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

Research output: Contribution to journalArticlepeer-review

Erika Molteni, Marta Ranzini, Elena Beretta, Marc Modat, Sandra Strazzer

Original languageEnglish
Article number675
Pages (from-to)1-13
Number of pages13
JournalJournal of Personalized Medicine
Volume11
Issue number7
DOIs
Published6 Aug 2021

Bibliographical note

Funding Information: Funding: This work was supported by “King’s College London (KCL)—Medical Research Council Skills Development Scheme” funds awarded to Erika Molteni, NIHR BRC based at Guy’s and St Thomas’ Trust and KCL and The Wellcome/EPSRC Centre for Medical Engineering (WT203148/Z/16/Z). Additionally, this research was funded in part by the Wellcome Trust (WT213038/Z/18/Z). For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. In addition, the study was funded by “Ricerca Corrente 2019–2020” awarded to the Scientific Institute E. Medea. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors

Abstract

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 stan-dardized 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 (N = 92), low-start fast (N = 168), slow (N = 130) and non-responders (N = 210). Fast responders were older (chigh = 1.8; clow = 1.1) than non-responders and suffered shorter coma (chigh = −14.7; clow = −4.3). High-start fast-responders had shorter length of stay (c = −1.6), and slow responders had lower incidence of epilepsy (c = −1.4), than non-responders (p < 0.001). Single-subject trajectory could be predicted with high accuracy at first discharge (accuracy = 0.80). In conclusion, we stratified patients based on the evolution of their response to a specific treatment program. Data at first discharge predicted the response over 7 years. This method enables early detection of the slow responders, who show poor post-acute functional gains, but achieve recovery comparable to fast responders by year 7. Further external validation in other rehabilitation programs is warranted.

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