Personalized Web-Based Cognitive Rehabilitation Treatments for Patients with Traumatic Brain Injury

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 perfor...

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Autores: Garcia-Rudolph, Alejandro|||0000-0003-0853-8334, García-Molina, Alberto|||0000-0002-5014-7591, Opisso, Eloy|||0000-0002-6868-6737, Tormos, Jose M.|||0000-0002-8764-2289
Tipo de documento: relatório científico
Data de publicação:2020
País:España
Recursos:Universitat Autònoma de Barcelona
Repositório:Dipòsit Digital de Documents de la UAB
Idioma:inglês
OAI Identifier:oai:ddd.uab.cat:238594
Acesso em linha:https://ddd.uab.cat/record/238594
https://dx.doi.org/urn:doi:10.2196/16077
Access Level:Acceso aberto
Palavra-chave:Cluster analysis
Traumatic brain injury
Web-based rehabilitation
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dc.title.none.fl_str_mv Personalized Web-Based Cognitive Rehabilitation Treatments for Patients with Traumatic Brain Injury
Cluster Analysis
title Personalized Web-Based Cognitive Rehabilitation Treatments for Patients with Traumatic Brain Injury
spellingShingle Personalized Web-Based Cognitive Rehabilitation Treatments for Patients with Traumatic Brain Injury
Garcia-Rudolph, Alejandro|||0000-0003-0853-8334
Cluster analysis
Traumatic brain injury
Web-based rehabilitation
title_short Personalized Web-Based Cognitive Rehabilitation Treatments for Patients with Traumatic Brain Injury
title_full Personalized Web-Based Cognitive Rehabilitation Treatments for Patients with Traumatic Brain Injury
title_fullStr Personalized Web-Based Cognitive Rehabilitation Treatments for Patients with Traumatic Brain Injury
title_full_unstemmed Personalized Web-Based Cognitive Rehabilitation Treatments for Patients with Traumatic Brain Injury
title_sort Personalized Web-Based Cognitive Rehabilitation Treatments for Patients with Traumatic Brain Injury
dc.creator.none.fl_str_mv Garcia-Rudolph, Alejandro|||0000-0003-0853-8334
García-Molina, Alberto|||0000-0002-5014-7591
Opisso, Eloy|||0000-0002-6868-6737
Tormos, Jose M.|||0000-0002-8764-2289
author Garcia-Rudolph, Alejandro|||0000-0003-0853-8334
author_facet Garcia-Rudolph, Alejandro|||0000-0003-0853-8334
García-Molina, Alberto|||0000-0002-5014-7591
Opisso, Eloy|||0000-0002-6868-6737
Tormos, Jose M.|||0000-0002-8764-2289
author_role author
author2 García-Molina, Alberto|||0000-0002-5014-7591
Opisso, Eloy|||0000-0002-6868-6737
Tormos, Jose M.|||0000-0002-8764-2289
author2_role author
author
author
dc.contributor.none.fl_str_mv Universitat Autònoma de Barcelona
dc.subject.none.fl_str_mv Cluster analysis
Traumatic brain injury
Web-based rehabilitation
topic Cluster analysis
Traumatic brain injury
Web-based rehabilitation
description 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. 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. 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. 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. 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.
publishDate 2020
dc.date.none.fl_str_mv 2
2020-01-01
2020
2020-01-01
dc.type.none.fl_str_mv Estudi clínic
http://purl.org/coar/resource_type/c_7877
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/report
format report
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https://dx.doi.org/urn:doi:10.2196/16077
url https://ddd.uab.cat/record/238594
https://dx.doi.org/urn:doi:10.2196/16077
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
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dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
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repository.mail.fl_str_mv
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spelling Personalized Web-Based Cognitive Rehabilitation Treatments for Patients with Traumatic Brain InjuryCluster AnalysisGarcia-Rudolph, Alejandro|||0000-0003-0853-8334García-Molina, Alberto|||0000-0002-5014-7591Opisso, Eloy|||0000-0002-6868-6737Tormos, Jose M.|||0000-0002-8764-2289Cluster analysisTraumatic brain injuryWeb-based rehabilitationTraumatic 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. 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. 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. 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. 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.Universitat Autònoma de Barcelona 22020-01-0120202020-01-01Estudi clínichttp://purl.org/coar/resource_type/c_7877VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/reportapplication/pdfhttps://ddd.uab.cat/record/238594https://dx.doi.org/urn:doi:10.2196/16077reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2385942026-06-06T12:50:31Z
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