External validation of the international mission for prognosis and analysis of clinical trials model and the role of markers of coagulation
ABSTRACT Background: Markers of coagulation have shown to have important value in predicting traumatic brain injury outcome. Objective: To externally validate and investigate the role of markers of coagulation for outcome prediction by using the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) model while adjusting for overall injury severity. Methods: A retrospective chart analysis of traumatic brain injury patients admitted to Helsinki University Central Hospital between 2009 and 2010 was performed. Outcome was estimated by using the criteria of the IMPACT model. Admission international normalized ratio (INR) and platelet count were used as markers of coagulation. Overall injury severity was categorized with the injury severity score (ISS). Variables were added to the calculated IMPACT risk, generating new models. Model performance was assessed by analyzing and comparing the area under the curve (AUC) of the models. Results: For 342 included patients, 6-month mortality was 32% and unfavorable neurological outcome was 36%. Patients with a poor outcome had lower platelets and higher INR and ISS than those with good outcome (P,.001). The IMPACT model had an AUC of 0.85 for predicting mortality and 0.81 for neurological outcome. Addition of INR but not ISS or platelets to the IMPACT predicted risk improved the predictive validity for mortality (ÄAUC 0.02, P =.034) but not neurological outcome (ÄAUC 0.00, P =.401). In multivariate analysis, INR remained significant for mortality but not for neurological outcome when adjusting for IMPACT risk and ISS (P =.012). Conclusion: The IMPACT model showed excellent performance, and INR was an independent predictor for mortality, independent of overall injury severity.
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ABSTRACT: 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.The Lancet Neurology 12/2013; 12(12):1200–1210. DOI:10.1016/S1474-4422(13)70234-5 · 21.82 Impact Factor
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ABSTRACT: Introduction By analysing risk-adjusted mortality ratios, weaknesses in the process of care might be identified. Traumatic brain injury (TBI) is the main cause of death in trauma, and thus it is crucial that trauma prediction models are valid for TBI patients. Accordingly, we assessed the validity of the RISC score in TBI patients by internal and external validation analyses. Methods Patients with moderate-to-severe TBI admitted to the TraumaRegister DGU® (TR-DGU) and the trauma registry of Helsinki University Hospital (TR-THEL) in 2006-2011 were included in this retrospective open cohort study. Definition of moderate-to-severe TBI was head abbreviated injury scale of 3 or higher. Subgroup analysis for patients with isolated and polytrauma TBI was performed. The performance of the RISC score was evaluated by assessing its discrimination (area under the curve, AUC) and calibration (Hosmer–Lemeshow [H–L] test). Results Among the 9,106 and 809 patients with moderate-to-severe TBI admitted to TR-DGU and TR-THEL, unadjusted mortality was 26% and 23%, respectively. Internal and external validation of the RISC score showed good discrimination (TR-DGU AUC 0.89, 95% confidence interval [CI] 0.88-0.90 and TR-THEL AUC 0.84, 95% CI 0.81-0.87), but poor calibration (p < 0.001) in patients with moderate-to-severe TBI. Subgroup analysis found the discrimination only to be modest in isolated TBI (AUC 0.76) and calibration to be particularly poor in polytrauma TBI (TR-DGU H–L = 4356, p < 0.001; TR-THEL H-L 112, p < 0.001). Conclusion The RISC score was found to be of limited predictive value in patients with moderate-to-severe TBI. A new general trauma scoring system that includes TBI specific prognostic factors is warranted.Injury 01/2014; 46(1). DOI:10.1016/j.injury.2014.08.026 · 2.46 Impact Factor
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ABSTRACT: Abstract Primary objective: To identify which tool (a model, a biomarker or a combination of these) has better prognostic strength in traumatic brain injury (TBI). Design and methods: Data of 100 patients were analysed. TBI prognostic model B, constructed in Trauma Audit and Research Network (TARN), was run on the dataset and then S100B was added to this model. Another model was developed containing only S100B and, subsequently, other important predictors were added to assess the enhancement of the predictive power. The outcome measures were survival and favourable outcome. Results: No difference between performance of the prognostic model or S100B in isolation is observed. Addition of S100B to the prognostic model improves the performance (e.g. AUC, R(2) Nagelkerke and classification accuracy of TARN model B to predict survival increase respectively from 0.66, 0.11 and 70% to 0.77, 0.25 and 75%). Similarly, the predictive power of S100B increases by adding other predictors (e.g. AUC (0.69 vs. 0.79), R(2) Nagelkerke (0.15 vs. 0.30) and classification accuracy (73% vs. 77%) for survival prediction). Conclusion: A better prognostic tool than those currently available may be a combination of clinical predictors with a biomarker.Brain Injury 03/2014; 28(7). DOI:10.3109/02699052.2014.890743 · 1.86 Impact Factor