Individualized Prognostic Prediction of the Long-Term Functional Trajectory in Pediatric Acquired Brain Injury.
Erika MolteniMarta Bianca Maria RanziniElena BerettaMarc ModatSandra StrazzerPublished in: Journal of personalized medicine (2021)
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.
Keyphrases
- brain injury
- subarachnoid hemorrhage
- cerebral ischemia
- end stage renal disease
- depressive symptoms
- healthcare
- chronic kidney disease
- physical activity
- public health
- quality improvement
- ejection fraction
- social media
- newly diagnosed
- liver failure
- young adults
- machine learning
- single cell
- drug induced
- peritoneal dialysis
- prognostic factors
- acute respiratory distress syndrome
- deep learning
- patient reported outcomes
- hepatitis b virus
- artificial intelligence
- smoking cessation