AUTHOR=Hakiki Bahia , Donnini Ida , Romoli Anna Maria , Draghi Francesca , Maccanti Daniela , Grippo Antonello , Scarpino Maenia , Maiorelli Antonio , Sterpu Raisa , Atzori Tiziana , Mannini Andrea , Campagnini Silvia , Bagnoli Silvia , Ingannato Assunta , Nacmias Benedetta , De Bellis Francesco , Estraneo Anna , Carli Valentina , Pasqualone Eugenia , Comanducci Angela , Navarro Jorghe , Carrozza Maria Chiara , Macchi Claudio , Cecchi Francesca 

TITLE=Clinical, Neurophysiological, and Genetic Predictors of Recovery in Patients With Severe Acquired Brain Injuries (PRABI): A Study Protocol for a Longitudinal Observational Study

JOURNAL=Frontiers in Neurology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.711312

DOI=10.3389/fneur.2022.711312

ISSN=1664-2295

ABSTRACT=Background: Due to continuous advances in intensive care technology and neurosurgical procedures, the number of survivors from severe acquired brain injuries (sABIs) has increased considerably weighing on the health national systems and rising a number of delicate ethical issues. The heterogeneity and the complex nature of the neurological damage below sABIs make it challenging to detect predictive factors of a better outcome. Identifying the profile of those patients who are more likely to have a good recovery will facilitate clinicians’ and parents’ decisions and allow to personalize and optimize rehabilitation strategies. This paper describes a multicenter study protocol to prospectively investigate outcomes and predictors of a large Italian cohort of sABIs survivors undergoing post-acute inpatient rehabilitation. 
Methods: All patients with diagnosis of sABI admitted to four intensive rehabilitation units within four months from the acute event, aged 18+, and providing informed consent will be enrolled. Measures will be taken at admission (T0), at three (T1) and six months (T2) from T0 and at follow-up at 12 and 24 months from onset, including clinical and functional data, neurophysiological results and analysis of neurogenetic biomarkers. 
Statistics: Advanced machine learning algorithms will be cross-validated to achieve data-driven prognosis prediction models. To assess the clinical applicability of the solutions obtained, the prediction of recovery milestones will be compared to the evaluation of a multi-disciplinary team.
Discussion: Identifying the profiles of patients with a favorable prognosis would allow a customization of rehabilitation strategies to optimize rehabilitation outcomes and enable accurate information to be provided to caregivers.
Clinical Trial Registration: The study was registered on ClinicalTrials.gov with the following registration number: NCT04495192.