Predicting the Health-related Quality of Life in Patients Following Traumatic Brain Injury

Abstract

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 (R2) 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 R2 of 0.6971 and a low RMSE of 0.06701, while the polynomial regression developed a nonlinear model that had the highest adjusted R2 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.