Dataset Information

Development of prognostic models for Health-Related Quality of Life following traumatic brain injury.

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.

SUBMITTER: Helmrich IRAR

PROVIDER: S-EPMC8847302 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development of prognostic models for Health-Related Quality of Life following traumatic brain injury.

Helmrich Isabel R A Retel IRAR van Klaveren David D Dijkland Simone A SA Lingsma Hester F HF Polinder Suzanne S Wilson Lindsay L von Steinbuechel Nicole N van der Naalt Joukje J Maas Andrew I R AIR Steyerberg Ewout W EW

Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation 20210730 2

<h4>Background</h4>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.<h4>Methods</h4>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 ...[more]

PMID: 34331197

Similar Datasets

Project description:Background Traumatic brain injury (TBI) commonly causes death and disability that can result in productivity loss and economic burden. The health-related quality of life (HRQoL) has been measured in patients suffering from TBI, both in clinical and socioeconomic perspectives. The study aimed to assess the HRQoL in patients following TBI using the European quality of life measure-5 domain-5 level (EQ-5D-5L) questionnaire and develop models for predicting the EQ-5D-5L index score in patients with TBI. Method A cross-sectional study was performed with 193 TBI patients who had completed the EQ-5D-5L questionnaire. The clinical characteristics, Glasgow coma scale (GCS) score, treatment, and Glasgow outcome scale (GOS) were collected. The total data was divided into training data (80%) and testing data (20%); hence, the factors affecting the EQ-5D-5L index scores were used to develop the predictive model with linear and nonlinear regression. The performances of the predictive models were estimated with the adjusted coefficient of determination (R 2 ) and the root mean square error (RMSE). Results A good recovery was found at 96.4%, while 2.1% displayed an unfavorable outcome. Moreover, the mean EQ-5D-5L index scores were 0.91558 (standard deviation [SD] 1.09639). GCS score, pupillary light reflex, surgery, and GOS score significantly correlated with the HRQoL scores. The multiple linear regression model had a high adjusted R 2 of 0.6971 and a low RMSE of 0.06701, while the polynomial regression developed a nonlinear model that had the highest adjusted R 2 of 0.6843 and the lowest RMSE of 0.06748. Conclusions A strong positive correlation between the physician-based outcome as GOS and HRQoL was observed. Furthermore, both the linear and nonlinear regression models were acceptable approaches to predict the HRQoL of patients after TBI. There would be limitations for estimating the HRQoL in unconscious or intubated patients. The HRQoL obtained from the predictive models would be an alternative method to resolve this problem.

Project description:Patient report of functioning is one component of the neurocognitive exam following traumatic brain injury, and standardized patient-reported outcomes measures are useful to track outcomes during rehabilitation. The Traumatic Brain Injury Quality of Life measurement system (TBI-QOL) is a TBI-specific extension of the PROMIS and Neuro-QoL measurement systems that includes 20 item banks across physical, emotional, social, and cognitive domains. Previous research has evaluated the responsiveness of the TBI-QOL measures in community-dwelling individuals and found clinically important change over a 6-month assessment interval in a sample of individuals who were on average 5 years post-injury. In the present study, we report on the responsiveness of the TBI-QOL Cognition-General Concerns and Executive Function item bank scores and the Cognitive Health Composite scores in a recently injured sample over a 1-year study period. Data from 128 participants with complicated mild, moderate, or severe TBI within the previous 6 months were evaluated. The majority of the sample was male, white, and non-Hispanic. The participants were 18-92 years of age and were first evaluated from 0 to 5 months post-injury. Eighty participants completed the 1-year follow-up assessment. Results show acceptable standard response mean values (0.47-0.51) for all measures and minimal detectable change values ranging from 8.2 to 8.8 T-score points for Cognition-General Concerns and Executive Functioning measures. Anchor rating analysis revealed that changes in scores on the Executive Function item bank and the Cognitive Health Composite were meaningfully associated with participant-reported changes in the areas of attention, multitasking, and memory. Evaluation of change score differences by a variety of clinical indicators demonstrated a small but significant difference in the three TBI-QOL change scores by TBI injury severity grouping. These results support the responsiveness of the TBI-QOL cognition measures in newly injured individuals and provides information on the minimal important differences for the TBI-QOL cognition measures, which can be used for score interpretation by clinicians and researchers seeking patient-reported outcome measures of self-reported cognitive QOL after TBI.

Project description:After mild traumatic brain injury (mTBI), a substantial proportion of individuals do not fully recover on the Glasgow Outcome Scale Extended (GOSE) or experience persistent post-concussion symptoms (PPCS). We aimed to develop prognostic models for the GOSE and PPCS at 6 months after mTBI and to assess the prognostic value of different categories of predictors (clinical variables; questionnaires; computed tomography [CT]; blood biomarkers). From the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study, we included participants aged 16 or older with Glasgow Coma Score (GCS) 13-15. We used ordinal logistic regression to model the relationship between predictors and the GOSE, and linear regression to model the relationship between predictors and the Rivermead Post-concussion Symptoms Questionnaire (RPQ) total score. First, we studied a pre-specified Core model. Next, we extended the Core model with other clinical and sociodemographic variables available at presentation (Clinical model). The Clinical model was then extended with variables assessed before discharge from hospital: early post-concussion symptoms, CT variables, biomarkers, or all three categories (extended models). In a subset of patients mostly discharged home from the emergency department, the Clinical model was extended with 2-3-week post-concussion and mental health symptoms. Predictors were selected based on Akaike's Information Criterion. Performance of ordinal models was expressed as a concordance index (C) and performance of linear models as proportion of variance explained (R2). Bootstrap validation was used to correct for optimism. We included 2376 mTBI patients with 6-month GOSE and 1605 patients with 6-month RPQ. The Core and Clinical models for GOSE showed moderate discrimination (C = 0.68 95% confidence interval 0.68 to 0.70 and C = 0.70[0.69 to 0.71], respectively) and injury severity was the strongest predictor. The extended models had better discriminative ability (C = 0.71[0.69 to 0.72] with early symptoms; 0.71[0.70 to 0.72] with CT variables or with blood biomarkers; 0.72[0.71 to 0.73] with all three categories). The performance of models for RPQ was modest (R2 = 4% Core; R2 = 9% Clinical), and extensions with early symptoms increased the R2 to 12%. The 2-3-week models had better performance for both outcomes in the subset of participants with these symptoms measured (C = 0.74 [0.71 to 0.78] vs. C = 0.63[0.61 to 0.67] for GOSE; R2 = 37% vs. 6% for RPQ). In conclusion, the models based on variables available before discharge have moderate performance for the prediction of GOSE and poor performance for the prediction of PPCS. Symptoms assessed at 2-3 weeks are required for better predictive ability of both outcomes. The performance of the proposed models should be examined in independent cohorts.

Dataset Information

Development of prognostic models for Health-Related Quality of Life following traumatic brain injury.

Background

Methods

Results

Conclusion

Publications

Development of prognostic models for Health-Related Quality of Life following traumatic brain injury.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets