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

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.

Mortality Prediction in Severe Traumatic Brain Injury Using Traditional and Machine Learning Algorithms.

Abstract Prognostic prediction of traumatic brain injury (TBI) in patients is crucial in clinical decision and health care policy making. This study aimed to develop and validate prediction models for in-hospital mortality after severe traumatic brain injury (sTBI). We developed and validated logistic regression (LR), LASSO regression, and machine learning (ML) algorithms including support vector machines (SVM) and XGBoost models. Fifty-four candidate predictors were included. Model performance was expressed in terms of discrimination (C-statistic) and calibration (intercept and slope). For model development, 2804 patients with sTBI in the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) China Registry study were included. External validation was performed in 1113 patients with sTBI in the CENTER-TBI European Registry study. XGBoost achieved high discrimination in mortality prediction, and it outperformed logistic and LASSO regression. The XGBoost model established in this study also outperformed prediction models currently available, including the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) core and International Mission for Prognosis and Analysis of Clinical Trials (CRASH) basic models. When including 54 variables, XGBoost and SVM reached C-statistics of 0.87 (95% confidence interval [CI]: 0.81-0.92) and 0.85 (95% CI: 0.79-0.90) at internal validation, and 0.88 (95% CI: 0.87-0.88) and 0.86 (95% CI: 0.85-0.87) at external validation, respectively. A simplified version of XGBoost and SVM using 26 variables selected by recursive feature elimination (RFE) reached C-statistics of 0.87 (95% CI: 0.82-0.92) and 0.86 (95% CI: 0.80-0.91) at internal validation, and 0.87 (95% CI: 0.87-0.88) and 0.87 (95% CI: 0.86-0.87) at external validation, respectively. However, when the number of variables included decreased, the difference between ML and LR diminished. All the prediction models can be accessed via a web-based calculator. Glasgow Coma Scale (GCS) score, age, pupillary light reflex, Injury Severity Score (ISS) for brain region, and the presence of acute subdural hematoma were the five strongest predictors for mortality prediction. The study showed that ML techniques such as XGBoost may capture information hidden in demographic and clinical predictors of patients with sTBI and yield more precise predictions compared with LR approaches.

Does poor methodological quality of prediction modeling studies translate to poor model performance? An illustration in traumatic brain injury.

BACKGROUND: Prediction modeling studies often have methodological limitations, which may compromise model performance in new patients and settings. We aimed to examine the relation between methodological quality of model development studies and their performance at external validation. METHODS: We systematically searched for externally validated multivariable prediction models that predict functional outcome following moderate or severe traumatic brain injury. Risk of bias and applicability of development studies was assessed with the Prediction model Risk Of Bias Assessment Tool (PROBAST). Each model was rated for its presentation with sufficient detail to be used in practice. Model performance was described in terms of discrimination (AUC), and calibration. Delta AUC (dAUC) was calculated to quantify the percentage change in discrimination between development and validation for all models. Generalized estimation equations (GEE) were used to examine the relation between methodological quality and dAUC while controlling for clustering. RESULTS: We included 54 publications, presenting ten development studies of 18 prediction models, and 52 external validation studies, including 245 unique validations. Two development studies (four models) were found to have low risk of bias (RoB). The other eight publications (14 models) showed high or unclear RoB. The median dAUC was positive in low RoB models (dAUC 8%, [IQR - 4% to 21%]) and negative in high RoB models (dAUC - 18%, [IQR - 43% to 2%]). The GEE showed a larger average negative change in discrimination for high RoB models (- 32% (95% CI: - 48 to - 15) and unclear RoB models (- 13% (95% CI: - 16 to - 10)) compared to that seen in low RoB models. CONCLUSION: Lower methodological quality at model development associates with poorer model performance at external validation. Our findings emphasize the importance of adherence to methodological principles and reporting guidelines in prediction modeling studies.

Incremental prognostic value of acute serum biomarkers for functional outcome after traumatic brain injury (CENTER-TBI): an observational cohort study.

BACKGROUND: Several studies have reported an association between serum biomarker values and functional outcome following traumatic brain injury. We aimed to examine the incremental (added) prognostic value of serum biomarkers over demographic, clinical, and radiological characteristics and over established prognostic models, such as IMPACT and CRASH, for prediction of functional outcome. METHODS: We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) core study. We included patients aged 14 years or older who had blood sampling within 24 h of injury, results from a CT scan, and outcome assessment according to the Glasgow Outcome Scale-Extended (GOSE) at 6 months. Amounts in serum of six biomarkers (S100 calcium-binding protein B, neuron-specific enolase, glial fibrillary acidic protein, ubiquitin C-terminal hydrolase L1 [UCH-L1], neurofilament protein-light, and total tau) were measured. The incremental prognostic value of these biomarkers was determined separately and in combination. The primary outcome was the GOSE 6 months after injury. Incremental prognostic value, using proportional odds and a dichotomised analysis, was assessed by delta C-statistic and delta R2 between models with and without serum biomarkers, corrected for optimism with a bootstrapping procedure. FINDINGS: Serum biomarker values and 6-month GOSE were available for 2283 of 4509 patients. Higher biomarker levels were associated with worse outcome. Adding biomarkers improved the C-statistic by 0·014 (95% CI 0·009-0·020) and R2 by 4·9% (3·6-6·5) for predicting GOSE compared with demographic, clinical, and radiological characteristics. UCH-L1 had the greatest incremental prognostic value. Adding biomarkers to established prognostic models resulted in a relative increase in R2 of 48-65% for IMPACT and 30-34% for CRASH prognostic models. INTERPRETATION: Serum biomarkers have incremental prognostic value for functional outcome after traumatic brain injury. Our findings support integration of biomarkers-particularly UCH-L1-in established prognostic models. FUNDING: European Union's Seventh Framework Programme, Hannelore Kohl Stiftung, OneMind, Integra LifeSciences, and NeuroTrauma Sciences.

Outcome Prediction after Moderate and Severe Traumatic Brain Injury: External Validation of Two Established Prognostic Models in 1742 European Patients.

The International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) and Corticoid Randomisation After Significant Head injury (CRASH) prognostic models predict functional outcome after moderate and severe traumatic brain injury (TBI). We aimed to assess their performance in a contemporary cohort of patients across Europe. The Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) core study is a prospective, observational cohort study in patients presenting with TBI and an indication for brain computed tomography. The CENTER-TBI core cohort consists of 4509 TBI patients available for analyses from 59 centers in 18 countries across Europe and Israel. The IMPACT validation cohort included 1173 patients with GCS ≤12, age ≥14, and 6-month Glasgow Outcome Scale-Extended (GOSE) available. The CRASH validation cohort contained 1742 patients with GCS ≤14, age ≥16, and 14-day mortality or 6-month GOSE available. Performance of the three IMPACT and two CRASH model variants was assessed with discrimination (area under the receiver operating characteristic curve; AUC) and calibration (comparison of observed vs. predicted outcome rates). For IMPACT, model discrimination was good, with AUCs ranging between 0.77 and 0.85 in 1173 patients and between 0.80 and 0.88 in the broader CRASH selection (n = 1742). For CRASH, AUCs ranged between 0.82 and 0.88 in 1742 patients and between 0.66 and 0.80 in the stricter IMPACT selection (n = 1173). Calibration of the IMPACT and CRASH models was generally moderate, with calibration-in-the-large and calibration slopes ranging between -2.02 and 0.61 and between 0.48 and 1.39, respectively. The IMPACT and CRASH models adequately identify patients at high risk for mortality or unfavorable outcome, which supports their use in research settings and for benchmarking in the context of quality-of-care assessment.