Predictors of Function, Activity, and Participation of Stroke Patients Undergoing Intensive Rehabilitation: A Multicenter Prospective Observational Study Protocol - 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; et al. (Frontiers in Neurology, 2021)
Background: The complex nature of stroke sequelae, the heterogeneity in rehabilitation path-ways, and the lack of validated prediction models of rehabilitation outcomes, challenge stroke re-habilitation quality assessment and clinical research. An integrated care pathway (ICP), defining a reproducible rehabilitation assessment and process, is necessary to investigate outcomes and predictors of response to treatment, including neurophysiological and neurogenetic biomarkers. Predictors may differ for different interventions, suggesting clues to personalize and optimize re-habilitation. 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 treatment are largely unexplored. This paper describes a multicenter study protocol to prospectively investi-gate outcomes and predictors of response to an evidence based ICP, in a large Italian cohort of stroke survivors undergoing post-acute inpatient rehabilitation. Methods: All ischemic or haemorrhagic stroke patients admitted to four Intensive Rehabilitation Units within 30 days from the acute event, aged 18+, providing informed consent, will be enrolled (expected sample: 270 patients). Measures will be taken at admission (T0), discharge (T1), fol-low-up, and six months after stroke (T2), including clinical data, nutritional, functional, neurolog-ical and neuropsychological measures, Electroencephalography and Motor evoked potentials, and analysis of the neurogenetic biomarkers. Statistics: In addition to the classical multivariate lo-gistic regression analysis, advanced machine learning algorithms will be cross-validated to achieve data-driven prognosis prediction models. Discussion: By identifying data-driven prognosis prediction models in stroke rehabilitation, this study might contribute to develop patient-oriented therapy and optimize rehabilitation outcomes.