Personalized Web-Based Cognitive Rehabilitation Treatments for Patients with Traumatic Brain Injury: Cluster Analysis
Authors: Garcia-Rudolph Alejandro , Garcia-Molina Alberto , Opisso Eloy , Tormos Muñoz Jose 
Published: May 2020

Background

Traumatic brain injury (TBI) is a leading cause of disability worldwide. TBI is a highly heterogeneous disease, which makes it complex for effective therapeutic interventions. Cluster analysis has been extensively applied in previous research studies to identify homogeneous subgroups based on performance in neuropsychological baseline tests. Nevertheless, most analyzed samples are rarely larger than a size of 100, and different cluster analysis approaches and cluster validity indices have been scarcely compared or applied in web-based rehabilitation treatments.

Objective

The aims of our study were as follows: (1) to apply state-of-the-art cluster validity indices to different cluster strategies: hierarchical, partitional, and model-based, (2) to apply combined strategies of dimensionality reduction by using principal component analysis and random forests and perform stability assessment of the final profiles, (3) to characterize the identified profiles by using demographic and clinically relevant variables, and (4) to study the external validity of the obtained clusters by considering 3 relevant aspects of TBI rehabilitation: Glasgow Coma Scale, functional independence measure, and execution of web-based cognitive tasks.

Methods

This study was performed from August 2008 to July 2019. Different cluster strategies were executed with Mclust, factoextra, and cluster R packages. For combined strategies, we used the FactoMineR and random forest R packages. Stability analysis was performed with the fpc R package. Between-group comparisons for external validation were performed using 2-tailed t test, chi-square test, or Mann-Whitney U test, as appropriate.

Results

We analyzed 574 adult patients with TBI (mostly severe) who were undergoing web-based rehabilitation. We identified and characterized 3 clusters with strong internal validation: (1) moderate attentional impairment and moderate dysexecutive syndrome with mild memory impairment and normal spatiotemporal perception, with almost 66% (111/170) of the patients being highly educated (P<.05); (2) severe dysexecutive syndrome with severe attentional and memory impairments and normal spatiotemporal perception, with 49.2% (153/311) of the patients being highly educated (P<.05); (3) very severe cognitive impairment, with 45.2% (42/93) of the patients being highly educated (P<.05). We externally validated them with severity of injury (P=.006) and functional independence assessments: cognitive (P<.001), motor (P<.001), and total (P<.001). We mapped 151,763 web-based cognitive rehabilitation tasks during the whole period to the 3 obtained clusters (P<.001) and confirmed the identified patterns. Stability analysis indicated that clusters 1 and 2 were respectively rated as 0.60 and 0.75; therefore, they were measuring a pattern and cluster 3 was rated as highly stable.

Conclusions

Cluster analysis in web-based cognitive rehabilitation treatments enables the identification and characterization of strong response patterns to neuropsychological tests, external validation of the obtained clusters, tailoring of cognitive web-based tasks executed in the web platform to the identified profiles, thereby providing clinicians a tool for treatment personalization, and the extension of a similar approach to other medical conditions.