Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/33048
Appears in Collections:Psychology Journal Articles
Peer Review Status: Refereed
Title: Development of prognostic models for Health-Related Quality of Life following traumatic brain injury
Author(s): Retel Helmrich, Isabel R A
van Klaveren, David
Dijkland, Simone A
Lingsma, Hester F
Polinder, Suzanne
Wilson, Lindsay
von Steinbuechel, Nicole
van der Naalt, Joukje
Maas, Andrew I R
Steyerberg, Ewout W
Keywords: Prognostic model research
Traumatic brain injury
Health-related quality of life
SF-36
QOLIBRI
Issue Date: 30-Jul-2021
Date Deposited: 9-Aug-2021
Citation: Retel Helmrich IRA, van Klaveren D, Dijkland SA, Lingsma HF, Polinder S, Wilson L, von Steinbuechel N, van der Naalt J, Maas AIR & Steyerberg EW (2021) Development of prognostic models for Health-Related Quality of Life following traumatic brain injury. Quality of Life Research. https://doi.org/10.1007/s11136-021-02932-z
Abstract: Background Traumatic brain injury (TBI) is a leading cause of impairments affecting Health-Related Quality of Life (HRQoL). We aimed to identify predictors of and develop prognostic models for HRQoL following TBI. Methods We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Core study, including patients with a clinical diagnosis of TBI and an indication for computed tomography presenting within 24 h of injury. The primary outcome measures were the SF-36v2 physical (PCS) and mental (MCS) health component summary scores and the Quality of Life after Traumatic Brain Injury (QOLIBRI) total score 6 months post injury. We considered 16 patient and injury characteristics in linear regression analyses. Model performance was expressed as proportion of variance explained (R2) and corrected for optimism with bootstrap procedures. Results 2666 Adult patients completed the HRQoL questionnaires. Most were mild TBI patients (74%). The strongest predictors for PCS were Glasgow Coma Scale, major extracranial injury, and pre-injury health status, while MCS and QOLIBRI were mainly related to pre-injury mental health problems, level of education, and type of employment. R2 of the full models was 19% for PCS, 9% for MCS, and 13% for the QOLIBRI. In a subset of patients following predominantly mild TBI (N = 436), including 2 week HRQoL assessment improved model performance substantially (R2 PCS 15% to 37%, MCS 12% to 36%, and QOLIBRI 10% to 48%). Conclusion Medical and injury-related characteristics are of greatest importance for the prediction of PCS, whereas patient-related characteristics are more important for the prediction of MCS and the QOLIBRI following TBI.
DOI Link: 10.1007/s11136-021-02932-z
Rights: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Notes: Output Status: Forthcoming/Available Online
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

Files in This Item:
File Description SizeFormat 
Helmrich2021_Article_DevelopmentOfPrognosticModelsF.pdfFulltext - Published Version1.06 MBAdobe PDFView/Open



This item is protected by original copyright



A file in this item is licensed under a Creative Commons License Creative Commons

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.