Handbook of Clinical Neurology, Vol. 128 (3rd series) Traumatic Brain Injury, Part II J. Grafman and A.M. Salazar, Editors © 2015 Elsevier B.V. All rights reserved

Chapter 29

Predicting outcome after traumatic brain injury ANDREW I.R. MAAS1*, HESTER F. LINGSMA2, AND BOB ROOZENBEEK3 Department of Neurosurgery, Antwerp University Hospital and University of Antwerp, Edegem, Belgium

1

2

Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands 3

Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands

No head injury is too severe to be despaired of, nor too trivial to be ignored. Hippocrates

INTRODUCTION Interest in prognosis after traumatic brain injury (TBI) dates back to classical times. In Ancient Greece, the quality of care was judged not so much by the actual result of treatment, but rather by the accuracy of the physician’s prediction of outcome. Estimates of prognosis – often subconsciously applied – are an important component in clinical decision making. However, as captured in the Hippocratic aphorism quoted above, it has always been considered difficult to predict the likely outcome in patients with TBI. For many years estimates of prognosis after TBI were little more than prophecies based upon clinical experience of physicians. The science of clinical decision making and advances in statistical modeling allow us now to consider prognosis in terms of probabilities rather than vague prophecies. Standardization of the assessment of initial severity following the introduction of the Glasgow Coma Scale (GCS) (Teasdale and Jennett, 1974) and standardized approaches to outcome assessment based upon the Glasgow Outcome Scale (GOS) (Jennett and Bond, 1975) have facilitated prognostic analysis in TBI. Furthermore, the availability of large databases has offered new opportunities for an evidence-based approach. Quantification of prognostic risk and predictive statements can be useful in a number of ways. Concern about the most likely outcome is of paramount importance to relatives, and prognostic estimates facilitate realistic counseling. The role of quantification of

prognostic risk in influencing decisions about the management in individual patients is more controversial. Although many physicians acknowledge that prognostic estimates have an important role in decision making, others attribute only a minor or even nonexistent role to prognosis. This difference reflects a range of attitudes influenced by both ethical and cultural differences as well as by clinical convictions. Nevertheless, some form of prognostic estimate is consciously or subconsciously used by physicians when allocating resources and prioritizing treatment – particularly in situations where resources may be more limited. Caution in the interpretation of prognostic risk estimates is appropriate: a prognostic estimate in an individual patient concerns a probabilistic equation with a range of uncertainties reflected in the confidence interval (CI). We should further recognize that predictive equations can never include all items relevant to an individual patient. Estimates derived from evidence-based analysis of large datasets remain preferable to a clinical prophecy, as estimates performed by physicians are often unduly optimistic, or, on the other hand, sometimes even unnecessarily pessimistic or inappropriately ambiguous (Barlow and Teasdale, 1986; Chang et al., 1989; Dawes et al., 1989; Kaufmann et al., 1992). No single clinician’s experience can ever match the wealth of data available in databases consisting of thousands of patients. The most important application of prognostic analysis in TBI is perhaps not so much at the level of the individual patient, but more at the group level. Patient populations can be characterized by baseline prognostic risk, thus facilitating more accurate and valid comparisons between different studies. Moreover, estimation of the baseline prognostic risk can be used as a benchmark for

*Correspondence to: Andrew I.R. Maas, M.D., Ph.D., Department of Neurosurgery, Antwerp University Hospital/University of Antwerp, Wilrijkstraat 10, 2650 Edegem, Belgium. Tel: +32-3-821-46-32, E-mail: [email protected]

456

A.I.R. MAAS ET AL.

evaluating quality of care. Finally, prognostic analysis and identification of covariates are important for stratification and covariate adjustment in clinical trials. In this chapter, we focus on the prediction of outcome in terms of mortality and functional outcome in adult patients with moderate and severe TBI. Cognitive and psychosocial outcomes are addressed in more detail in Chapters 31, 40, and 44. Specific pediatric considerations are described in Chapters 15 and 41. We aim to describe the basics of prognostic analysis and to review current knowledge about traditional and newly recognized predictors for outcome. We will also discuss prognostic modeling as an important instrument in clinical practice and research and critically review existing models. Finally, we will discuss the potential of prognostic analysis in the field of TBI.

METHODOLOGY OF PROGNOSTIC STUDIES Prognostic studies are inherently longitudinal and most commonly performed in cohorts of patients with outcome determined at a fixed time point. The cohort is defined by the presence of one or more particular characteristics such as hospital admission for TBI. It is important to define the cohort as accurately as possible

in order to prevent a bias in the selection of patients for participation. Several steps can be identified in prediction research (Table 29.1): univariate analysis, multivariable analysis, and the development of prediction models. In univariate analysis the association between a single predictor and outcome is analyzed. For example, we might ask what the association is between absent pupillary reactivity to light on admission and the 6 month GOS. Importantly, we should recognize that a univariate association does not take into account the role of other predictors that may contribute to the observed association. In other words, associations determined in univariate analysis do not necessarily represent causality and may be secondary to other factors. In multivariable analysis the focus is more on the actual predictive value of a specific variable in relation to other covariates. Questions appropriate for multivariable analysis are, for example, what are the most important predictors in TBI. Multivariable analysis provides insight into whether apparent predictive effects might be explained by other predictors. Multivariable regression analysis is the most commonly used statistical technique for multivariable analysis, adjusting for correlated predictors in the assessment of predictive effects.

Table 29.1 Steps in prognostic analysis in traumatic brain injury Aim

Limitations

Performance measures

Univariate analysis

To estimate the relation between a single predictor and outcome

Multivariable analysis

To determine the prognostic value of a predictor, adjusting for confounding effects of other predictors

Sensitivity, specificity, positive predictive value, negative predictive value, odds ratio Odds ratio Risk ratio Nagelkerkers R2

Prediction models

To combine predictors into a model to estimate the risk of an outcome for individual patients

Does not take into account the role of confounding factors that may explain (part of) the observed association In individual patients, predictors may influence outcome in opposite directions; does not take into account interactions or differential effects for specific subpopulations External validation essential to prove generalizability outside the development setting

Discrimination: area under the receiver operating characteristic curve Calibration: graphical representation Hosmer-Lemeshow goodness of fit test

Sensitivity, proportion of patients with the outcome that have the predictor (true positive); specificity, proportion of patients without the outcome that do not have the predictor (true negative); positive predictive value, proportion of patients with the predictor that have the outcome; negative predictive value, proportion of patients without the predictor that do not have the outcome; odds ratio, ratio of the odds for better versus poorer outcome in the presence of the variable, compared with the odds in the absence of the variable; risk ratio, risk of outcome in group with the predictor versus group without the predictor; R2, proportion of variability in outcome that is explained by the predictor; R2 indicates predictive value better than odds ratio does, because prevalence is also taken into account. (Adapted from Lingsma et al., 2010.)

PREDICTING OUTCOME AFTER TRAUMATIC BRAIN INJURY Prognostic modeling aims to combine information from different prognostic features into a prognostic model (mathematical equation) to predict outcome in individual patients. Prediction models, providing estimates of prognostic probabilities, are becoming an increasingly important tool in clinical medicine. Single predictors often have insufficient predictive value to distinguish between patients with a favorable or unfavorable outcome. Moreover, patients may have different characteristics that affect prognosis in opposite directions: for example, for a 65-year-old patient with reactive pupils we may predict a less favorable outcome based on age, but a better outcome based on pupillary reactivity. Prognostic modeling is by definition a multivariable challenge in which risk factors need to be considered jointly. It should be recognized, however, that prognostic models – sophisticated as they may be – provide probabilistic calculations with an inherent degree of uncertainty. This uncertainty is expressed in the confidence interval surrounding the prognostic estimate. Common approaches to develop prediction models include regression analysis, recursive partitioning, classification and regression trees (CART), as well as neural networks. Adequate sample sizes are required to address scientific questions with empirical data. Whilst in clinical trials the required sample size can be calculated accurately depending on hypotheses, effect size, and calculation of statistical power, this is more complex in prognostic studies. The effective sample size is determined more by the number of events in the study and not by the total number of subjects. For example, when we study a disease with a 10% chance of mortality, a study with 100 patients will contain only 10 events and this number determines the effective sample size. Small sample sizes will only permit relatively simple analysis whereas more complex questions will require larger sample sizes. As a rule of thumb, the number of predictors that can be analyzed is determined by the equation numberof events 10 For example, prognostic analysis with mortality as the end point in a cohort of 1000 patients with a mortality rate of 10% permits analysis of 10 predictors.

Expressing prognostic strength Various measures exist to express the association between predictors and outcome (Table 29.2). Different opinions may exist as to the preferred measure. In the first edition of the Guidelines on Management and Prognosis of Severe Head Injury, the positive predictive value (PPV) was used as primary measure for expressing prognostic strength (Brain Trauma Foundation, 2001).

457

The PPV, however, is limited in value because it does not take into account the prevalence of a predictor within the population. Sensitivity and specificity are commonly used when describing diagnostic performances but are less commonly applied in prognostic analysis. When used, they should always be taken in combination. A widely used measure for expressing the strength of prognostic effects is the odds ratio (OR). In multivariable analysis, the OR provided by the regression models are adjusted for the other predictors in the model and thus provide a true indication of prognostic strength. An odds ratio of 1 indicates absence of prognostic effect. The relation is significant if the 95% CI of the OR does not include the value 1. A relative disadvantage of the OR is that is does not account for the prevalence of a predictor. Both the independent prognostic strength and prevalence are taken into consideration in measures such as Nagelkerke’s R2 which quantifies the variation explained by one predictor, or a set of predictors. The R2 can also be used to express the added predictive value of one or more predictors over and above others.

Choice and selection of predictors The choice for considering a predictor is generally based upon prior knowledge or expectations that a certain factor might be associated with outcome. To reduce the number of predictors and select the most important ones, various approaches can be taken. Data-driven approaches include forward and backward stepwise selection. However, these approaches should be reserved for large datasets, since in relatively small samples datadriven selection of predictors might lead to biased estimates, overfitted models, and poor external validity. To prevent such problems, selection of predictors can better be based on prior knowledge from the literature or clinical experience. The relevance of a predictor is not only determined by the prognostic strength, but also by the prevalence of a predictor. If, for example, two predictors have a similar prognostic strength (e.g., comparable ORs) but the one has a prevalence of 50% and the other only 1%, the first will be clinically much more relevant. Besides clinical and statistical considerations, practical aspects may play a role in deciding which predictors to include in a model. Especially when one aims to develop a model for use in clinical practice, the availability, reliability, and costs of a predictor should be considered in relation to the (additional) predictive value.

Scoring and handling of predictors Predictors should be well defined and reliably measurable by any observer. This is important as observer variation may cloud prognostic analysis. Variability in measurement may, however, also result from biological

458

A.I.R. MAAS ET AL.

Table 29.2 Performance measures of predictors Measure

Equation

Definition

Interpretation

Relative risk (RR)

a/(a + b) c/(c + d)

Risk of outcome in group with predictor/risk of outcome without predictor

Odds ratio (OR)

a*d b*c

Ratio of the odds for better versus poorer outcome in the presence of the parameter (a/b) compared to the odds in the absence of the parameter (c/d)

R2

1-exp(-LR/n) 1-exp(-L0/n)

Sensitivity

a. a+c d. b+d a. a+b

Model sum of squares (¼parameter of regression model/total sum of squares (¼parameter of the regression model) Number of true positives/total number with the outcome Number of true negatives/total number without the outcome Number of true positives/number of positives

E.g. RR of 2 means that the group with the predictor has twice the risk of the group without the predictor. When the predictor is continuous, RR represents the increase per unit If the prognostic factor is not associated with outcome, the odds ratio will be 1. In reporting the odds ratio, the 95% confidence interval (CI) are frequently included. Statistical significance of the relationship is present if the CI does not include the value 1 Percentage of variability in the outcome that is explained by the predictor(s)

d. c+d

Number of true negatives/number of negatives

Proportion of patients without the predictor that do not have the outcome

Dead

Alive

a c

b d

Specificity Positive predictive value (PPV) Negative predictive value (NPV)

Predictor present Predictor absent

Proportion of patients with the outcome that have the predictor (true positive) Proportion of patients without the outcome that do not have the predictor (true negative). Proportion of patients with the predictor that do have the outcome

(Adapted from Maas et al., 2011.)

variability, such as different levels of blood pressure at different times. It should therefore be recognized that numerical values of single measurements may be misleading, and it is important to clearly define at which time point predictors are measured. Predictors may be scored as binary variables, as categorical variables, or in a continuous way. An example of a binary variable is gender, of a categorical variable “cause of injury,” whilst age constitutes a continuous parameter. In practice, many continuously available variables are collapsed for analysis into categorical or binary values. For example, for investigating the association between age and outcome, threshold values are used to categorize age ranges (e.g., by decade) or age is collapsed further to a binary variable, for example, age below or above 60 years. This approach has numerous disadvantages: from a clinical perspective one would not expect prognostic risk to be much different for a

patient of, e.g., 59 years of age versus that for a patient of 60 or 61. Second, from a methodological perspective, collapsing an ordinal or continuous scale into a binary variable leads to loss of information and is statistically inefficient (Royston et al., 2006). It is therefore advised to analyze continuous variables in their continuous nature.

Missing data Missing values occur in any clinical database, particularly in observational studies. Dealing with missing data is therefore an important, but often underappreciated issue. A common approach is to simply delete patients with missing values from the analysis. This scenario represents “complete case analysis” (Little and Rubin, 2002). Whilst this approach is simple to understand, it discards information from patients who may have

PREDICTING OUTCOME AFTER TRAUMATIC BRAIN INJURY information on some, but not all predictors. Thus, it is statistically inefficient. Moreover, complete case analysis may lead to a biased estimate as there may be a systematic reason for some patients having missing data (Vach and Blettner, 1991). Such bias occurs when missingness (absence of a predictor) is associated with outcome. Alternatively, imputation procedures can be used to replace missing values with the most likely estimate for a plausible value. Advances in statistical techniques now offer opportunities for imputation of missing values with regression models that estimate the most plausible value based on observed data, with either simple or multiple imputation. In single imputation a single dataset with imputed values is created. In multiple imputation, multiple datasets (e.g., 5) with imputed values are created. Subsequent analyses are performed in each dataset and the estimates are summarized. Although multiple imputation better captures the uncertainty of the imputed values, current statistical insight is that any imputation procedure is preferable to complete case analysis (Steyerberg, 2008). Deciding which variables to use for imputation requires sensible judgment by the analyst, based on knowledge of the subject and the research question.

OUTCOME MEASURES FOR PROGNOSTIC STUDIES IN PATIENTS WITH TRAUMATIC BRAIN INJURY Various outcome measures are used in prognostic analysis for TBI. These include mortality, global outcome measures, e.g., GOS, measures of disability (functional

459

independence measure (FIM)), patient-centered outcome (e.g., scores on quality of life questionnaires), or wider indicators of burden of disease (e.g., absence from work). The choice for a particular outcome measure will depend on characteristics of the population under study. Mortality may be a poor end point for a study focused on patients with mild TBI but is more appropriate for population with more severe injuries. Conversely, even the eight category extended GOS may not permit sufficient differentiation of outcome in patients with milder injuries. In general, the outcome measure chosen should be clinically relevant to the population under study and “hard” end points are preferred. Statistical power may also direct the choice of outcome. When an outcome is infrequent, it may not be suited as an end point for statistical analysis. Whatever the end point chosen, assessment at a fixed time point is essential. Many studies, however, report on mortality or even the GOS on discharge. We do not consider this advisable, as local discharge policies may bias the outcome estimate. If, for example, local policy would be to discharge a patient considered to have a poor prognosis early to a nursing facility, mortality may be underestimated. The GOS is commonly used as end point for prognostic studies in populations with moderate or severe TBI (Table 29.3). The GOS is an ordinal scale with five categories. It should be recognized that it is not an interval scale, meaning that calculation of an average value of the GOS is not appropriate. Furthermore, the GOS is a global functional scale in which functioning in various domains is integrated. Outcome following TBI is, however, complex and multidimensional, causing the GOS to

Table 29.3 Glasgow Outcome Scale and its extended version GOS 1 2 3

4

Death Mortality from any cause Vegetative state Unable to interact with environment, unresponsive Severe disability Conscious but dependent Moderate disability Independent but disabled

GOSE 1

Death

2

Vegetative state

3

Lower: dependent on others for activities of daily living Upper: dependent on others for some activities

4 5 6

5

Good recovery Return to normal occupation and social activities, may have minor residual deficits

7

8 GOS: Glasgow Outcome Scale; GOSE: Glasgow Outcome Scale - Extended.

Lower: unable to return to work or participate in social activities Upper: return to work at reduced capacity, reduced participation in social activities Lower: minor social or mental deficits which do not impair normal functioning Upper: full recovery, no residual complaints or deficits

460

A.I.R. MAAS ET AL.

perhaps be less sensitive, particularly in the upper levels. The eight point extended GOS (GOSE) was introduced to increase sensitivity. The use of a structured interview to determine a patient’s GOS score is advocated to obtain more consistency in outcome assignment (Wilson et al., 1998). Despite the desire for a scale of increased sensitivity, in practice both the GOS and the GOSE are commonly collapsed into a dichotomous variable differentiating favorable from unfavorable outcome. As in coding predictors, collapsing outcome scales causes loss of information, and we consider it preferable to quantify prognostic effects across the full range of the GOS, rather than dichotomizing it into a binary variable. To this purpose, approaches to analyze the GOS in an ordinal way may be applied, such as proportional odds regression. Even with the use of an ordinal approach towards prognostic analysis, both the GOS and GOSE remain summary scales with broad categories and do not discriminate between physical and mental disabilities. More specific tools for outcome assessment include the functional independence measure (Gabbe et al., 2008), cognitive testing, and quality of life assessment (Wilde et al., 2010). Generic health-related quality of life measures, such as the SF36, have not been commonly used in the assessment of outcome after TBI and may not capture domains particularly relevant to TBI. These domains are included more specifically in disease-specific scales such as the QOLIBRI scale (quality of life after brain injury) which has recently been introduced (Truelle et al., 2010; von Steinbuechel et al., 2010a, b). The QOLIBRI scale rates the subject’s perception of quality of life in six domains: cognition, self, daily life and autonomy, social relationships, emotions, and physical problems using a five point Likert scale. In 2012, the QOLIBRI overall scale was introduced as a brief index of health-related quality of life after TBI with a similar construct as the QOLIBRI total score (von Steinbuechel et al., 2012). Quality-of-life scales are characterized by reporting of the subjective experience of the patient or caretaker and consequently provide a different perspective than perhaps more objective assessments by healthcare professionals. In our opinion the complexity of outcome following TBI makes a strong case for the development of composite outcome measures, summarizing the multiple facets of outcome following TBI into a multidimensional summary scale.

BUILDING BLOCKS FOR PROGNOSTIC ANALYSIS Conceptually, the main predictors of outcome after TBI can be grouped together into “building blocks,” adding further information as this becomes available over time (Table 29.4). Early end points, such as the occurrence of

neuroworsening, intracranial pressure (ICP) control, or the results of advanced neuroimaging studies, may be considered as early end points for prognostic analysis based upon admission predictors, but may function as predictors for late outcome. Estimating prognosis should therefore be considered part of a dynamic process. This was already demonstrated in the classic prognostic studies of Jennett et al. (1976) in which different prognostic models were described for the time periods: admission, 24 hours, 2–3 days, and 4–7 days. The majority of prognostic studies have, however, concentrated on the association between predictors available upon admission and late outcome. An extensive overview of the univariate association between predictors and outcome in patients with severe closed TBI is contained in the section “early indicators of prognosis in severe TBI” of the Brian Trauma Foundation’s Guidelines on Management and Prognosis of Severe Head Injury, published in July 2000 (www.tbiguidelines.org). Only relatively few studies, summarized in this evidence-based overview, reported on results of multivariable analysis. Extensive results of multivariable analysis were reported by the IMPACT study group in a meta-analysis of individual patient data from eight randomized control trials and three observational series, including data from over 9000 patients (Maas et al., 2007; Murray et al., 2007). The main predictors and their prognostic strengths are summarized in Table 29.5. Figure 29.1 presents the prognostic values, expressed as the explained variance (Nagelkerke R2) for the different components for the building blocks available upon admission. In combination, these predictors explain approximately 35% of that variance in outcome following moderate to severe TBI. There is therefore a strong incentive to develop “novel and emerging” biomarkers which may be used not only for better characterization of TBI and tracking of disease processes but also to add prognostic information. Below we discuss the available evidence on “traditional” and “novel and emerging” predictors in more detail.

“Traditional” predictors DEMOGRAPHIC FACTORS Age is one of the strongest predictors of outcome after TBI. The association between increasing age and poorer outcome has been demonstrated in many publications (Signorini et al., 1999; Gomez et al., 2000; Ono et al., 2001; Andrews et al., 2002; Ratanalert et al., 2002; Hukkelhoven et al., 2003; Bahloul et al., 2004; Demetriades et al., 2004; Mushkudiani et al., 2007; MRC CRASH Trial Collaborators, 2008; Tokutomi et al., 2008). Most of these studies have used threshold values varying from 30 to 60 years of age in their

PREDICTING OUTCOME AFTER TRAUMATIC BRAIN INJURY

461

Table 29.4 Building blocks for prognostic analysis Characteristics of the individual Biological constitution Genotype Demographic factors Age Race Socioeconomic status and education Medical history

Admission

Clinical course

Early end points

Outcome

Injury details Type (closed, penetrating, etc.) Cause

Biological response to injury Metabolomics

Early mortality (day 14)

Mortality

Change in adm. parameters Clinical severity Change in CT Biomarkers, laboratory values

Neuroworsening

Clinical severity Intracranial (GCS/ pupils) Extracranial (AIS/ ISS) Second insults Systemic (hypoxia, hypotension, hypothermia) Intracranial (neuroworsening, seizures)

GOS (E)

New predictors Second insult Clinical monitoring (ICP, brain tissue PO2, evoked potentials)

HRQoL ICP control Neuroimaging

Neuroimaging Neuropsychological assessment

CT characteristics Biomarkers/ laboratory values CT, computed tomography; GCS, Glasgow Coma Scale; AIS, Abbreviated Injury Score; ISS, Injury Severity Score; GOS, Glasgow Outcome Scale; GOS (E), Glasgow Outcome Scale - Extended; ICP, intracranial pressure; HRQoL, health-related quality of life.

analysis. Only few studies have used a continuous analysis for exploring the association between age and outcome. Some of these studies report a change around age 30–40 years, above which outcome is increasingly poorer, other report a more continuous relation across all ages, which may be approximated by a linear function (Gomez et al., 2000; Hukkelhoven et al., 2003; Mushkudiani et al., 2007; MRC CRASH Trial Collaborators, 2008; Tokutomi et al., 2008). The continuous association between age and outcome is demonstrated in Figure 29.2. Although males are more prone to sustain TBI in road traffic accidents and assaults, a clear association between gender and outcome assessed by the GOS has not been shown for TBI. A meta-analysis conducted by Farace and Alves (2000), however, did find poorer quality of life and worse functional outcome in females who survived severe TBI compared to males. Thus, the possibility that there may be some effect of gender on outcome remains present, although it is unlikely that this can be captured by global assessment scales such as the GOS. An association between race and outcome after TBI exists, but is poorly

understood. A meta-analysis performed by the IMPACT study group, combining evidence from 5330 patients, confirmed an association between race and outcome and reported that black patients have a poorer outcome than white or Asian patients. Further evidence in support of poorer outcome with black patients was reported by Sorani et al. (2009), Shafi et al. (2007), and ArangoLasprilla et al. (2007). Arango-Lasprilla et al. (2007) confirmed an association between minority status and poorer functional outcome in a large cohort of the US-based TBI model systems database. After controlling for sociodemographic, injury, and functional characteristics, Hispanics and African Americans had lower scores on the 1 year assessments of Disability Rating Scale (DRS), Functional Independent Measures (FIM), and Community Integration Questionnaire (CIQ) compared to whites. The underlying reasons for this association are poorly understood, but may include differences in genetic constitution and differences in access to acute and postacute care. Exploring possible reasons for the effect of race on outcome after TBI should be considered a priority for further research.

462

A.I.R. MAAS ET AL.

Table 29.5 Strength of the association between predictors and outcome in traumatic brain injury Predictor Demographics Age Gender Race: black Asian Clinical severity Motor score Absent Abnormal extension Abnormal flexion Flexion Pupillary reactivity One reacting Both nonreacting Extracranial injuries Secondary insults Hypotension Hypoxia Hypothermia Structural abnormalities CT classification CT class I CT class III/IV Mass lesion tSAH present Type of intracranial lesion Epidural Laboratory parameters Glucose pH Prothrombine time Hb Sodium 25 mL Midline shift > 5 mm, no mass lesion > 25 mL Any lesion surgically evacuated High or mixed density lesion > 25 mL, not surgically evacuated

Table 29.7 Rotterdam prognostic CT score Predictor value Basal cisterns Normal Compressed Absent Midline shift No shift or shift  5 mm Shift > 5 mm Epidural mass lesion Present Absent Intraventricular blood or tSAH Absent Present Sum score*

Score

0 1 2 0 1 0 1 0 1 S+1

*

Sum score can be used to obtain the predicted probability of mortality from the formulae below. We chose to add plus 1 to make the grading numerically consistent with the grading of the motor score of the GCS and with the Marshall CT classification. The corresponding probabilities are calculated with the formula: Probability (mortality), 1/[1 + e-(-2.60+0.80* sumscore)]. tSAH, traumatic subarachnoid hemorrhage.

Prognostic studies focusing on CT characteristics have in general used relatively broad categorization. In traumatic subarachnoid hemorrhage, for example, most studies have concentrated on the simple presence or absence of this finding without differentiating the location (basal cistern versus cortical) or extent. Quantification of the volume of lesions or of the degree of midline shift has seldom been used but recent studies have shown that quantitative CT analysis can add to prognostic information (Yuh et al., 2012). Although, MR imaging, and specifically advanced imaging techniques (discussed below) permit more sensitive detection of white matter tract and/or brainstem lesions, these studies can seldom be performed in the acute situation, mainly for logistic reasons. Thus, we

do not expect the advanced MR imaging to contribute much to the prognosis of TBI in the acute situation. Here, more detailed descriptive analysis of CT characteristics and quantified CT studies have the greatest potential to improve classification and prognosis after TBI. However, in the subacute phase (e.g., 2–3 weeks) MR imaging may offer important contributions, in both patients with severe and those with milder injuries. Yuh et al. (2013) reported an abnormal early brain MR in 27% of patients with mild TBI and a normal CT examination on admission. The presence of structural MR abnormalities was related to an incidence of problems at 3 month outcome. In a series of 105 patients who remained comatose for at least 7 days after TBI, Galanaud et al. (2012) reported that quantitative DTI increases the accuracy of long-term outcome prediction.

Novel and emerging predictors GENETIC CONSTITUTION Several genes and their polymorphisms are under investigation in patients with TBI (see also Ch. 3). Genetic variation may possibly be one of the reasons why the clinical course and outcome may be quite different between patients with similar injuries and injury severity upon admission. Various studies have shown the presence of APOE-e4 to be associated with poorer functional recovery (Teasdale et al., 1997; Friedman et al., 1999; Ost et al., 2008). Other studies, however, show no clear association between presence of the APOE-e4 allele and outcome in patients with severe (Olivecrona et al., 2010) or mild to moderate TBI (Pruthi et al., 2010). In an observational study on 1094 patients with TBI, Teasdale et al. (2005) could not confirm earlier findings of an overall association between the presence of APO-e4 and poorer outcome. However, an interaction between age and APOe4 genotype was found, suggesting that the effect of APO-e4 genotype on outcome after head injury may be expressed through processes of repair and recovery. In a meta-analysis of 14 cohort studies (n ¼ 2427), Zhou et al. found that the APO-e4 genotype is not associated

466 A.I.R. MAAS ET AL. with the initial injury severity, but that the presence of the values will indeed lead to a better outcome. Some labogene does increase the risk of poor clinical outcome at ratory variables routinely measured on admission are 6 months after injury (relative risk 1.36, 95% CI associated with outcome following TBI. The greatest 1.04–1.78) (Zhou et al., 2008). Preliminary evidence furprognostic effects exist for high glucose concentrations, ther points to a possible association of P53, interleukin low hemoglobin, low platelets, and coagulation disturgenes, COMT, DND2, and CACNA 1A to poorer outbances (Lannoo et al., 2000; Rovlias and Kotsou, 2001; come following TBI (Dardiotis et al., 2010). Several studVan Beek et al., 2007; Saggar et al., 2009; Helmy ies report genetic associations with TBI outcome, but et al., 2010). their relevance is often limited by small sample sizes Several putative serum, cerebrospinal fluid (CSF), (Dardiotis et al., 2010). Detection and quantification and microdialysis biomarkers have been evaluated in of the association between genetic profile and outcome clinical studies of TBI. The oldest and most researched requires large patient numbers and to date these have not concern S-100B and neuron-specific enolase (NSE) been accumulated in a single large database for TBI. We (Sawauchi et al., 2005; Naeimi et al., 2006; Nyle´n consider it a priority that this be established, not only to et al., 2008; Beaudeux, 2009; Rainey et al., 2009; determine the relation between genetic profile and outSchultke et al., 2009). Many studies have demonstrated come but also as genetic profile may be related to risk an association between S-100B (Naeimi et al., 2006; for lesion progression or development of edema followWang and Zhang, 2006; Korfias et al., 2007; Egeaing TBI. These insights can be the basis for selection of Guerrero et al., 2012) and NSE (Naeimi et al., 2006; patients who are likely to respond to novel targeted Wang and Zhang, 2006; Guzel et al., 2008; Meric therapies. et al., 2010) with a greater severity of injuries (e.g., by comparing severe patients versus those with milder injuries). Also many studies have demonstrated an associaLABORATORY VALUES AND BIOMARKERS tion between higher serum levels and poorer outcome, The prognostic value of laboratory values in TBI has both for NSE (Wang and Zhang, 2006; Berger et al., been underappreciated for a long time. This may be con2007; Lo et al., 2011) and S-100B. In a systematic review, sidered surprising as many laboratory determinations Turgeon et al. (2009) identified 26 studies investigating are routinely performed and measured values are objecthe association between S-100B levels on admission and tive. Interest in prognostic value of laboratory assays has outcome. In a meta-analysis of these studies they conincreased considerably over the past 5–10 years with the firmed the association between S-100B and poorer outidentification of biomarkers considered more specific come, but threshold values varied considerably. More for neuronal or glial cell damage. These biomarkers in recent studies have further confirmed, mostly in univarparticular may provide opportunities for diagnosis, for iate analyses, prognostic effects of S-100B levels tracking of a disease process, and may possibly function (Murillo-Cabezas et al., 2010; Vos et al., 2010; as early end points for evaluation of neuroprotective Gonzales-Mao et al., 2011; Krnjak et al., 2011). Few studagents and strategies. Moreover, the underlying pathoies, however, have utilized multivariable analysis (Lesko, physiologic processes that are measured by these bio2010; Vos et al., 2010; Lo et al., 2011). Most of these studmarkers might be a target for novel therapeutic ies have been relatively small and as a consequence the interventions. Some laboratory parameters may mainly added value of these biomarkers over more traditional reflect the degree of injury (Margulies et al., 1994); predictors has not been convincingly shown. Moreover, abnormalities in other parameters may induce further we have come to recognize that these biomarkers are not damage or delay recovery process. For example, coaguspecific to damage of the central nervous system. The lopathy may cause more rapid increase of contusional past 5 years have witnessed an emerging field of novel lesions, hyperglycemia may aggravate pathophysiologic biomarkers considered more specific to neuronal or glial pathways (Zou et al., 2002), and hyponatremia may cell damage (see also Ch. 16). Particular interest has enhance cerebral edema. In the interpretation of progfocused on glial fibrillar acidic products (GFAP), nostic effects of laboratory parameters, the question UCH-L1, and spectrin breakdown products (Mondello of causality is highly pertinent. Although from a clinical et al., 2014). aII-Spectrin breakdown products have perspective a desire may exist to correct abnormal labomainly been investigated in CSF, but preliminary eviratory values, specifically if these parameters are related dence shows GFAP and UCH-L1 to have potential as to poorer outcome, it should be recognized that this may serum biomarkers. Various relatively small studies have not by definition improve outcome; the observed abnorshown these markers to be useful in the diagnosis of mality may be little more than a surrogate marker of brain injury, with higher levels found in patients with the severity of injury. Randomized controlled trials are more severe injuries (Lumpkins et al., 2008; Hayes thus required to establish if correction of abnormal et al., 2011), and have also demonstrated an association

PREDICTING OUTCOME AFTER TRAUMATIC BRAIN INJURY between higher values and poorer outcome (Nyle´n et al., 2006; Berger et al., 2010, Vos et al., 2010; Czeiter et al., 2011, Fraser et al., 2011; Mondello et al., 2012). Most of these studies have focused on a univariate approach to analysis. Results from a relatively small study (n ¼ 45) suggest a possible added predictive value of GFAP and UCH-L1 over a model of clinical predictors (Czeiter et al., 2011). The added prognostic values of these biomarkers in relation to other clinical predictors still needs to be quantified. The same laboratory determinations that have prognostic value on admission (e.g., glucose, platelets, and coagulation disturbances) are also relevant during the clinical course. Persistently high glucose concentrations are associated with poorer outcome even after adjustment for important predictors (Lannoo et al., 2000; Rovlias and Kotsous, 2000; Andrews et al., 2002; Salim et al., 2009).

ADVANCED MR IMAGING TECHNIQUES Advanced MR imaging techniques with potential prognostic relevance in TBI include susceptibility-weighted imaging (SWI), diffusion tensor imaging (DTI), and MR spectroscopy (see also Chs 17 and 19). SWI is a high resolution MR imaging sequence that is more sensitive in detecting small hemorrhagic lesions than conventional imaging; it is therefore particularly useful for the detection and quantification of small punctate lesions as seen in diffuse axonal injury (DAI). Various studies have demonstrated a high sensitivity for detecting DAI lesions (Ashwal et al., 2006; Chastain et al., 2009; Geurts et al., 2009; Beauchamp et al., 2011). Its prognostic value is, however, unclear: the absence of lesions on SWI has been reported to be associated with better outcome (Ashwal et al., 2009), but other studies did not find a clear association between lesions visualized on SWI and outcome (Chastain et al., 2009). DTI is a powerful technique to examine white matter integrity and to explore connectivity. Most studies on DTI have been limited to relatively small numbers. These studies have demonstrated accurate visualization of abnormalities consistent with traumatic axonal injury, and DT imaging is particularly useful in the diagnosis of milder injuries, including blast TBI (Paliotta et al., 2009; Mac Donald et al., 2011). Prognostic studies on tensor imaging have primarily focused on the prediction of recovery from coma in severely injured patients remaining unconscious (Perlbarg et al., 2009; Tollard et al., 2009; Kis et al., 2011). Further, an association between abnormalities on tensor imaging and cognitive disturbances has been demonstrated (Kumar et al., 2009; Matsushita et al., 2011).

467

MR spectroscopy permits semiquantitative detection of metabolites in regions of the brain. In TBI interest has mainly focused on N-acetylaspartate (NAA), choline (Cho), and their ratio to creatinine and lactate. Belli et al. (2006) have shown higher levels of extracellular NAA in nonsurvivors and conclude that extracellular NAA may be a potential marker for monitoring interventions aimed at preserving mitochondrial function. A strong correlation between reduced NAA/Cr and NAA/Cho ratios as well as elevated lactate levels with poorer outcome has been demonstrated in infants (Holshouser et al., 2009) and in adults (Signoretti et al., 2008). Despite these encouraging results, the added value of advanced MR imaging for prognostic purposes is uncertain. Nearly all the studies reported have been performed more than 1 week after injury, often even much later. Advanced MR studies can seldom be performed in the acute situation, mainly for logistical reasons. Further, the lack of current standardization of acquisition protocols and postacquisition processing may substantially confound comparisons between different studies. Finally, the number of patients studied in the publications is too small to permit definitive conclusions. However, the use of these advanced MR imaging techniques offers great potential for better tracking of disease processes, for use as early end points in clinical studies, and has the potential to add to prognostic information.

Clinical course Following admission, details on the clinical course continuously become available and these can add to more accurate prognostic estimates. This additional information may relate to changes in admission variables, to new events occurring, such as second insults, or to results of clinical, radiologic, and electrophysiologic monitoring. Any clinical or radiologic improvement or deterioration is strongly associated with respectively better or poorer prognosis. In particular, deterioration in the GCS and progression of abnormalities on repeated CT examinations are associated with poorer outcome. In comparison to the vast body of studies that have examined the association between predictors available upon admission and outcome, relatively few have focused on the additional value of including more detailed clinical assessments in prognostic estimates. In more severely injured patients invasive monitoring in the intensive care unit can provide much information. Many studies have shown an association of high intracranial pressure, low cerebral perfusion pressure, and decreased brain oxygen tension with poorer outcome. These associations, together with our understanding of pathophysiologic consequences, form the main rationale for guideline

468

A.I.R. MAAS ET AL.

recommendations to avoid high intracranial pressure and low cerebral perfusion pressure. Interpretation of the many studies on clinical monitoring is often difficult as the time of initiation and duration of monitoring varies greatly between and within studies. Moreover, different summary measures are used in analysis, such as the highest, lowest, mean values obtained during monitoring, or the number of episodes or percentage of time that values are above or below predefined thresholds. A recent study (Hartings et al., 2011) has shown a clear association between the occurrence of spreading depressions and poorer outcome in patient with TBI. Spreading depressions can be monitored by electrocortcography. Despite the strong association demonstrated in this study, interpretation of these findings should be approached with some caution. First, the patient population studied was restricted to patients undergoing surgical treatment – hence results cannot be extrapolated toward the larger group of patients with diffuse injuries. Second, evolution of the clinical condition was not taken into account in the multivariable analysis so that no strong conclusion can be made concerning the independent added predictive value of spreading depressions. Nevertheless, these results are highly intriguing, particularly as they may also open opportunities for therapeutic intervention.

PROGNOSTIC MODELS Prognostic models combine information from different predictors into a mathematical equation to predict outcome in individual patients. Seven distinct steps can be identified in the development of valid prediction models with regression analysis (Table 29.8).These steps illustrate the complexity involved in the development of valid prognostic models for TBI. They further illustrate that in

addition to focusing on the development phase, particular attention is required to assess performance and model validity.

Available models for traumatic brain injury A recent review article (Lingsma et al., 2010) identified 27 prognostic models reported in 16 studies meeting the following criteria: outcome measures: mortality more than 2 weeks after discharge or 6 months GOS predictors measured within 24 hours after injury inclusion of more than 200 patients 14 years of age GCS  13 or GCS motor score  5 nonpenetrating injury. Two independently conducted systematic reviews have reported shortcomings in the majority of these studies (Perel et al., 2006; Mushkudiani et al., 2008). Problems identified included, in particular, a high risk of overfitting and lack of external validation. Overfitting relates to the phenomenon that predictive performance may be much poorer in new patients than expected from the development population. A main cause of this is including too many predictors in the final model, as may occur if the selection of predictors is data-driven in a small dataset. Overfitting can be assessed by internal validation techniques such as bootstrap resampling as well as by external validation. Two prediction models have been published which were developed on large patients series and externally validated: a model presented by the MRC CRASH Trial Collaborators and a prediction model proposed by the IMPACT study group (MRC CRASH Trial Collaborators, 2008; Steyerberg et al., 2008). The CRASH and IMPACT models are available on the Internet

Table 29.8 The seven steps in the development of a valid prediction model Topic

Action

Problem definition and data inspection Coding of predictors Model specification Model estimation Model performance Model validity Model presentation

Define research questions, outcome of interest and explore availability of potential predictors Recode predictors into a common format if required Specify predictors to be included in the model Estimation of model parameters Performance measures include calibration and discrimination* Determine internal and external validity Regression formulas, score charts, nomograms or web-based calculators

*

Calibration refers to the reliability of predictions: if we predict 10%, on average 10% of the subjects with this prediction are expected to have the outcome of interest. Discrimination refers to the ability of a model to separate subjects with and without the outcome and can be quantified by the C-statistic or the area under the receiver operating characteristic (ROC) curve. This curve shows the relationship between sensitivity and specificity. Overall model performance measures include the R2 and the Brier score.

PREDICTING OUTCOME AFTER TRAUMATIC BRAIN INJURY (www.crash.lshtm.ac.uk; www.tbi-impact.org). Both studies showed that the largest amount of prognostic information is contained in a core set of three predictors (age, GCS or motor score, and pupillary reactivity). A summary overview of characteristics of these two models is presented in Table 29.9. The CRASH and IMPACT models were initially reciprocally validated externally on the other datasets and this validation confirmed a good performance. Various studies have since then reported external validation of the IMPACT models (Yeoman et al., 2011; Panczykowski et al., 2012; Roozenbeek et al., 2012a, b).

469

interpretation of the predicted probability might become even more complicated. The uncertainty in prognostic estimates warrants caution, in particular when prognostic estimates may be used to guide resource allocation or decisions on treatment. We would not advocate basing clinical decisions solely on prognostic estimates. Rather, such estimates may be seen as an instrument which may be used to support and check clinical decision making. At the group level, prognostic models may be used for a number of purposes:

Applications of prognostic models

characterization of patient populations and classification by predictive risk clinical trial design and analysis as reference for assessing quality of care.

Prognostic models are becoming an increasingly important instrument for use in the clinical situation. Just as a younger resident needs to learn the use of surgical instruments, so do clinicians need to learn how to use prognostic models. In general, prognostic models can be applied at the level of the individual or at the “group” level. At the level of the individual, prognostic models can be used for purposes of providing information to relatives, for resource allocation, to guide and support decisions on treatment, and they may also be seen as a possible incentive to clinicians to “beat” the estimate. When applying prognostic models at the level of an individual patient we should always recognize that prognostic estimates reflect no more than a statistical probability, while the patient will either experience the outcome of interest or not. In addition the estimate probability itself carries uncertainty. This uncertainty can be quantified by calculating the confidence interval around the estimate. Whether confidence intervals should be presented with model predictions is debated. While a confidence interval shows the degree of uncertainty,

Prognostic risk estimation at hospital admission enables characterization and classification of populations according to their prognostic risk distribution. Such characterization provides an integrated insight into variation in the case mix of different studies, thus permitting a better comparison of populations. In the research field, prognostic models offer opportunities both in the enrolment and analysis phase of clinical trials. A particular problem in trials on TBI is the inherent heterogeneity of the patient population (see also Ch. 47). Traditionally, most trials have used relatively strict enrolment criteria in order to decrease this heterogeneity. Simulation studies performed by the IMPACT study group have, however, showed that this is statistically inefficient and recommend relatively broad enrolment criteria with adjustment for the heterogeneity in the analysis phase by covariate adjustment. In contrast to strict enrolment criteria on admission, this approach substantially increases statistical power, reducing the required sample size by approximately 25%. Prognostic models have other important applications in the analysis phase of clinical trials and are essential when use of

Table 29.9 Comparison of CRASH and IMPACT prediction models

IMPACT

CRASH

Predicted outcome

Core model

CT model

Laboratory model

Mortality or unfavorable outcome at 6 months Mortality at 14 days or unfavorable outcome at 6 months

Age, motor score, pupil reactivity

Core model plus: hypoxia, hypotension, CT classification, traumatic subarachnoid hemorrhage on CT, epidural mass on CT

Core model plus: glucose and hemoglobin concentrations

Age, GCS score, pupil reactivity, major extracranial injury

Core model plus: petechial hemorrhages, obliteration of the third ventricle or basal cisterns, subarachnoid bleeding, midline shift, nonevacuated hematoma

CRASH, Corticosteroid Randomisation After Significant Head Injury; GCS, Glasgow Coma Scale; IMPACT, International Mission for Prognosis and Clinical Trial design in TBI; TBI, traumatic brain injury. (Data from MRC CRASH Trial Collaborators, 2008, and Steyerberg et al., 2008.)

470

A.I.R. MAAS ET AL.

sliding dichotomy is considered. The sliding dichotomy was first introduced by Barer (1998) in the field of stroke and the concept in TBI was taken forward by Murray et al. (2005). The traditional approach to analysis of functional outcome after TBI is to dichotomize the GOS at a fixed point into two categories, unfavorable versus favorable. This approach causes loss of information and reduces sensitivity of the analysis; in the sliding dichotomy approach, the point of dichotomy is differentiated according to the baseline prognostic risk. For a patient with a very severe injury, for example, survival may be a relevant end point, whereas for patients with less severe injuries any outcome worse than good recovery might be considered unfavorable. In other words, the analysis is focused on detecting if a patient does better than expected. Simulation studies performed by the IMPACT study group have showed that an ordinal approach to analysis of the GOS, either by applying a proportional odds methodology or by using the sliding dichotomy, is highly efficient in the analysis phase and can increase statistical power by up to 50%. Thus, prognostic models have become an important tool in the context of clinical trials on heterogeneous populations such as TBI patients. An important potential application of prognostic models is for benchmarking the quality of healthcare delivery (Lingsma et al., 2010). In quality assessment, the observed outcomes are compared to expected outcomes. Prognostic models that are specific to TBI are essential for setting baselines for clinical audience and benchmarking. We see a great potential of prognostic models for assessing the quality of healthcare delivery, specifically also because they have been developed not only for mortality but also for functional outcome as assessed by the GOS. It should be recognized, however, that the cumulative R2 of the IMPACT models amounts to 0.35, indicating that 65%, of the variation is unexplained and case mix adjustment is likely to be incomplete.

CONCLUSION AND FUTURE DIRECTIONS Recognition of the importance of prognosis in TBI dates back to ancient Greek times. For a long period of time, however, prognostic estimates were little more than prophecies. Advances in statistical modeling and the availability of large datasets have facilitated prognostic analysis and the development of validated prognostic models with good generalizability. Multivariable analysis has identified age, clinical severity, structural abnormalities as visualized by CT scanning, second insults, and biomarkers as relevant factors to include in models to predict outcome in individual patients. The past decade has witnessed an exponential increase in prognostic research in TBI. Prediction models are currently available which have been developed on large datasets

with state-of-the-art methods. These models offer new opportunities and should be considered an important instrument in clinical decision making and research. We further see a great potential for their use toward assessment of the quality of healthcare delivery. Continued development, refinement, and validation of prognostic models for TBI is required and should become an ongoing process.

REFERENCES AAAM (1990). The Abbreviated Injury Scale, 1990 revision. Association for the Advancement of Automotive Medicine, 15–24, Des Plaines, IL. Aarabi B, Alden TD, Chestnut RM et al. (2001). Management and prognosis of penetrating brain injury. J Trauma 51 (Suppl.): S1–S86. Andrews PJ, Sleeman DH, Statham PF et al. (2002). Predicting recovery in patients suffering from traumatic brain injury by using admission variables and physiological data: a comparison between decision tree analysis and logistic regression. J Neurosurg 97: 326–336. Arango-Lasprilla JC, Rosenthal M, Deluca J et al. (2007). Traumatic brain injury and functional outcomes: does minority status matter? Brain Inj 21: 701–708. Ashwal S, Babikian T, Gardner-Nichols J et al. (2006). Susceptibility-weighted imaging and proton magnetic resonance spectroscopy in assessment of outcome after pediatric traumatic brain injury. Arch Phys Med Rehabil 87 (Suppl. 2): S50–S58. Ashwal S, Cobert C, Aaen G et al. (2009). Prevalence of cerebral microhemorrhage on susceptibility weighted imaging and correlation with long term outcome in pediatric non-accidental trauma. Eur J Paediatr Neurol 13: S87. http://dx.doi.org/10.1016/S1090-3798(09)70272-7. Bahloul M, Chelly H, Ben Hmida M et al. (2004). Prognosis of traumatic head injury in South Tunisia: a multivariate analysis of 437 cases. J Trauma 57: 255–261. Baker SP, O’Neill B, Haddon Jr W et al. (1974). The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. J Trauma 14: 187–196. Barer D (1998). Could stroke mega-trials be missing important treatment effects? Cerebrovasc Dis (Suppl. 4): 47. Barlow P, Teasdale G (1986). Prediction of outcome and the management of severe head injuries: the attitudes of neurosurgeons. Neurosurgery 19: 989–991. Beauchamp MH, Ditchfield M, Babl FE et al. (2011). Detecting traumatic brain lesions in children: CT versus MRI versus susceptibility weighted imaging (SWI). J Neurotrauma 28: 915–927. Beaudeux JL (2009). S100B protein: a novel biomarker for the diagnosis of head injury [in French]. Ann Pharm Fr 67: 187–194. Belli A, Sen J, Petzold A et al. (2006). Extracellular N-acetylaspartate depletion in traumatic brain injury. J Neurochem 96: 861–869. Berger RP, Beers SR, Richichi R et al. (2007). Serum biomarker concentrations and outcome after pediatric traumatic brain injury. J Neurotrauma 24: 1793–1801.

PREDICTING OUTCOME AFTER TRAUMATIC BRAIN INJURY Berger RP, Bazaco MC, Wagner AK et al. (2010). Trajectory analysis of serum biomarker concentrations facilitates outcome prediction after pediatric traumatic and hypoxemic brain injury. Dev Neurosci 32: 396–405. Brain Trauma Foundation (2001). Guidelines for the management and prognosis of severe traumatic brain injury. J Neurotrauma 51 (Suppl. 2): S1–S86. Bukur M, Kurtovic S, Berry C et al. (2012). Pre-hospital hypothermia is not associated with increased survival after traumatic brain injury. J Surg Res 175: 24–29. Butcher I, Maas AI, Lu J et al. (2007). Prognostic value of admission blood pressure in traumatic brain injury; results from the IMPACT study. J Neurotrauma 24: 294–302. Chang RWS, Lee B, Jacobs S (1989). Accuracy of decisions to withdraw therapy in critically ill patients: clinical judgment versus a computer model. Crit Care Med 17: 1091–1097. Chastain CA, Oyoyo UE, Zipperman M et al. (2009). Predicting outcomes of traumatic brain injury by imaging modality and injury distribution. J Neurotrauma 26: 1183–1196. Chesnut RM (1995). Secondary brain insults after head injury: clinical perspectives. New Horiz 3: 366–375. Chun KA, Manley GT, Stiver SI et al. (2010). Interobserver variability in the assessment of CT imaging features of traumatic brain injury. J Neurotrauma 27: 325–330. Czeiter E, Mondello S, Kovacs N et al. (2011). Brain injury biomarkers may improve the predictive power of the IMPACT outcome calculator. Acta Neurochir 153: 1882. Dardiotis E, Fountas KN, Dardioti M et al. (2010). Genetic association studies in patients with traumatic brain injury. Neurosurg Focus 28: E9. Dawes RM, Faust D, Meehl RE (1989). Clinical versus actuarial judgment. Science 243: 1668–1674. Demetriades D, Murray J, Martin M et al. (2004). Pedestrians injured by automobiles: relationship of age to injury type and severity. J Am Coll Surg 199: 382–387. Egea-Guerrero JJ, Revuelto-Rey J, Murillo-Cabezas F et al. (2012). Accuracy of the S100b protein as a marker of brain damage in traumatic brain injury. Brain Inj 26: 76–82. Farace E, Alves WM (2000). Do women fare worse? A metaanalysis of gender differences in outcome after traumatic brain injury. Neurosurg Focus 8: e6. Fraser DD, Close TE, Rose KL et al. (2011). Severe traumatic brain injury in children elevates glial fibrillary acidic protein in cerebrospinal fluid and serum. Pediatr Crit Care Med 12: 319–324. Friedman G, Froom P, Sazbon L et al. (1999). Apolipoprotein E-epsilon4 genotype predicts a poor outcome in survivors of traumatic brain injury. Neurology 52: 244–248. Gabbe BJ, Simpson PM, Sutherland AM et al. (2008). Functional measures at discharge: are they useful predictors of longer term outcomes for trauma registries? Ann Surg 247: 854–859. Galanaud D, Perlbarg V, Gupta R et al., Neuro Imaging for Coma Emergence and Recovery Consortium (2012). Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology 117: 1300–1310. Geurts BH, Andriessen TM, Vos PE et al. (2009). Magnetic resonance imaging in traumatic brain injury: a comparison

471

between MR sequences in inter-rater reliability, lesion detection and relationship with outcome. Neuroradiology 51: S104. Gomez PA, Lobato RD, Boto GR et al. (2000). Age and outcome after severe head injury. Acta Neurochir (Wien) 142: 373–380, discussion 380–381. Guzel A, Er U, Tatli M et al. (2008). Serum neuron-specific enolase as a predictor of short-term outcome and its correlation with Glasgow Coma Scale in traumatic brain injury. Neurosurg Rev 31: 439–444, discussion 444–445. Hartings JA, Bullock MR, Okonkwo DO et al. (2011). Spreading depolarisations and outcome after traumatic brain injury: a prospective observational study. Lancet Neurol 10: 1058–1064. Hayes R, Mondello S, Wang K (2011). Clinical studies of the utility of serum biomarkers for the diagnosis, prognosis, and management of traumatic brain injury. J Neurotrauma 28: A24. Heinzelmann M, Platz A, Imhof HG (1996). Outcome after acute extradural haematoma, influence of additional injuries and neurological complications in the ICU. Injury 27: 345–349. Helmy A, Timofeev I, Palmer CR et al. (2010). Hierarchical log linear analysis of admission blood parameters and clinical outcome following traumatic brain injury. Acta Neurochir (Wien) 152: 953–957. Ho KM, Burrell M, Rao S et al. (2010). Extracranial injuries are important in determining mortality of neurotrauma. Crit Care Med 38: 1562–1568. Holshouser B, Aaen G, Colbert C et al. (2009). Lactate and decreased NAA predict longterm outcome better than presence of microhemorrhages seen with susceptibility weighted imaging (SWI) after non-accidental trauma (NAT). J Neurotrauma 26: A15. Hukkelhoven CW, Steyerberg EW, Rampen AJ et al. (2003). Patient age and outcome following severe traumatic brain injury: an analysis of 5600 patients. J Neurosurg 99: 666–673. Jacobs B, Beems T, Stulemeijer M et al. (2010). Outcome prediction in mild traumatic brain injury: age and clinical variables are stronger predictors than CT abnormalities. J Neurotrauma 27: 655–668. Jennett B, Bond M (1975). Assessment of outcome after severe brain damage. Lancet 1: 480–484. Jennett B, Teasdale G, Braakman R et al. (1976). Predicting outcome in individual patients after severe head injury. Lancet 1: 1031–1034. Kaufmann MA, Buchmann B, Scheidegger D et al. (1992). Severe head injury: should expected outcome influence resuscitation and first-day decisions? Resuscitation 23: 199–206. Kis D, Mencser Z, Czigner A et al. (2011). Prediction of long term outcome in severe traumatic brain injury using diffusion tensor imaging and probabilistic tractography. Acta Neurochir 153: 1883. Korfias S, Stranjalis G, Boviatsis E et al. (2007). Serum S-100B protein monitoring in patients with severe traumatic brain injury. Intensive Care Med 33: 255–260. Krnjak L, Gradisˇek P, Herman S et al. (2011). High concentrations protein s100b in serum predicts mortality after traumatic brain injury. Eur J Pharm Sci 44: 58–59.

472

A.I.R. MAAS ET AL.

Kumar R, Husain M, Gupta RK et al. (2009). Serial changes in the white matter diffusion tensor imaging metrics in moderate traumatic brain injury and correlation with neurocognitive function. J Neurotrauma 26: 481–495. Lannoo E, Van Rietvelde F, Colardyn F et al. (2000). Early predictors of mortality and morbidity after severe closed head injury. J Neurotrauma 17: 403–414. Lefering R, Paffrath T, Linker R et al. (2008). Head injury and outcome – what influence do concomitant injuries have? J Trauma 65: 1036–1043. Lesko M (2010). Comparing the prognostic performance of S100B with prognostic models in traumatic brain injury. Emerg Med J 27: A2. Lingsma HF, Roozenbeek B, Steyerberg EW et al. (2010). Early prognosis in traumatic brain injury: from prophecies to predictions. Lancet Neurol 9: 543–554. Little RJA, Rubin DB (2002). Statistical Analysis with Missing Data, 2nd edn. Wiley, Hoboken, NJ. Lo T, Jones P, Chambers I et al. (2011). Serum biomarkers and prediction of significantly deranged cerebral perfusion pressure (CPP) insult magnitude in childhood brain trauma. Pediatr Crit Care Med 12: A112. Lumpkins KM, Bochicchio GV, Keledjian K et al. (2008). Glial fibrillary acidic protein is highly correlated with brain injury. J Trauma 65: 778–782, discussion 782–784. Maas AI, Hukkelhoven CW, Marshall LF et al. (2005). Prediction of outcome in traumatic brain injury with computed tomographic characteristics: a comparison between the computed tomographic classification and combinations of computed tomographic predictors. Neurosurgery 57: 1173–1182. Maas AI, Marmarou A, Murray GD et al. (2007). Prognosis and clinical trial design in traumatic brain injury: the IMPACT study. J Neurotrauma 24: 232–238. Maas AIR, Engel DC, Lingsma H (2011). Prognosis after traumatic brain injury. In: RH Winn (Ed.), Youmans Neurological Surgery, 6th edn. Elsevier-Saunders, Philadelphia, USA, pp. 3497–3506. Mac Donald CL, Johnson AM, Cooper D et al. (2011). Detection of blast-related traumatic brain injury in U.S. military personnel. N Engl J Med 364: 2091–2100. Manley G, Knudson MM, Morabito D et al. (2001). Hypotension, hypoxia, and head injury: frequency, duration, and consequences. Arch Surg 136: 1118–1123. Margulies DR, Hiatt JR, Vinson D et al. (1994). Relationship of hyperglycemia and severity of illness to neurologic outcome in head injury patients. Am Surg 60: 387–390. Marmarou A, Lu J, Butcher I et al. (2007). Prognostic value of the Glasgow Coma Scale and pupil reactivity in traumatic brain injury assessed pre-hospital and on enrolment: an IMPACT analysis. J Neurotrauma 24: 270–280. Marshall LF, Bowers S, Klauber MR et al. (1991). A new classification of head injury based on computerised tomography. J Neurosurg 75 (Suppl.): S14–S20. Matsushita M, Hosoda K, Naitoh Y et al. (2011). Utility of diffusion tensor imaging in the acute stage of mild to moderate traumatic brain injury for detecting white matter lesions

and predicting long-term cognitive function in adults. J Neurosurg 115: 130–139. McHugh GS, Engel DC, Butcher I et al. (2007). Prognostic value of secondary insults in traumatic brain injury: results from the IMPACT study. J Neurotrauma 24: 287–293. Meric E, Gunduz A, Turedi S et al. (2010). The prognostic value of neuron-specific enolase in head trauma patients. J Emerg Med 38: 297–301. Mondello S, Linnet A, Buki A et al. (2012). Clinical utility of serum levels of ubiquitin C-terminal hydrolase as a biomarker for severe traumatic brain injury. Neurosurgery 70: 666–675. Mondello S, Schmid K, Berger RP et al. (2014). The challenge of mild traumatic brain injury: role of biochemical markers in diagnosis of brain damage. Med Res Rev 34: 503–531. MRC CRASH Trial Collaborators, Perel P, Arango M et al. (2008). Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients. BMJ 336: 425–429. Murillo-Cabezas F, Mun˜oz-Sa´nchez MA, Rinco´n-Ferrari MD et al. (2010). The prognostic value of the temporal course of S100beta protein in post-acute severe brain injury: a prospective and observational study. Brain Inj 24: 609–619. Murray GD, Barer D, Choi S et al. (2005). Design and analysis of phase III trials with ordered outcome scales: the concept of the sliding dichotomy. J Neurotrauma 22: 511–517. Murray GD, Butcher I, McHugh GS et al. (2007). Multivariate prognostic analysis in traumatic brain injury: results from the IMPACT study. J Neurotrauma 24: 329–337. Mushkudiani NA, Engel DC, Steyerberg EW et al. (2007). Prognostic value of demographic characteristics in traumatic brain injury: results from the IMPACT study. J Neurotrauma 24: 259–269. Mushkudiani NA, Hukkelhoven CW, Hernandez AV et al. (2008). A systematic review finds methodological improvements necessary for prognostic models in determining traumatic brain injury outcomes. J Clin Epidemiol 61: 331–343. Naeimi ZS, Weinhofer A, Sarahrudi K et al. (2006). Predictive value of S-100B protein and neuron specifi c-enolase as markers of traumatic brain damage in clinical use. Brain Inj 20: 463–468. Nyle´n K, Ost M, Csajbok LZ et al. (2006). Increased serumGFAP in patients with severe traumatic brain injury is related to outcome. J Neurol Sci 240: 85–91. Nyle´n K, Ost M, Csajbok LZ et al. (2008). Serum levels of S100B, S100A1B and S100BB are all related to outcome after severe traumatic brain injury. Acta Neurochir (Wien) 150: 221–227. Olivecrona M, Wildemyr Z, Koskinen LO (2010). The apolipoprotein E epsilon4 allele and outcome in severe traumatic brain injury treated by an intracranial pressuretargeted therapy. J Neurosurg 112: 1113–1119. Ono J, Yamaura A, Kubota M (2001). Outcome prediction in severe head injury: analyses of clinical prognostic factors. J Clin Neurosci 8: 120–123.

PREDICTING OUTCOME AFTER TRAUMATIC BRAIN INJURY Ost M, Nyle´n K, Csajbok L et al. (2008). Apolipoprotein E polymorphism and gender difference in outcome after severe traumatic brain injury. Acta Anaesthesiol Scand 52: 1364–1369. Paliotta C, Bakhadirov K, Marquez de la Plata C et al. (2009). Analyzing diffusion tensor imaging data in traumatic axonal injury: what is the best approach? J Neurotrauma 26: A36. Panczykowski DM, Puccio AM, Scruggs BJ et al. (2012). Prospective independent validation of impact modeling as a prognostic tool in severe traumatic brain injury. J Neurotrauma 29: 47–52. Perel P, Edwards P, Wentz R et al. (2006). Systematic review of prognostic models in traumatic brain injury. BMC Med Inform Decis Mak 6: 38. Perel P, Arango M, Clayton T et al. (2008). Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients. BMJ 336: 425–429. Perlbarg V, Puybasset L, Tollard E et al. (2009). Relation between brain lesion location and clinical outcome in patients with severe traumatic brain injury: a diffusion tensor imaging study using voxel-based approaches. Hum Brain Mapp 30: 3924–3933. Pruthi N, Chandramouli BA, Kuttappa TB et al. (2010). Apolipoprotein E polymorphism and outcome after mild to moderate traumatic brain injury: a study of patient population in India. Neurol India 58: 264–269. Rainey T, Lesko M, Sacho R et al. (2009). Predicting outcome after severe traumatic brain injury using the serum S100B biomarker: results using a single (24 h) time-point. Resuscitation 80: 341–345. Ratanalert S, Chompikul J, Hirunpat S (2002). Talked and deteriorated head injury patients: how many poor outcomes can be avoided? J Clin Neurosci 9: 640–643. Roozenbeek B, Chiu Y, Lingsma H et al. (2012a). Predicting 14-day mortality after severe traumatic brain injury: validity of the IMPACT models in the Brain Trauma Foundation TBI-track New York State database. J Neurotrauma 29: 1306–1312. Roozenbeek B, Lingsma H, Lecky F et al. (2012b). Prediction of outcome after moderate and severe traumatic brain injury: external validation of the IMPACT and CRASH prognostic models. Crit Care Med 40: 1609–1617. Rovlias A, Kotsou S (2000). The influence of hyperglycemia on neurological outcome in patients with severe head injury. Neurosurgery 46: 335–343. Rovlias A, Kotsou S (2001). The blood leukocyte count and its prognostic significance in severe head injury. Surg Neurol 55: 190–196. Royston P, Altman DG, Sauerbrei W (2006). Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med 25: 127–141. Saggar V, Mittal RS, Vyas MC (2009). Hemostatic abnormalities in patients with closed head injuries and their role in predicting early mortality. J Neurotrauma 26: 1665–1668. Salim A, Hadjizacharia P, Dubose J et al. (2009). Persistent hyperglycemia in severe traumatic brain injury: an independent predictor of outcome. Am Surg 75: 25–29.

473

Sarrafzadeh AS, Peltonen EE, Kaisers U et al. (2001). Secondary insults in severe head injury – do multiply injured patients do worse? Crit Care Med 29: 1116–1123. Sawauchi S, Taya K, Murakami S et al. (2005). Serum S-100B protein and neuron-specific enolase after traumatic brain injury [in Japanese]. No Shinkei Geka 33: 1073–1080. Schultke E, Sadanand V, Kelly ME et al. (2009). Can admission S-100b predict the extent of brain damage in head trauma patients? Can J Neurol Sci 36: 612–616. Shafi S, Marquez de la Plata C, Diaz-Arrastia R et al. (2007). Racial disparities in long-term functional outcome after traumatic brain injury. J Trauma 63: 1263–1270. Signoretti S, Marmarou A, Aygok GA et al. (2008). Assessment of mitochondrial impairment in traumatic brain injury using high-resolution proton magnetic resonance spectroscopy. J Neurosurg 108: 42–52. Signorini DF, Andrews PJ, Jones PA et al. (1999). Predicting survival using simple clinical variables: a case study in traumatic brain injury. J Neurol Neurosurg Psychiatry 66: 20–25. Sorani MD, Lee M, Kim H et al. (2009). Race\ethnicity and outcome after traumatic brain injury at a single, diverse center. J Trauma 67: 75–80. Steyerberg EW (2008). Clinical Prediction Models: a Practical Approach to Development, Validation, and Updating. Springer, New York. Steyerberg EW, Mushkudiani N, Perel P et al. (2008). Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Med 5: e165. Stocchetti N, Pagan F, Calappi E et al. (2004). Inaccurate early assessment of neurological severity in head injury, J Neurotrauma 21: 1131–1140. Teasdale G, Jennett B (1974). Assessment of coma and impaired consciousness. A practical scale. Lancet 2: 81–84. Teasdale GM, Nicoll JA, Murray G et al. (1997). Association of apolipoprotein E polymorphism with outcome after head injury. Lancet 350: 1069–1071. Teasdale GM, Murray GD, Nicoll JA (2005). The association between APOE epsilon4, age and outcome after head injury: a prospective cohort study. Brain 128: 2556–2561. Tokutomi T, Miyagi T, Ogawa T et al. (2008). Age-associated increases in poor outcomes after traumatic brain injury: a report from the Japan Neurotrauma Data Bank. J Neurotrauma 25: 1407–1414. Tollard E, Galanaud D, Perlbarg V et al. (2009). Experience of diffusion tensor imaging and 1H spectroscopy for outcome prediction in severe traumatic brain injury: preliminary results. Crit Care Med 37: 1448–1455. Truelle J, Koskinen S, Hawthorne G et al. (2010). Quality of life after traumatic brain injury: the clinical use of the QOLIBRI, a novel disease-specific instrument. Brain Inj 24: 1272–1291. Turgeon A, Mercier E, Simard JF et al. (2009). Predictive value of s-100(beta) protein for prognosis in patients with moderate and severe TBI: a systematic review and metaanalysis. Crit Care Med 37: A313.

474

A.I.R. MAAS ET AL.

Vach W, Blettner M (1991). Biased estimation of the odds ratio in case-control studies due to the use of ad hoc methods of correcting for missing values for confounding variables. Am J Epidemiol 134: 895–907. Van Beek JG, Mushkudiani NA, Steyerberg EW et al. (2007). Prognostic value of admission laboratory parameters in traumatic brain injury: results from the IMPACT study. J Neurotrauma 24: 315–328. van Leeuwen N, Lingsma HF, Perel P et al. (2012). Prognostic value of major extracranial injury in traumatic brain injury: an individual patient data meta-analysis in 39,274 patients. Neurosurgery 70: 811–818, discussion 818. von Steinbuechel N, Wilson L, Gibbons H et al. (2010a). Quality of Life after Brain Injury (QOLIBRI) – scale development and metric properties. J Neurotrauma 27: 1167–1185. von Steinbuechel N, Wilson L, Gibbons H et al. (2010b). Quality of Life after Brain Injury (QOLIBRI) – scale validity and correlates of quality of life. J Neurotrauma 27: 1157–1165. von Steinbuechel N, Wilson L, Gibbons H et al. (2012). QOLIBRI overall scale: a brief index of health-related quality of life after traumatic brain injury. J Neurol Neurosurg Psychiatry 83: 1041–1047. Vos PE, Jacobs B, Andriessen TM et al. (2010). GFAP and S100B are biomarkers of traumatic brain injury: an observational cohort study. Neurology 75: 1786–1793. Wang XH, Zhang XD (2006). Evaluating the prognosis and degree of brain injury by combined S-100 protein and neuron specific enolase determination. Neural Regen Res 1: 649–652. Wilde EA, Whiteneck GG, Bogner J et al. (2010). Recommendations for the use of common outcome

measures in traumatic brain injury research. Arch Phys Med Rehabil 91: 1650–1660. Wilson JTL, Pettigrew LEL, Teasdale GM (1998). Structured interviews for the Glasgow Outcome Scale and the extended Glasgow Outcome Scale: guidelines for their use. J Neurotrauma 15: 573–585. Yeoman P, Pattani H, Silcocks P et al. (2011). Validation of the IMPACT outcome prediction score using the Nottingham Head Injury Register dataset. J Trauma 71: 387–392. Yuh EL, Cooper SR, Ferguson AR et al. (2012). Quantitative CT improves outcome prediction in acute traumatic brain injury. J Neurotrauma 29: 735–746. Yuh EL, Mukherjee P, Lingsma HF et al., TRACK-TBI Investigators (2013). Magnetic resonance imaging improves 3-month outcome prediction in mild traumatic brain injury. Ann Neurol 73: 224–235. Zhou W, Xu D, Peng X et al. (2008). Meta-analysis of APOE4 allele and outcome after traumatic brain injury. J Neurotrauma 25: 279–290. Zou MH, Shi C, Cohen RA (2002). High glucose via peroxynitrite causes tyrosine nitration and inactivation of prostacyclin synthase that is associated with thromboxane/ prostaglandin H2 receptor-mediated apoptosis and adhesion molecule expression in cultured human aortic endothelial cells. Diabetes 51: 198–203.

WEBSITES www.crash.lshtm.ac.uk www.tbiguidelines.org. www.tbi-impact.org.