The Impact of Cerebral Amyloid Angiopathy on Functional Outcome of Patients Affected by Spontaneous Intracerebral Hemorrhage Discharged from Intensive Inpatient Rehabilitation: A Cohort Study.

cerebral amyloid angiopathy rehabilitation outcomes spontaneous intracerebral hemorrhage

Journal

Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402

Informations de publication

Date de publication:
11 Oct 2022
Historique:
received: 02 08 2022
revised: 19 09 2022
accepted: 29 09 2022
entrez: 27 10 2022
pubmed: 28 10 2022
medline: 28 10 2022
Statut: epublish

Résumé

Background: Sporadic CAA is recognized as a major cause of sICH and sABI. Even if intensive rehabilitation is recommended to maximize functional recovery after sICH, no data are available on whether CAA may affect rehabilitation outcomes. In this observational prospective study, to explore the impact of CAA on rehabilitation results, functional outcomes after intensive rehabilitation have been compared between patients affected by sICH with and without a diagnosis of CAA. Methods: All adults affected by sABI due to sICH and admitted to the IRU of IRCCS-Don-Gnocchi-Foundation were consecutively enrolled for 12 months. Demographic and clinical data were recorded upon admission and discharge. Results: Among 102 sICH patients (age: 66 (IQR = 16), 53% female), 13% were diagnosed as probable/possible-CAA. TPO and functional assessment were comparable upon admission, but CAA patients were significantly older (p = 0.001). After a comparable LOS, CAA patients presented higher care burden (ERBI: p = 0.025), poorer functional recovery (FIM: p = 0.02) and lower levels of global independence (GOSE > 4: p = 0.03). In multivariate analysis, CAA was significantly correlated with a lower FIM (p = 0.019) and a lower likelihood of reaching GOS-E > 4, (p = 0.041) at discharge, independently from age. Conclusions: CAA seems to be independently associated with poorer rehabilitation outcomes, suggesting the importance of improving knowledge about CAA to better predict rehabilitation outcomes.

Identifiants

pubmed: 36292146
pii: diagnostics12102458
doi: 10.3390/diagnostics12102458
pmc: PMC9600668
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Ministero della Salute
ID : Ricerca corrente RC2020-RC2021 Programs
Organisme : Ministero della Salute
ID : 5× mille funds AF2018: "Data Science in Rehabilitation Medicine"
Organisme : Ministero della Salute
ID : 5× mille funds AF2019: "Study and development of biomedical data science and machine learning methods to support the appropriateness and the decision-making process in rehabilitation medicine."

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Auteurs

Carmen Barbato (C)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Florence, Italy.
NEUROFARBA Department, Neuroscience Section, University of Florence, 50139 Florence, Italy.

Piergiuseppe Liuzzi (P)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Florence, Italy.
Scuola Superiore Sant'Anna, Istituto di BioRobotica, Pontedera, Viale Rinaldo Piaggio 34, 56025 Pisa, Italy.

Anna Maria Romoli (AM)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Florence, Italy.

Francesca Draghi (F)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Florence, Italy.

Daniela Maccanti (D)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Florence, Italy.

Andrea Mannini (A)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Florence, Italy.

Claudio Macchi (C)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Florence, Italy.
Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy.

Francesca Cecchi (F)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Florence, Italy.
Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy.

Bahia Hakiki (B)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Florence, Italy.

Classifications MeSH