Statistical models for outcome prediction are central to traumatic brain injury research and critical to baseline risk adjustment. Glasgow coma score (GCS) and pupil reactivity are crucial... Show moreStatistical models for outcome prediction are central to traumatic brain injury research and critical to baseline risk adjustment. Glasgow coma score (GCS) and pupil reactivity are crucial covariates in all such models but may be measured at multiple time points between the time of injury and hospital and are subject to a variable degree of unreliability and/or missingness. Imputation of missing data may be undertaken using full multiple imputation or by simple substitution of measurements from other time points. However, it is unknown which strategy is best or which time points are more predictive. We evaluated the pseudo-R-2 of logistic regression models (dichotomous survival) and proportional odds models (Glasgow Outcome Score-extended) using different imputation strategies on the The Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study dataset. Substitution strategies were easy to implement, achieved low levels of missingness (<< 10%) and could outperform multiple imputation without the need for computationally costly calculations and pooling multiple final models. While model performance was sensitive to imputation strategy, this effect was small in absolute terms and clinical relevance. A strategy of using the emergency department discharge assessments and working back in time when these were missing generally performed well. Full multiple imputation had the advantage of preserving time-dependence in the models: the pre-hospital assessments were found to be relatively unreliable predictors of survival or outcome. The predictive performance of later assessments was model-dependent. In conclusion, simple substitution strategies for imputing baseline GCS and pupil response can perform well and may be a simple alternative to full multiple imputation in many cases. Show less
Helmrich, I.R.A.R.; Klaveren, D. van; Dijkland, S.A.; Lingsma, H.F.; Polinder, S.; Wilson, L.; ... ; CENTER TBI Collaborators 2021
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... Show moreBackground 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 (R-2) 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. R-2 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 (R-2 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. Show less
Dijkland, S.A.; Helmrich, I.R.A.R.; Nieboer, D.; Jagt, M. van der; Dippel, D.W.J.; Menon, D.K.; ... ; CENTER-TBI Participants Investig 2020
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... Show moreThe 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. Show less
Dijkland, S.A.; Jaja, B.N.R.; Jagt, M. van der; Roozenbeek, B.; Vergouwen, M.D.I.; Suarez, J.I.; ... ; SAHIT Collaboration 2020
OBJECTIVE Differences in clinical outcomes between centers and countries may reflect variation in patient characteristics, diagnostic and therapeutic policies, or quality of care. The purpose of... Show moreOBJECTIVE Differences in clinical outcomes between centers and countries may reflect variation in patient characteristics, diagnostic and therapeutic policies, or quality of care. The purpose of this study was to investigate the presence and magnitude of between-center and between-country differences in outcome after aneurysmal subarachnoid hemorrhage (aSAH).METHODS The authors analyzed data from 5972 aSAH patients enrolled in randomized clinical trials of 3 different treatments from the Subarachnoid Hemorrhage International Trialists (SAHIT) repository, including data from 179 centers and 20 countries. They used random effects logistic regression adjusted for patient characteristics and timing of aneurysm treatment to estimate between-center and between-country differences in unfavorable outcome, defined as a Glasgow Outcome Scale score of 1-3 (severe disability, vegetative state, or death) or modified Rankin Scale score of 4-6 (moderately severe disability, severe disability, or death) at 3 months. Between-center and between-country differences were quantified with the median odds ratio (MOR), which can be interpreted as the ratio of odds of unfavorable outcome between a typical high-risk and a typical low-risk center or country.RESULTS The proportion of patients with unfavorable outcome was 27% (n = 1599). The authors found substantial between-center differences (MOR 1.26, 95% CI 1.16-1.52), which could not be explained by patient characteristics and timing of aneurysm treatment (adjusted MOR 1.21, 95% CI 1.11-1.44). They observed no between-country differences (adjusted MOR 1.13, 95% CI 1.00-1.40).CONCLUSIONS Clinical outcomes after aSAH differ between centers. These differences could not be explained by patient characteristics or timing of aneurysm treatment. Further research is needed to confirm the presence of differences in outcome after aSAH between hospitals in more recent data and to investigate potential causes. Show less
