AUTHOR=Hakiki Bahia , Paperini Anita , Castagnoli Chiara , Hochleitner Ines , Verdesca Sonia , Grippo Antonello , Scarpino Maenia , Maiorelli Antonio , Mosca Irene Eleonora , Gemignani Paola , Borsotti Marco , Gabrielli Maria Assunta , Salvadori Emilia , Poggesi Anna , Lucidi Giulia , Falsini Catiuscia , Gentilini Monica , Martini Monica , Luisi Maria Luisa Eliana , Biffi Barbara , Mainardi Paolo , Barretta Teresa , Pancani Silvia , Mannini Andrea , Campagnini Silvia , Bagnoli Silvia , Ingannato Assunta , Nacmias Benedetta , Macchi Claudio , Carrozza Maria Chiara , Cecchi Francesca 

TITLE=Predictors of Function, Activity, and Participation of Stroke Patients Undergoing Intensive Rehabilitation: A Multicenter Prospective Observational Study Protocol

JOURNAL=Frontiers in Neurology

VOLUME=12

YEAR=2021

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2021.632672

DOI=10.3389/fneur.2021.632672

ISSN=1664-2295

ABSTRACT=<p><bold>Background:</bold> The complex nature of stroke sequelae, the heterogeneity in rehabilitation pathways, and the lack of validated prediction models of rehabilitation outcomes challenge stroke rehabilitation quality assessment and clinical research. An integrated care pathway (ICP), defining a reproducible rehabilitation assessment and process, may provide a structured frame within investigated outcomes and individual predictors of response to treatment, including neurophysiological and neurogenetic biomarkers. Predictors may differ for different interventions, suggesting clues to personalize and optimize rehabilitation. To date, a large representative Italian cohort study focusing on individual variability of response to an evidence-based ICP is lacking, and predictors of individual response to rehabilitation are largely unexplored. This paper describes a multicenter study protocol to prospectively investigate outcomes and predictors of response to an evidence-based ICP in a large Italian cohort of stroke survivors undergoing post-acute inpatient rehabilitation.</p><p><bold>Methods:</bold> All patients with diagnosis of ischemic or hemorrhagic stroke confirmed both by clinical and brain imaging evaluation, admitted to four intensive rehabilitation units (adopting the same stroke rehabilitation ICP) within 30 days from the acute event, aged 18+, and providing informed consent will be enrolled (expected sample: 270 patients). Measures will be taken at admission (T0), at discharge (T1), and at follow-up 6 months after a stroke (T2), including clinical data, nutritional, functional, neurological, and neuropsychological measures, electroencephalography and motor evoked potentials, and analysis of neurogenetic biomarkers.</p><p><bold>Statistics:</bold> In addition to classical multivariate logistic regression analysis, advanced machine learning algorithms will be cross-validated to achieve data-driven prognosis prediction models.</p><p><bold>Discussion:</bold> By identifying data-driven prognosis prediction models in stroke rehabilitation, this study might contribute to the development of patient-oriented therapy and to optimize rehabilitation outcomes.</p><p><bold>Clinical Trial Registration:</bold><ext-link ext-link-type="uri" xlink:href="http://ClinicalTrials.gov" xmlns:xlink="http://www.w3.org/1999/xlink">ClinicalTrials.gov</ext-link>, NCT03968627. <ext-link ext-link-type="uri" xlink:href="https://www.clinicaltrials.gov/ct2/show/NCT03968627?term=Cecchi&amp;cond=Stroke&amp;draw=2&amp;rank=2" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.clinicaltrials.gov/ct2/show/NCT03968627?term=Cecchi&amp;cond=Stroke&amp;draw=2&amp;rank=2</ext-link>.</p>