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

• Published in: Journal of personalized medicine Vol. 11; no. 7; p. 675
• Main Authors: Molteni, Erika, Ranzini, Marta Bianca Maria, Beretta, Elena, Modat, Marc, Strazzer, Sandra
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18.07.2021; MDPI
• Subjects: Age; Brain injury; Brain research; Caregivers; Clustering; Cognitive ability; Coma; Design; Disability; Epilepsy; Etiology; Latent class analysis; Neurosurgery; Patients; Pediatrics; Precision medicine; Predictions; Recovery of function; Rehabilitation; Structural equation modeling; Survival analysis; Traumatic brain injury; Typological analysis; trajectory prediction; single-subject recovery prediction; structural equation modelling (SEM); Functional Independence Measure for children (WeeFIM); mixed models; latent class analysis; acquired brain injury (ABI)
• ISSN: 2075-4426; 2075-4426
• DOI: 10.3390/jpm11070675

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 (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.