Self-Reported Complaints as Prognostic Markers for Outcome After Mild Traumatic Brain Injury in Elderly: A Machine Learning Approach

Self-reported complaints are common after mild traumatic brain injury (mTBI). Particularly in the elderly with mTBI, the pre-injury status might play a relevant role in the recovery process. In most mTBI studies, however, pre-injury complaints are neither analyzed nor are the elderly included. Here,...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mayra Bittencourt, Sebastián A. Balart-Sánchez, Natasha M. Maurits, Joukje van der Naalt
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	mild traumatic brain injury (mTBI) older age post-traumatic complaints post-concussive symptoms prognosis recovery Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/3977878171004c5c93ee54919d3d28f2
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3977878171004c5c93ee54919d3d28f2
record_format	dspace
spelling	oai:doaj.org-article:3977878171004c5c93ee54919d3d28f22021-12-02T06:03:00ZSelf-Reported Complaints as Prognostic Markers for Outcome After Mild Traumatic Brain Injury in Elderly: A Machine Learning Approach1664-229510.3389/fneur.2021.751539https://doaj.org/article/3977878171004c5c93ee54919d3d28f22021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fneur.2021.751539/fullhttps://doaj.org/toc/1664-2295Self-reported complaints are common after mild traumatic brain injury (mTBI). Particularly in the elderly with mTBI, the pre-injury status might play a relevant role in the recovery process. In most mTBI studies, however, pre-injury complaints are neither analyzed nor are the elderly included. Here, we aimed to identify which individual pre- and post-injury complaints are potential prognostic markers for incomplete recovery (IR) in elderly patients who sustained an mTBI. Since patients report many complaints across several domains that are strongly related, we used an interpretable machine learning (ML) approach to robustly deal with correlated predictors and boost classification performance. Pre- and post-injury levels of 20 individual complaints, as self-reported in the acute phase, were analyzed. We used data from two independent studies separately: UPFRONT study was used for training and validation and ReCONNECT study for independent testing. Functional outcome was assessed with the Glasgow Outcome Scale Extended (GOSE). We dichotomized functional outcome into complete recovery (CR; GOSE = 8) and IR (GOSE ≤ 7). In total 148 elderly with mTBI (median age: 67 years, interquartile range [IQR]: 9 years; UPFRONT: N = 115; ReCONNECT: N = 33) were included in this study. IR was observed in 74 (50%) patients. The classification model (IR vs. CR) achieved a good performance (the area under the receiver operating characteristic curve [ROC-AUC] = 0.80; 95% CI: 0.74–0.86) based on a subset of only 8 out of 40 pre- and post-injury complaints. We identified increased neck pain (p = 0.001) from pre- to post-injury as the strongest predictor of IR, followed by increased irritability (p = 0.011) and increased forgetfulness (p = 0.035) from pre- to post-injury. Our findings indicate that a subset of pre- and post-injury physical, emotional, and cognitive complaints has predictive value for determining long-term functional outcomes in elderly patients with mTBI. Particularly, post-injury neck pain, irritability, and forgetfulness scores were associated with IR and should be assessed early. The application of an ML approach holds promise for application in self-reported questionnaires to predict outcomes after mTBI.Mayra BittencourtSebastián A. Balart-SánchezNatasha M. MauritsJoukje van der NaaltFrontiers Media S.A.articlemild traumatic brain injury (mTBI)older agepost-traumatic complaintspost-concussive symptomsprognosisrecoveryNeurology. Diseases of the nervous systemRC346-429ENFrontiers in Neurology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	mild traumatic brain injury (mTBI) older age post-traumatic complaints post-concussive symptoms prognosis recovery Neurology. Diseases of the nervous system RC346-429
spellingShingle	mild traumatic brain injury (mTBI) older age post-traumatic complaints post-concussive symptoms prognosis recovery Neurology. Diseases of the nervous system RC346-429 Mayra Bittencourt Sebastián A. Balart-Sánchez Natasha M. Maurits Joukje van der Naalt Self-Reported Complaints as Prognostic Markers for Outcome After Mild Traumatic Brain Injury in Elderly: A Machine Learning Approach
description	Self-reported complaints are common after mild traumatic brain injury (mTBI). Particularly in the elderly with mTBI, the pre-injury status might play a relevant role in the recovery process. In most mTBI studies, however, pre-injury complaints are neither analyzed nor are the elderly included. Here, we aimed to identify which individual pre- and post-injury complaints are potential prognostic markers for incomplete recovery (IR) in elderly patients who sustained an mTBI. Since patients report many complaints across several domains that are strongly related, we used an interpretable machine learning (ML) approach to robustly deal with correlated predictors and boost classification performance. Pre- and post-injury levels of 20 individual complaints, as self-reported in the acute phase, were analyzed. We used data from two independent studies separately: UPFRONT study was used for training and validation and ReCONNECT study for independent testing. Functional outcome was assessed with the Glasgow Outcome Scale Extended (GOSE). We dichotomized functional outcome into complete recovery (CR; GOSE = 8) and IR (GOSE ≤ 7). In total 148 elderly with mTBI (median age: 67 years, interquartile range [IQR]: 9 years; UPFRONT: N = 115; ReCONNECT: N = 33) were included in this study. IR was observed in 74 (50%) patients. The classification model (IR vs. CR) achieved a good performance (the area under the receiver operating characteristic curve [ROC-AUC] = 0.80; 95% CI: 0.74–0.86) based on a subset of only 8 out of 40 pre- and post-injury complaints. We identified increased neck pain (p = 0.001) from pre- to post-injury as the strongest predictor of IR, followed by increased irritability (p = 0.011) and increased forgetfulness (p = 0.035) from pre- to post-injury. Our findings indicate that a subset of pre- and post-injury physical, emotional, and cognitive complaints has predictive value for determining long-term functional outcomes in elderly patients with mTBI. Particularly, post-injury neck pain, irritability, and forgetfulness scores were associated with IR and should be assessed early. The application of an ML approach holds promise for application in self-reported questionnaires to predict outcomes after mTBI.
format	article
author	Mayra Bittencourt Sebastián A. Balart-Sánchez Natasha M. Maurits Joukje van der Naalt
author_facet	Mayra Bittencourt Sebastián A. Balart-Sánchez Natasha M. Maurits Joukje van der Naalt
author_sort	Mayra Bittencourt
title	Self-Reported Complaints as Prognostic Markers for Outcome After Mild Traumatic Brain Injury in Elderly: A Machine Learning Approach
title_short	Self-Reported Complaints as Prognostic Markers for Outcome After Mild Traumatic Brain Injury in Elderly: A Machine Learning Approach
title_full	Self-Reported Complaints as Prognostic Markers for Outcome After Mild Traumatic Brain Injury in Elderly: A Machine Learning Approach
title_fullStr	Self-Reported Complaints as Prognostic Markers for Outcome After Mild Traumatic Brain Injury in Elderly: A Machine Learning Approach
title_full_unstemmed	Self-Reported Complaints as Prognostic Markers for Outcome After Mild Traumatic Brain Injury in Elderly: A Machine Learning Approach
title_sort	self-reported complaints as prognostic markers for outcome after mild traumatic brain injury in elderly: a machine learning approach
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/3977878171004c5c93ee54919d3d28f2
work_keys_str_mv	AT mayrabittencourt selfreportedcomplaintsasprognosticmarkersforoutcomeaftermildtraumaticbraininjuryinelderlyamachinelearningapproach AT sebastianabalartsanchez selfreportedcomplaintsasprognosticmarkersforoutcomeaftermildtraumaticbraininjuryinelderlyamachinelearningapproach AT natashammaurits selfreportedcomplaintsasprognosticmarkersforoutcomeaftermildtraumaticbraininjuryinelderlyamachinelearningapproach AT joukjevandernaalt selfreportedcomplaintsasprognosticmarkersforoutcomeaftermildtraumaticbraininjuryinelderlyamachinelearningapproach
_version_	1718400102930841600

Self-Reported Complaints as Prognostic Markers for Outcome After Mild Traumatic Brain Injury in Elderly: A Machine Learning Approach

Ejemplares similares