterised.

Using pooled data from the control arms of trials or from observational studies where baseline assessment occurred within 72 hours of stroke onset, we will determine the characteristics of patients exhibiting different recovery profiles over the course of the first year post stroke. These data represent the natural history of recovery. Using classification clustering, we will determine sub- groups of patients who exhibit distinct profiles. Using data from the treatment arms of trials we’ll conduct exploratory decision analysis to interrogate whether patient subgroups (eg, no, slow, fast recovery) respond to treatment.

 

Students will work within the Centre for Research Excellence in Stroke, based at the Florey Institute in Heidelberg.

Key references: Bernhardt, J., H. Dewey, et al. (2006). "A Very Early Rehabilitation Trial (AVERT)." International Journal of Stroke 1: 169-171. Ceglowski, R., L. Churilov, et al. (2006). "Combining Data Mining and Discrete Event Simulation for a value-added view of a hospital emergency department." J Oper Res Soc 58(2): 246-254.

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