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Objective: The International Mission on Prognosis and Analysis of Clinical Trials and Corticoid Randomisation After Significant Head injury prognostic models predict outcome after traumatic brain injury but have not been compared in large datasets. The objective of this is study is to validate externally and compare the International Mission on Prognosis and Analysis of Clinical Trials and Corticoid Randomisation after Significant Head injury prognostic models for prediction of outcome after moderate or severe traumatic brain injury. Design: External validation study. Patients: We considered five new datasets with a total of 9,036 patients, comprising three randomized trials and two observational series, containing prospectively collected individual traumatic brain injury patient data. Measurements and Main Results: Outcomes were mortality and unfavorable outcome, based on the Glasgow Outcome Score at 6 months after injury. To assess performance, we studied the discrimination of the models (by area under the receiver operating characteristic curves), and calibration (by comparison of the mean observed to predicted outcomes and calibration slopes). The highest discrimination was found in the Trauma Audit and Research Network trauma registry (area under the receiver operating characteristic curves between 0.83 and 0.87), and the lowest discrimination in the Pharmos trial (area under the receiver operating characteristic curves between 0.65 and 0.71). Although differences in predictor effects between development and validation populations were found (calibration slopes varying between 0.58 and 1.53), the differences in discrimination were largely explained by differences in case mix in the validation studies. Calibration was good, the fraction of observed outcomes generally agreed well with the mean predicted outcome. No meaningful differences were noted in performance between the International Mission on Prognosis and Analysis of Clinical Trials and Corticoid Randomisation After Significant Head injury models. More complex models discriminated slightly better than simpler variants. Conclusions: Since both the International Mission on Prognosis and Analysis of Clinical Trials and the Corticoid Randomisation After Significant Head injury prognostic models show good generalizability to more recent data, they are valid instruments to quantify prognosis in traumatic brain injury. (Crit Care Med 2012; 40: 1609-1617)

The Glasgow Outcome Scale (GOS) is firmly established as the primary outcome measure for use in Phase III trials of interventions in traumatic brain injury (TBI). However, the GOS has been criticized for its lack of sensitivity to detect small but clinically relevant changes in outcome. The Glasgow Outcome Scale-Extended (GOSE) potentially addresses this criticism, and in this study we estimate the efficiency gain associated with using the GOSE in place of the GOS in ordinal analysis of 6-month outcome. The study uses both simulation and the reanalysis of existing data from two completed TBI studies, one an observational cohort study and the other a randomized controlled trial. As expected, the results show that using an ordinal technique to analyze the GOS gives a substantial gain in efficiency relative to the conventional analysis, which collapses the GOS onto a binary scale (favorable versus unfavorable outcome). We also found that using the GOSE gave a modest but consistent increase in efficiency relative to the GOS in both studies, corresponding to a reduction in the required sample size of the order of 3-5%. We recommend that the GOSE be used in place of the GOS as the primary outcome measure in trials of TBI, with an appropriate ordinal approach being taken to the statistical analysis.

Background Clinical trials in traumatic brain injury have a disappointing track record, with a long history of ‘negative’ Phase III trials. One contributor to this lack of success is almost certainly the low efficiency of the conventional approach to the analysis, which discards information by dichotomizing an ordinal outcome scale. Purpose Our goal was to evaluate the potential efficiency gains, which can be achieved by using techniques, which extract additional information from ordinal outcome data — the proportional odds model and the sliding dichotomy. In addition, we evaluated the additional efficiency gains, which can be achieved through covariate adjustment. Methods The study was based on simulations, which were built around a database of patient-level data extracted from eight Phase III trials and three observational studies in traumatic brain injury. Two different putative treatment effects were explored, one which followed the proportional odds model, and the other which assumed that the effect of the intervention was to reduce the risk of death without changing the distribution of outcomes within survivors. The results are expressed as efficiency gains, reported as the percentage reduction in sample size that can be used with the ordinal analyses without loss of statistical power relative to the conventional binary analysis. Results The simulation results show substantial efficiency gains. Use of the sliding dichotomy allows sample sizes to be reduced by up to 40% without loss of statistical power. The proportional odds model gives modest additional gains over and above the gains achieved by use of the sliding dichotomy. Limitations As with any simulation study, it is difficult to know how far the findings may be extrapolated beyond the actual situations that were modeled. Conclusions Both ordinal techniques offer substantial efficiency gains relative to the conventional binary analysis. The choice between the two techniques involves subtle value judgments. In the situations examined, the proportional odds model gave efficiency gains over and above the sliding dichotomy, but arguably, the sliding dichotomy is more intuitive and clinically appealing. Clinical Trials 2010; 7: 44—57. http://ctj.sagepub.com

Substantial heterogeneity exists among patients who suffer from traumatic brain injury (TBI). Strict enrollment criteria may diminish heterogeneity in randomized controlled trials (RCTs), but will also decrease recruitment and may affect the outcome distribution. The aim of this study was to investigate the influences of commonly used enrollment criteria for RCTs in TBI on potential recruitment and on outcome distribution. We used individual patient data from the International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) database, including six therapeutic phase III RCTs (n-5816) and three surveys (n-2217) in TBI. The primary outcome was the Glasgow Outcome Scale (GOS) at 6 months after injury, which we dichotomized as favorable/unfavorable. We investigated the influences of commonly used enrollment criteria on recruitment and outcome distribution: time window between injury and admission to study hospital = 1 reactive pupil; motor score > 1; Glasgow Coma Scale

Summary Background Major depressive disorder severely impairs the quality of life of patients with medical disorders such as cancer, but evidence to guide its management is scarce. We aimed to assess the efficacy and cost of a nurse-delivered complex intervention that was designed to treat major depressive disorder in patients who have cancer. Methods We did a randomised trial in a regional cancer centre in Scotland, UK. 200 outpatients who had cancer with a prognosis of greater than 6 months and major depressive disorder (identified by screening) were eligible and agreed to take part. Their mean age was 56·6 (SD 11·9) years, and 141 (71%) were women. We randomly assigned 99 of these participants to usual care, and 101 to usual care plus the intervention, with minimisation for sex, age, diagnosis, and extent of disease. The intervention was delivered by a cancer nurse at the centre over an average of seven sessions. The primary outcome was the difference in mean score on the self-reported Symptom Checklist-20 depression scale (range 0 to 4) at 3 months after randomisation. Analysis was by intention to treat. This trial is registered as ISRCTN84767225. Findings Primary outcome data were missing for four patients. For 196 patients for whom we had data at 3 months, the adjusted difference in mean Symptom Checklist-20 depression score, between those who received the intervention and those who did not, was 0·34 (95% CI 0·13–0·55). This treatment effect was sustained at 6 and 12 months. The intervention also improved anxiety and fatigue but not pain or physical functioning. It cost an additional £5278 (US$10 556) per quality-adjusted life-year gained. Interpretation The intervention—Depression Care for People with Cancer—offers a model for the management of major depressive disorder in patients with cancer and other medical disorders who are attending specialist medical services that is feasible, acceptable, and potentially cost effective. Funding Cancer Research UK.

The Glasgow Outcome Scale (GOS) is the primary endpoint for efficacy analysis of clinical trials in traumatic brain injury (TBI). Accurate and consistent assessment of outcome after TBI is essential to the evaluation of treatment results, particularly in the context of multicenter studies and trials. The inconsistent measurement or interobserver variation on GOS outcome, or for that matter, on any outcome scales, may adversely affect the sensitivity to detect treatment effects in clinical trial. The objective of this study is to examine effects of nondifferential misclassification of the widely used five-category GOS outcome scale and in particular to assess the impact of this misclassification on detecting a treatment effect and statistical power. We followed two approaches. First, outcome differences were analyzed before and after correction for misclassification using a dataset of 860 patients with severe brain injury randomly sampled from two TBI trials with known differences in outcome. Second, the effects of misclassification on outcome distribution and statistical power were analyzed in simulation studies on a hypothetical 800-patient dataset. Three potential patterns of nondifferential misclassification (random, upward and downward) on the dichotomous GOS outcome were analyzed, and the power of finding treatments differences was investigated in detail. All three patterns of misclassification reduce the power of detecting the true treatment effect and therefore lead to a reduced estimation of the true efficacy. The magnitude of such influence not only depends on the size of the misclassification, but also on the magnitude of the treatment effect. In conclusion, nondifferential misclassification directly reduces the power of finding the true treatment effect. An awareness of this procedural error and methods to reduce misclassification should be incorporated in TBI clinical trials.

Computerized tomography (CT) scanning provides an objective assessment of the structural damage to the brain following traumatic brain injury (TBI). We aimed to describe and quantify the relationship between CT characteristics and 6-month outcome, assessed by the Glasgow Outcome Scale (GOS). Individual patient data from the IMPACT database were available on CT classification (N = 5209), status of basal cisterns ( N = 3861), shift ( N = 4698), traumatic subarachnoid hemorrhage (tSAH) ( N = 7407), and intracranial lesions ( N = 7613). We used binary logistic and proportional odds regression for prognostic analyses. The CT classification was strongly related to outcome, with worst outcome for patients with diffuse injuries in CT class III (swelling; OR 2.50; CI 2.09-3.0) or CT class IV (shift; OR 3.03; CI 2.12-4.35). The prognosis in patients with mass lesions was better for patients with an epidural hematoma (OR 0.64; CI 0.56-0.72) and poorer for an acute subdural hematoma (OR 2.14; CI 1.87-2.45). Partial obliteration of the basal cisterns (OR 2.45; CI 1.88-3.20), tSAH (OR 2.64; CI 2.42-2.89), or midline shift (1-5 mm-OR 1.36; CI 1.09-1.68);5 mm-OR 2.20; CI 1.64-2.96) were strongly related to poorer outcome. Discrepancies were found between the scoring of basal cisterns/shift and the CT classification, indicating observer variation. These were less marked in studies that had used a central review process. Multivariable analysis indicated that individual CT characteristics added substantially to the prognostic value of the CT classification alone. We conclude that both the CT classification and individual CT characteristics are important predictors of outcome in TBI. For clinical trials, a central review process is advocated to minimize observer variability in CT assessment.

We aimed to describe and quantify the relationship between cause of injury and final outcome following traumatic brain injury (TBI). Individual patient data (N = 8708) from eight therapeutic Phase III randomized clinical trials in moderate or severe TBI, and three TBI surveys were used to investigate the relationship between cause of injury and outcome, as assessed by the Glasgow Outcome Scale (GOS) at 6 months. Proportional odds methodology was applied to quantify the strength of the association and expressed as an odds ratio in a meta-analysis. Heterogeneity across studies was assessed and associations with other predictive factors explored. In a univariate analysis, a strong association between the cause of injury and long-term outcome in moderate to severe TBI patients was observed, with consistent results across the studies. Road traffic accidents (OR 0.66, 95% CI 0.60-0.73), assaults (OR 0.66, 95% CI 0.52-0.84), and injuries sustained during sporting or recreational activities (OR 0.45, 95% CI 0.28-0.71) were all associated with better outcomes than the reference category of falls. Falls were found to be associated with an older age and with a higher incidence of mass lesions. Following adjustment for age in the analysis, the relationship between cause of injury and outcome was lost.

Abnormalities in laboratory parameters are frequent following traumatic brain injury (TBI), but few studies have investigated their predictive value. We aimed to describe and quantify the relation between laboratory parameters that are routinely determined on admission and final outcome following TBI. Individual patient data were available in the IMPACT database from six Phase III randomized controlled trials and one observational study in TBI. We studied glucose (N = 4834), sodium ( N = 5270), pH ( N = 3398), hemoglobin (Hb, N = 3875), platelet count ( N = 1629), and prothrombin time (PT; N = 840) for their associations with outcome at 6 months (Glasgow Outcome Scale [GOS]). We used logistic regression models with linear, quadratic, and restricted cubic spline functions. The strength of the associations was expressed as an unadjusted odds ratio, calculated over the shift in outcome between the 25th and 75th percentiles. Proportional odds methodology was further applied to quantify the strength of the associations across the full range of the GOS. All parameters were consistently associated with outcome in a continuous relationship: glucose and prothrombin time showed a positive linear relation to outcome (i.e., increasing values associated with poorer outcome) and Hb, platelets, and pH an inverse linear relation (i.e., low values associated with poorer outcome). Sodium demonstrated a U-shaped relation to outcome, with low levels being more strongly related to poorer outcome. Effects were strongest for increasing levels of glucose (odds ratio 1.7; 95% CI 1.54-1.83) and decreasing levels of Hb (odds ratio 0.7; CI 0.60-0.78). Higher glucose values were associated with increasing age, but on adjusted analysis, the strength of the association with outcome remained. Whether treatment of abnormal values may improve outcome needs further rigorous study.

The univariate prognostic analysis of the IMPACT database on traumatic brain injury (TBI) poses the formidable challenge of how best to summarize a highly complex set of data in a way which is accessible without being overly simplistic. In this paper, we describe and illustrate the battery of statistical methods that have been used. Boxplots, histograms, tabulations, and splines were used for initial data checking and in identifying appropriate transformations for more formal statistical modeling. Imputation techniques were used to minimize the problems associated with the analysis of incomplete data due to missing values. The associations between covariates and outcome (Glasgow Outcome Scale [GOS] assessed at 6 months) were expressed as odds ratios with supporting confidence intervals when the GOS was collapsed to a dichotomous scale. This was extended to use common odds ratios from proportional odds models to express associations over the full range of the GOS. Forest plots were used to illustrate the consistency of results from study to study within the IMPACT database. The overall prognostic strength of the prognostic factors was expressed as the proportion of variance explained (Nagelkerke's R(2) statistic). Many of our approaches are based on simple graphical displays of the data, but, where appropriate, we have also used methods that although established in the statistical literature are relatively novel in their application to TBI.

Outcome following traumatic brain injury (TBI) is not only dependent on the nature and severity of injury and subsequent treatment, but also on constituent characteristics of injured individuals. We aimed to describe and quantify the relationship between demographic characteristics and six month outcome assessed by the Glasgow Outcome Scale (GOS) after TBI. Individual patient data on age (n = 8719), gender (n = 8720), race (n = 5320), and education (n = 2201) were extracted from eight therapeutic Phase III randomized clinical trials and three surveys in moderate or severe TBI, contained in the IMPACT database. The strength of prognostic effects was analyzed with binary and proportional odds regression analysis and expressed as an odds ratio. Age was analyzed as a continuous variable with spline functions, and the odds ratio calculated over the difference between the 75 th and 25 th percentiles. Associations with other predictors were explored. Increasing age was strongly related to poorer outcome (OR 2.14; 95% CI 2.00-2.28) in a continuous fashion that could be approximated by a linear function. No gender differences in outcome were found (OR: 1.01; CI 0.92-1.11), and exploratory analysis failed to show any gender/age interaction. The studies included predominantly Caucasians (83%); outcome in black patients was poorer relative to this group (OR 1.30; CI 1.09-1.56). This relationship was sustained on adjusted analyses, and requires further study into mediating factors. Higher levels of education were weakly related to a better outcome (OR: 0.70; CI 0.52-0.94). On multivariable analysis adjusting for age, motor score, and pupils, the prognostic effect of race and education were sustained. We conclude that outcome following TBI is dependent on age, race, to a lesser extent on education, but not on gender.

We studied the prognostic strength of the individual components of the Glasgow Coma Scale (GCS) and pupil reactivity to Glasgow Outcome Score (GOS) at 6 months post-injury. A total of 8721 moderate or severe traumatic brain injury (TBI) patient data from the IMPACT database on traumatic brain injury comprised the study cohort. The associations between motor score and pupil reactivity and 6-month GOS were analyzed by binary logistic regression and proportional odds methodology. The strength of prognostic effects were expressed as the unadjusted odds ratios presented for all individual studies as well as in meta-analysis. We found a consistent strong association between motor score and 6-month GOS across all studies (OR 1.74-7.48). The Eye and Verbal components were also strongly associated with GOS. In the pooled population, one or both un-reactive pupils and lower motor scores were significantly associated with unfavorable outcome (range 2.71-7.31). We also found a significant change in motor score from pre-hospital direct to study hospital enrollment ( p0.0001) and from the first in-hospital to study enrollment scores (p0.0001). Pupil reactivity was more robust between these time points. It is recommended that the study hospital enrollment GCS and pupil reactivity be used for prognostic analysis.

Research in traumatic brain injury (TBI) is challenging for several reasons; in particular, the heterogeneity between patients regarding causes, pathophysiology, treatment, and outcome. Advances in basic science have failed to translate into successful clinical treatments, and the evidence underpinning guideline recommendations is weak. Because clinical research has been hampered by non-standardised data collection, restricted multidisciplinary collaboration, and the lack of sensitivity of classification and efficacy analyses, multidisciplinary collaborations are now being fostered. Approaches to deal with heterogeneity have been developed by the IMPACT study group. These approaches can increase statistical power in clinical trials by up to 50% and are also relevant to other heterogeneous neurological diseases, such as stroke and subarachnoid haemorrhage. Rather than trying to limit heterogeneity, we might also be able to exploit it by analysing differences in treatment and outcome between countries and centres in comparative effectiveness research. This approach has great potential to advance care in patients with TBI.

Clinical trials in traumatic brain injury (TBI) pose complex methodological challenges, largely related to the heterogeneity of the population. The International Mission on Prognosis and Clinical Trial Design in TBI study group has explored approaches for dealing with this heterogeneity with the aim to optimize clinical trials in TBI. Extensive prognostic analyses and simulation studies were conducted on individual patient data from eight trials and three observational studies. Here, we integrate the results of these studies into the International Mission on Prognosis and Clinical Trial Design in TBI recommendations for design and analysis of trials in TBI: Details of the major baseline prognostic characteristics should be provided in every report on a TBI study; in trials they should be differentiated per treatment group. We also advocate the reporting of the baseline prognostic risk as determined by validated prognostic models. Inclusion criteria should be as broad as is compatible with the current understanding of the mechanisms of action of the intervention being evaluated. This will maximize recruitment rates and enhance the generalizability of the results. The statistical analysis should incorporate prespecified covariate adjustment to mitigate the effects of the heterogeneity. The statistical analysis should use an ordinal approach, based on either sliding dichotomy or proportional odds methodology. Broad inclusion criteria, prespecified covariate adjustment, and an ordinal analysis will promote an efficient trial, yielding gains in statistical efficiency of more than 40%. This corresponds to being able to detect a 7% treatment effect with the same number of patients needed to demonstrate a 10% difference with an unadjusted analysis based on the dichotomized Glasgow outcome scale.

Objective. Heterogeneity of patients is a common problem in randomized controlled trials (RCTs) in various fields of clinical research. We aimed to investigate the potential benefits of different approaches for dealing with heterogeneity in a case study on traumatic brain injury (TBI). Design and Setting: Statistical modeling studies in three surveys and six randomized controlled trials. Patients: Individual patient data (n = 8033) from the IMPACT database. Interventions: We investigated the statistical power and efficiency of randomized controlled trials (RCTs) in relation to (1) selection according to baseline characteristics, (2) prognostic targeting (i.e., excluding those with a relatively extreme prognosis), and (3) covariate-adjusted analysis. Statistical power was expressed as the required sample size for obtaining 80% power and efficiency as the relative change in study duration, reflecting both gains in power and adverse effects on recruitment. Uniform and targeted treatment effects were simulated for 6 month unfavorable outcome. Results: For a uniform treatment effect, selection resulted in a sample size reduction of 33% in the surveys and 5% in the RCTs, but decreased recruitment by 65% and 41%, respectively. Hence, the relative study duration was prolonged (surveys: +95%; RCTs: +60%). Prognostic targeting resulted in sample size reductions of 28% and 17%, and increased relative study duration by +5% in surveys and +11% in the RCTs. Covariate adjustment reduced sample sizes by 30% and 16%, respectively, and did not affect recruitment. For a targeted treatment effect, the sample size reductions by selection (surveys: 47%; RCTs: 20%) and prognostic targeting (surveys: 49%; RCTs: 41%) were larger and adverse effects on recruitment smaller. Conclusions. The benefits of selection and prognostic targeting in terms of statistical power are reversed by adverse effects on recruitment. Covariate adjusted analysis in a broadly selected group of patients is advisable if a uniform treatment effect is assumed, since there is no decrease in recruitment. (Crit Care Med 2009; 37:2683-2690)

Background Traumatic brain injury (TBI) is a leading cause of death and disability. A reliable prediction of outcome on admission is of great clinical relevance. We aimed to develop prognostic models with readily available traditional and novel predictors. Methods and Findings Prospectively collected individual patient data were analyzed from 11 studies. We considered predictors available at admission in logistic regression models to predict mortality and unfavorable outcome according to the Glasgow Outcome Scale at 6 mo after injury. Prognostic models were developed in 8,509 patients with severe or moderate TBI, with cross-validation by omission of each of the 11 studies in turn. External validation was on 6,681 patients from the recent Medical Research Council Corticosteroid Randomisation after Significant Head Injury (MRC CRASH) trial. We found that the strongest predictors of outcome were age, motor score, pupillary reactivity, and CT characteristics, including the presence of traumatic subarachnoid hemorrhage. A prognostic model that combined age, motor score, and pupillary reactivity had an area under the receiver operating characteristic curve (AUC) between 0.66 and 0.84 at cross-validation. This performance could be improved (AUC increased by approximately 0.05) by considering CT characteristics, secondary insults (hypotension and hypoxia), and laboratory parameters (glucose and hemoglobin). External validation confirmed that the discriminative ability of the model was adequate (AUC 0.80). Outcomes were systematically worse than predicted, but less so in 1,588 patients who were from high-income countries in the CRASH trial. Conclusions Prognostic models using baseline characteristics provide adequate discrimination between patients with good and poor 6 mo outcomes after TBI, especially if CT and laboratory findings are considered in addition to traditional predictors. The model predictions may support clinical practice and research, including the design and analysis of randomized controlled trials., Ewout Steyerberg and colleagues describe a prognostic model for the prediction of outcome of traumatic brain injury using data available on admission., Editors' Summary Background. Traumatic brain injury (TBI) causes a large amount of morbidity and mortality worldwide. According to the Centers for Disease Control, for example, about 1.4 million Americans will sustain a TBI—a head injury—each year. Of these, 1.1 million will be treated and released from an emergency department, 235,000 will be hospitalized, and 50,000 will die. The burden of disease is much higher in the developing world, where the causes of TBI such as traffic accidents occur at higher rates and treatment may be less available. Why Was This Study Done? Given the resources required to treat TBI, a very useful research tool would be the ability to accurately predict on admission to hospital what the outcome of a given injury might be. Currently, scores such as the Glasgow Coma Scale are useful to predict outcome 24 h after the injury but not before. Prognostic models are useful for several reasons. Clinically, they help doctors and patients make decisions about treatment. They are also useful in research studies that compare outcomes in different groups of patients and when planning randomized controlled trials. The study presented here is one of a number of analyses done by the IMPACT research group over the past several years using a large database that includes data from eight randomized controlled trials and three observational studies conducted between 1984 and 1997. There are other ongoing studies that also seek to develop new prognostic models; one such recent study was published in BMJ by a group involving the lead author of the PLoS Medicine paper described here. What Did the Researchers Do and Find? The authors analyzed data that had been collected prospectively on individual patients from the 11 studies included in the database and derived models to predict mortality and unfavorable outcome at 6 mo after injury for the 8,509 patients with severe or moderate TBI. They found that the strongest predictors of outcome were age, motor score, pupillary reactivity, and characteristics on the CT scan, including the presence of traumatic subarachnoid hemorrhage. A core prognostic model could be derived from the combination of age, motor score, and pupillary reactivity. A better score could be obtained by adding CT characteristics, secondary problems (hypotension and hypoxia), and laboratory measurements of glucose and hemoglobin. The scores were then tested to see how well they predicted outcome in a different group of patients—6,681 patients from the recent Medical Research Council Corticosteroid Randomisation after Significant Head Injury (MRC CRASH) trial. What Do These Findings Mean? In this paper the authors show that it is possible to produce prognostic models using characteristics collected on admission as part of routine care that can discriminate between patients with good and poor outcomes 6 mo after TBI, especially if the results from CT scans and laboratory findings are added to basic models. This paper has to be considered together with other studies, especially the paper mentioned above, which was recently published in the BMJ (MRC CRASH Trial Collaborators [2008] Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients. BMJ 336: 425–429.). The BMJ study presented a set of similar, but subtly different models, with specific focus on patients in developing countries; in that case, the patients in the CRASH trial were used to produce the models, and the patients in the IMPACT database were used to verify one variant of the models. Unfortunately this related paper was not disclosed to us during the initial review process; however, during PLoS Medicine's subsequent consideration of this manuscript we learned of it. After discussion with the reviewers, we took the decision that the models described in the PLoS Medicine paper are sufficiently different from those reported in the other paper and as such proceeded with publication of the paper. Ideally, however, these two sets of models would have been reviewed and published side by side, so that readers could easily evaluate the respective merits and value of the two different sets of models in the light of each other. The two sets of models are, however, discussed in a Perspective article also published in PLoS Medicine (see below). Additional Information. Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050165. This paper and the BMJ paper mentioned above are discussed further in a PLoS Medicine Perspective article by Andrews and Young The TBI Impact site provides a tool to calculate the scores described in this paper The CRASH trial, which is used to validate the scores mentioned here, has a Web site explaining the trial and its results The R software, which was used for the prognostic analyses, is freely available The MedlinePlus encyclopedia has information on head injury The WHO site on neurotrauma discusses head injury from a global perspective The CDC's National Center for Injury Prevention and Control gives statistics on head injury in the US and advice on prevention

Hypotension following traumatic brain injury (TBI) is recognized as an important secondary insult that is associated with adverse outcome. We aimed to describe the relationship between actual levels of admission blood pressure and Glasgow Outcome Scale (GOS) at 6 months. Individual patient data from the IMPACT database were available on systolic (N = 6801) and mean arterial (N = 6647) blood pressure. Regression models with restricted cubic spline functions were used to explore the shape of the relationships between blood pressure and outcome in unadjusted and adjusted analyses. Proportional odds methodology was applied to quantify the strength of the associations across the full range of the GOS. Analyses were performed to search for threshold values. A smooth U-shaped relationship was observed between systolic (SBP) and mean arterial (MABP) blood pressures and outcome, without any evidence of an abrupt threshold effect. Best outcomes were observed for values of SBP of the order of 135 mm Hg and for values of MABP of the order of 90 mm Hg. Both lower (OR 1.53; 95% CI: 1.31-1.80) and higher levels (OR 1.42; CI: 1.20-1.68) of SBP and lower (OR 1.30; CI 1.12-1.51) and higher levels of MABP (OR 1.45; CI 1.19-1.76) were associated with poorer outcome. These findings were consistent across studies. The relationship between high blood pressure level and poorer outcome largely disappeared on adjusted analysis. Current guidelines for the management of blood pressure in TBI focus on the avoidance of hypotension as defined by SBP90 mm Hg. Our finding of a smooth relationship with improving outcome as SBP increases up to 135 mm Hg, while not supporting a strong causal inference, does suggest that current guidelines need to be reconsidered.

We determined the relationship between secondary insults (hypoxia, hypotension, and hypothermia) occurring prior to or on admission to hospital and 6-month outcome after traumatic brain injury (TBI). A meta-analysis of individual patient data, from seven Phase III randomized clinical trials (RCT) in moderate or severe TBI and three TBI population-based series, was performed to model outcome as measured by the Glasgow Outcome Scale (GOS). Proportional odds modeling was used to relate the probability of a poor outcome to hypoxia (N = 5661), hypotension ( N = 6629), and hypothermia ( N = 4195) separately. We additionally analyzed the combined effects of hypoxia and hypotension and performed exploratory analysis of associations with computerized tomography (CT) classification and month of injury. Having a pre-enrollment insult of hypoxia, hypotension or hypothermia is strongly associated with a poorer outcome (odds ratios of 2.1 95% CI [1.7-2.6], 2.7 95% CI [2.1-3.4], and 2.2 95% CI [1.6-3.2], respectively). Patients with both hypoxia and hypotension had poorer outcomes than those with either insult alone. Radiological signs of raised intracranial pressure (CT class III or IV) were more frequent in patients who had sustained hypoxia or hypotension. A significant association was observed between month of injury and hypothermia. The occurrence of secondary insults prior to or on admission to hospital in TBI patients is strongly related to poorer outcome and should therefore be a priority for emergency department personnel.

We studied the prognostic value of a wide range of conventional and novel prognostic factors on admission after traumatic brain injury (TBI) using both univariate and multivariable analysis. The outcome measure was Glasgow Outcome Scale at 6 months after injury. Individual patient data were available on a cohort of 8686 patients drawn from eight randomized controlled trials and three observational studies. The most powerful independent prognostic variables were age, Glasgow Coma Scale (GCS) motor score, pupil response, and computerized tomography (CT) characteristics, including the Marshall CT classification and traumatic subarachnoid hemorrhage. Prothrombin time was also identified as a powerful independent prognostic factor, but it was only available for a limited number of patients coming from three of the relevant studies. Other important prognostic factors included hypotension, hypoxia, the eye and verbal components of the GCS, glucose, platelets, and hemoglobin. These results on prognostic factors will underpin future work on the IMPACT project, which is focused on the development of novel approaches to the design and analysis of clinical trials in TBI. In addition, the results provide pointers to future research, including further analysis of the prognostic value of prothrombin time, and the evaluation of the clinical impact of intervening aggressively to correct abnormalities in hemoglobin, glucose, and coagulation.

The objective of this report is to describe the design and content of the International Mission for Prognosis And Clinical Trial (IMPACT) database of traumatic brain injury which contains the complete dataset from most clinical trials and organized epidemiologic studies conducted over the past 20 years. This effort, funded by the U.S. National Institutes of Health, has led to the accumulation thus far of data from 9205 patients with severe and moderate brain injuries from eight randomized placebo controlled trials and three observational studies. Data relevant to the design and analysis of pragmatic Phase III clinical trials, including pre-hospital, admission, and post-resuscitation assessments, information on the acute management, and short- and long-term outcome were merged into a top priority data set (TPDS). The major emphasis during the first phase of study is on information from time of injury to post-resuscitation and outcome at 6 months thereby providing a unique resource for prognostic analysis and for studies aimed at optimizing the design and analysis of Phase III trials in traumatic brain injury.

Objectives (1) To assess the overall performance of the Hospital Anxiety and Depression Scale (HADS) as a screening instrument to identify cases of major depressive disorder (MDD) in mixed cancer outpatients. (2) To determine the sensitivity and specificity of various HADS cut-off scores. (3) To recommend an optimal HADS cut-off score for use in this population. Methods A cross-sectional questionnaire and interview survey of consecutive patients attending outpatient clinics in a regional cancer centre in Edinburgh, Scotland, United Kingdom. Results Thirty (8.3%) of 361 patients met criteria for MDD at interview. With the use of the total HADS score to identify these cases of MDD, the area under the receiver operating characteristic curve was 0.94 (95% CI, 0.91–0.97). A cut-off of 14/15 gave a sensitivity of 0.87 (95% CI, 0.70–0.95), a specificity of 0.85 (95% CI, 0.81–0.89), and a positive predictive value of 0.35, and was considered optimal. Conclusion The HADS can be used effectively as an initial screening tool for the detection of MDD in outpatients attending mixed cancer clinics.