Individualized Prognostic Prediction of the Long-Term Functional Trajectory in Pediatric Acquired Brain Injury.

Functional Independence Measure for children (WeeFIM) acquired brain injury (ABI) latent class analysis mixed models single-subject recovery prediction structural equation modelling (SEM) trajectory prediction

Journal

Journal of personalized medicine
ISSN: 2075-4426
Titre abrégé: J Pers Med
Pays: Switzerland
ID NLM: 101602269

Informations de publication

Date de publication:
18 Jul 2021
Historique:
received: 08 06 2021
revised: 09 07 2021
accepted: 15 07 2021
entrez: 6 8 2021
pubmed: 7 8 2021
medline: 7 8 2021
Statut: epublish

Résumé

In pediatric acquired brain injury, heterogeneity of functional response to specific rehabilitation treatments is a key confound to medical decisions and outcome prediction. We aimed to identify patient subgroups sharing comparable trajectories, and to implement a method for the early prediction of the long-term recovery course from clinical condition at first discharge. 600 consecutive patients with acquired brain injury (7.4 years ± 5.2; 367 males; median GCS = 6) entered a standardized rehabilitation program. Functional Independent Measure scores were measured yearly, until year 7. We classified the functional trajectories in clusters, through a latent class model. We performed single-subject prediction of trajectory membership in cases unseen during model fitting. Four trajectory types were identified (post.prob. > 0.95): high-start fast (

Identifiants

pubmed: 34357142
pii: jpm11070675
doi: 10.3390/jpm11070675
pmc: PMC8305391
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Medical Research Council
ID : Skills Development Fellowship Scheme
Pays : United Kingdom

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Auteurs

Erika Molteni (E)

School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK.

Marta Bianca Maria Ranzini (MBM)

School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK.

Elena Beretta (E)

Acquired Brain Injury Unit, Scientific Institute IRCCS E. Medea, 22040 Bosisio Parini, Italy.

Marc Modat (M)

School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK.

Sandra Strazzer (S)

Acquired Brain Injury Unit, Scientific Institute IRCCS E. Medea, 22040 Bosisio Parini, Italy.

Classifications MeSH