External Validation of the International Mission for Prognosis and Analysis of Clinical Trials Model and the Role of Markers of Coagulation
*Department of Neurosurgery, Helsinki University Central Hospital, Helsinki, Finland Neurosurgery
(Impact Factor: 3.62).
08/2013; 73(2):305-11. DOI: 10.1227/01.neu.0000430326.40763.ec
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.
Available from: Ewout W Steyerberg
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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 · 21.90 Impact Factor
Available from: Omar Bouamra
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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.
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).
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 · 1.81 Impact Factor
Available from: Rahul Raj
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ABSTRACT: The aim of this study was to evaluate the usefulness of the APACHE II (Acute Physiology and Chronic Health Evaluation II), SAPS II (Simplified Acute Physiology Score II) and SOFA (Sequential Organ Failure Assessment) scores compared to simpler models based on age and Glasgow Coma Scale (GCS) in predicting long-term outcome of patients with moderate-to-severe traumatic brain injury (TBI) treated in the intensive care unit (ICU).
A national ICU database was screened for eligible TBI patients (age over 15 years, GCS 3-13) admitted in 2003-2012. Logistic regression was used for customization of APACHE II, SAPS II and SOFA score-based models for six-month mortality prediction. These models were compared to an adjusted SOFA-based model (including age) and a reference model (age and GCS). Internal validation was performed by a randomized split-sample technique. Prognostic performance was determined by assessing discrimination, calibration and precision.
In total, 1,625 patients were included. The overall six-month mortality was 33%. The APACHE II and SAPS II-based models showed good discrimination (area under the curve (AUC) 0.79, 95% confidence interval (CI) 0.75 to 0.82; and 0.80, 95% CI 0.77 to 0.83, respectively), calibration (P > 0.05) and precision (Brier score 0.166 to 0.167). The SOFA-based model showed poor discrimination (AUC 0.68, 95% CI 0.64 to 0.72) and precision (Brier score 0.201) but good calibration (P > 0.05). The AUC of the SOFA-based model was significantly improved after the insertion of age and GCS ([increment]AUC +0.11, P < 0.001). The performance of the reference model was comparable to the APACHE II and SAPS II in terms of discrimination (AUC 0.77; compared to APACHE II, DeltaAUC -0.02, P = 0.425; compared to SAPS II, DeltaAUC -0.03, P = 0.218), calibration (P > 0.05) and precision (Brier score 0.181).
A simple prognostic model, based only on age and GCS, displayed a fairly good prognostic performance in predicting six-month mortality of ICU-treated patients with TBI. The use of the more complex scoring systems APACHE II, SAPS II and SOFA added little to the prognostic performance.
Critical care (London, England) 04/2014; 18(2):R60. DOI:10.1186/cc13814 · 4.48 Impact Factor
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