scholarly journals Prognosis in moderate-severe traumatic brain injury in a Swedish cohort and external validation of the IMPACT models

2021 ◽  
Author(s):  
Elham Rostami ◽  
David Gustafsson ◽  
Anders Hånell ◽  
Timothy Howells ◽  
Samuel Lenell ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Outcome Prediction ◽  
External Validation ◽  
Low Risk ◽  
Unfavorable Outcome ◽  
Time Interval ◽  
Quality Register ◽  
The Impact ◽  
Predicted Risk

Abstract Background A major challenge in management of traumatic brain injury (TBI) is to assess the heterogeneity of TBI pathology and outcome prediction. A reliable outcome prediction would have both great value for the healthcare provider, but also for the patients and their relatives. A well-known prediction model is the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) prognostic calculator. The aim of this study was to externally validate all three modules of the IMPACT calculator on TBI patients admitted to Uppsala University hospital (UUH). Method TBI patients admitted to UUH are continuously enrolled into the Uppsala neurointensive care unit (NICU) TBI Uppsala Clinical Research (UCR) quality register. The register contains both clinical and demographic data, radiological evaluations, and outcome assessments based on the extended Glasgow outcome scale extended (GOSE) performed at 6 months to 1 year. In this study, we included 635 patients with severe TBI admitted during 2008–2020. We used IMPACT core parameters: age, motor score, and pupillary reaction. Results The patients had a median age of 56 (range 18–93), 142 female and 478 male. Using the IMPACT Core model to predict outcome resulted in an AUC of 0.85 for mortality and 0.79 for unfavorable outcome. The CT module did not increase AUC for mortality and slightly decreased AUC for unfavorable outcome to 0.78. However, the lab module increased AUC for mortality to 0.89 but slightly decreased for unfavorable outcome to 0.76. Comparing the predicted risk to actual outcomes, we found that all three models correctly predicted low risk of mortality in the surviving group of GOSE 2–8. However, it produced a greater variance of predicted risk in the GOSE 1 group, denoting general underprediction of risk. Regarding unfavorable outcome, all models once again underestimated the risk in the GOSE 3–4 groups, but correctly predicts low risk in GOSE 5–8. Conclusions The results of our study are in line with previous findings from centers with modern TBI care using the IMPACT model, in that the model provides adequate prediction for mortality and unfavorable outcome. However, it should be noted that the prediction is limited to 6 months outcome and not longer time interval.

Prognostic significance of abnormal hematological parameters in severe traumatic brain injury requiring decompressive craniectomy

2020 ◽  
Vol 132 (2) ◽  
pp. 545-551 ◽  
Author(s):  
Jade-Marie Corbett ◽  
Kwok M. Ho ◽  
Stephen Honeybul
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Decompressive Craniectomy ◽  
Prognostic Model ◽  
Prognostic Significance ◽  
Unfavorable Outcome ◽  
Severe Tbi ◽  
Hematological Abnormalities ◽  
The Impact ◽  
Predicted Risk

OBJECTIVEHematological abnormalities after severe traumatic brain injury (TBI) are common, and are associated with a poor outcome. Whether these abnormalities offer additional prognostic significance over and beyond validated TBI prognostic models is uncertain.METHODSThis retrospective cohort study compared the ability of admission hematological abnormalities to that of the IMPACT (International Mission for Prognosis and Analysis of Clinical Trials) prognostic model to predict 18-month neurological outcome of 388 patients who required a decompressive craniectomy after severe TBI, between 2004 and 2016, in Western Australia. Area under the receiver operating characteristic (AUROC) curve was used to assess predictors’ ability to discriminate between patients with and without an unfavorable outcome of death, vegetative state, or severe disability.RESULTSOf the 388 patients included in the study, 151 (38.9%) had an unfavorable outcome at 18 months after decompressive craniectomy for severe TBI. Abnormalities in admission hemoglobin (AUROC 0.594, p = 0.002), plasma glucose (AUROC 0.592, p = 0.002), fibrinogen (AUROC 0.563, p = 0.036), international normalized ratio (INR; AUROC 0.645, p = 0.001), activated partial thromboplastin time (AUROC 0.564, p = 0.033), and disseminated intravascular coagulation score (AUROC 0.623, p = 0.001) were all associated with a higher risk of unfavorable outcome at 18 months after severe TBI. As a marker of inflammation, neutrophil to lymphocyte ratio was not significantly associated with the risk of unfavorable outcome (AUROC 0.500, p = 0.998). However, none of these parameters, in addition to the platelet count, were significantly associated with an unfavorable outcome after adjusting for the IMPACT predicted risk (odds ratio [OR] per 10% increment in risk 2.473, 95% confidence interval [CI] 2.061–2.967; p = 0.001). After excluding 8 patients (2.1%) who were treated with warfarin prior to the injury, there was a suggestion that INR was associated with some additional prognostic significance (OR 3.183, 95% CI 0.856–11.833; p = 0.084) after adjusting for the IMPACT predicted risk.CONCLUSIONSIn isolation, INR was the best hematological prognostic parameter in severe TBI requiring decompressive craniectomy, especially when patients treated with warfarin were excluded. However, the prognostic significance of admission hematological abnormalities was mostly captured by the IMPACT prognostic model, such that they did not offer any additional prognostic information beyond the IMPACT predicted risk. These results suggest that new prognostic factors for TBI should be evaluated in conjunction with predicted risks of a comprehensive prognostic model that has been validated, such as the IMPACT prognostic model.

scholarly journals Prognostic performance of computerized tomography scoring systems in civilian penetrating traumatic brain injury: an observational study

2019 ◽  
Vol 161 (12) ◽  
pp. 2467-2478 ◽  
Author(s):  
Matias Lindfors ◽  
Caroline Lindblad ◽  
David W. Nelson ◽  
Bo-Michael Bellander ◽  
Jari Siironen ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Computerized Tomography ◽  
Outcome Prediction ◽  
Unfavorable Outcome ◽  
Receiver Operating Curve ◽  
Excellent Performance ◽  
Head Ct ◽  
Severity Scores ◽  
Penetrating Traumatic Brain Injury

Abstract Background The prognosis of penetrating traumatic brain injury (pTBI) is poor yet highly variable. Current computerized tomography (CT) severity scores are commonly not used for pTBI prognostication but may provide important clinical information in these cohorts. Methods All consecutive pTBI patients from two large neurotrauma databases (Helsinki 1999–2015, Stockholm 2005–2014) were included. Outcome measures were 6-month mortality and unfavorable outcome (Glasgow Outcome Scale 1–3). Admission head CT scans were assessed according to the following: Marshall CT classification, Rotterdam CT score, Stockholm CT score, and Helsinki CT score. The discrimination (area under the receiver operating curve, AUC) and explanatory variance (pseudo-R2) of the CT scores were assessed individually and in addition to a base model including age, motor response, and pupil responsiveness. Results Altogether, 75 patients were included. Overall 6-month mortality and unfavorable outcome were 45% and 61% for all patients, and 31% and 51% for actively treated patients. The CT scores’ AUCs and pseudo-R2s varied between 0.77–0.90 and 0.35–0.60 for mortality prediction and between 0.85–0.89 and 0.50–0.57 for unfavorable outcome prediction. The base model showed excellent performance for mortality (AUC 0.94, pseudo-R2 0.71) and unfavorable outcome (AUC 0.89, pseudo-R2 0.53) prediction. None of the CT scores increased the base model’s AUC (p > 0.05) yet increased its pseudo-R2 (0.09–0.15) for unfavorable outcome prediction. Conclusion Existing head CT scores demonstrate good-to-excellent performance in 6-month outcome prediction in pTBI patients. However, they do not add independent information to known outcome predictors, indicating that a unique score capturing the intracranial severity in pTBI may be warranted.

scholarly journals Predicting outcome in patients with moderate to severe traumatic brain injury using electroencephalography

Critical Care ◽  
2019 ◽  
Vol 23 (1) ◽  
Author(s):  
Marjolein E. Haveman ◽  
Michel J. A. M. Van Putten ◽  
Harold W. Hom ◽  
Carin J. Eertman-Meyer ◽  
Albertus Beishuizen ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Random Forest ◽  
Outcome Prediction ◽  
Training Set ◽  
Arterial Blood ◽  
Continuous Eeg ◽  
Severe Tbi ◽  
Validation Set ◽  
The Impact

Abstract Background Better outcome prediction could assist in reliable quantification and classification of traumatic brain injury (TBI) severity to support clinical decision-making. We developed a multifactorial model combining quantitative electroencephalography (qEEG) measurements and clinically relevant parameters as proof of concept for outcome prediction of patients with moderate to severe TBI. Methods Continuous EEG measurements were performed during the first 7 days of ICU admission. Patient outcome at 12 months was dichotomized based on the Extended Glasgow Outcome Score (GOSE) as poor (GOSE 1–2) or good (GOSE 3–8). Twenty-three qEEG features were extracted. Prediction models were created using a Random Forest classifier based on qEEG features, age, and mean arterial blood pressure (MAP) at 24, 48, 72, and 96 h after TBI and combinations of two time intervals. After optimization of the models, we added parameters from the International Mission for Prognosis And Clinical Trial Design (IMPACT) predictor, existing of clinical, CT, and laboratory parameters at admission. Furthermore, we compared our best models to the online IMPACT predictor. Results Fifty-seven patients with moderate to severe TBI were included and divided into a training set (n = 38) and a validation set (n = 19). Our best model included eight qEEG parameters and MAP at 72 and 96 h after TBI, age, and nine other IMPACT parameters. This model had high predictive ability for poor outcome on both the training set using leave-one-out (area under the receiver operating characteristic curve (AUC) = 0.94, specificity 100%, sensitivity 75%) and validation set (AUC = 0.81, specificity 75%, sensitivity 100%). The IMPACT predictor independently predicted both groups with an AUC of 0.74 (specificity 81%, sensitivity 65%) and 0.84 (sensitivity 88%, specificity 73%), respectively. Conclusions Our study shows the potential of multifactorial Random Forest models using qEEG parameters to predict outcome in patients with moderate to severe TBI.

External Validation of the International Mission for Prognosis and Analysis of Clinical Trials Model and the Role of Markers of Coagulation

Neurosurgery ◽  
2013 ◽  
Vol 73 (2) ◽  
pp. 305-311 ◽  
Author(s):  
Rahul Raj ◽  
Jari Siironen ◽  
Riku Kivisaari ◽  
Juha Hernesniemi ◽  
Päivi Tanskanen ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Clinical Trials ◽  
Brain Injury ◽  
Neurological Outcome ◽  
Injury Severity ◽  
External Validation ◽  
Impact Model ◽  
Impact Risk ◽  
The Impact

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 ([INCREMENT]AUC 0.02, P = .034) but not neurological outcome ([INCREMENT]AUC 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.

IMPACT and CRASH prognostic models for traumatic brain injury: external validation in a South-American cohort

2020 ◽  
Vol 26 (6) ◽  
pp. 546-554
Author(s):  
Kwankaew Wongchareon ◽  
Hilaire J Thompson ◽  
Pamela H Mitchell ◽  
Jason Barber ◽  
Nancy Temkin
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Low Income ◽  
Characteristic Curve ◽  
External Validation ◽  
Prognostic Models ◽  
South American ◽  
Unfavourable Outcome ◽  
Middle Income ◽  
The Impact

ObjectiveTo develop a robust prognostic model, the more diverse the settings in which the system is tested and found to be accurate, the more likely it will be generalisable to untested settings. This study aimed to externally validate the International Mission for Prognosis and Clinical Trials in Traumatic Brain Injury (IMPACT) and Corticosteroid Randomization after Significant Head Injury (CRASH) models for low-income and middle-income countries using a dataset of patients with severe traumatic brain injury (TBI) from the Benchmark Evidence from South American Trials: Treatment of Intracranial Pressure study and a simultaneously conducted observational study.MethodA total of 550 patients with severe TBI were enrolled in the study, and 466 of those were included in the analysis. Patient admission characteristics were extracted to predict unfavourable outcome (Glasgow Outcome Scale: GOS<3) and mortality (GOS 1) at 14 days or 6 months.ResultsThere were 48% of the participants who had unfavourable outcome at 6 months and these included 38% who had died. The area under the receiver operating characteristic curve (AUC) values were 0.683–0.775 and 0.640–0.731 for the IMPACT and CRASH models respectively. The IMPACT CT model had the highest AUC for predicting unfavourable outcomes, and the IMPACT Lab model had the best discrimination for predicting 6-month mortality. The discrimination for both the IMPACT and CRASH models improved with increasing complexity of the models. Calibration revealed that there were disagreement between observed and predicted outcomes in the IMPACT and CRASH models.ConclusionThe overall performance of all IMPACT and CRASH models was adequate when used to predict outcomes in the dataset. However, some disagreement in calibration suggests the necessity for updating prognostic models to maintain currency and generalisability.

scholarly journals Prognostic significance of age in traumatic brain injury

2012 ◽  
Vol 03 (02) ◽  
pp. 131-135 ◽  
Author(s):  
S S Dhandapani ◽  
D Manju ◽  
B S Sharma ◽  
A K Mahapatra
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Surgical Intervention ◽  
Prognostic Significance ◽  
Age Groups ◽  
Unfavorable Outcome ◽  
Confounding Factors ◽  
Age Group ◽  
Independent Association ◽  
The Impact

ABSTRACT Background: Age is a strong prognostic factor following traumatic brain injury (TBI), with discrepancies defining the critical prognostic age threshold. This study was undertaken to determine the impact of various age thresholds on outcome after TBI. Materials and Methods: The ages of patients admitted with TBI were prospectively studied in relation to mode of injury, Glasgow coma score (GCS), CT category and surgical intervention. Mortality was assessed at 1 month, and neurological outcome was assessed at 6 months. Appropriate statistical analyzes (details in article) were performed. Results: Of the total 244 patients enrolled, 144 patients had severe, 38 patients had moderate and 62 patients had mild TBI, respectively. Age had significant association with grade of injury, CT category and surgical intervention (P < 0.01). Mortality at 1 month was significantly associated with increasing age with patients dead at 1 month being 15% for age < 18, 44% for age between 18 and 59 years, and 52% in the age group > 59 years respectively (P < 0.001). Unfavorable outcome showed significant association with an increase in age, every decade (P < 0.001). In multivariate analysis, there was stepwise increase in the odds of unfavorable outcome across age groups centered on 40 years, independent of confounding factors. The adjusted odds ratios for unfavorable outcome with regard to age thresholds 30, 40 and 50 years were 11.3, 53.3 and 1171, respectively (P < 0.005). Moreover, there was significant association of unfavorable outcome with age > 40 years in all subgroups, based on GCS and surgical intervention (P < 0.05). Conclusions: In patients with TBI, age demonstrates independent association with unfavorable outcome at 6 months, in stepwise manner centered on a threshold of 40 years.

Time Interval Between Traumatic Brain Injury And Post Traumatic Epilepsy

2013 ◽  
Vol 21 (2) ◽  
pp. 222-228
Author(s):  
Daniel Garbin Di Luca ◽  
Glenda Corrêa Borges de Lacerda
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
First Year ◽  
Time Interval ◽  
Partial Seizures ◽  
Complex Partial Seizures ◽  
Post Traumatic ◽  
Faster Development ◽  
Post Traumatic Epilepsy ◽  
Traumatic Epilepsy

Introduction. The estimated time interval in which an individual can develop Post Traumatic Epilepsy (PTE) after a traumatic brain injury (TBI) is not clear. Objective. To assess the possible influence of the clinical features in the time interval between TBI and PTE develop­ment. Method. We analyzed retrospectively 400 medical records from a tertiary Brazilian hospital. We selected and reevaluated 50 patients and data was confronted with the time between TBI and PTE devel­opment by a Kaplan-Meier survival analysis. A Cox-hazard regression was also conducted to define the characteristics that could be involved in the latent period of the PTE development. Results. Patients devel­oped PTE especially in the first year (56%). We found a tendency of a faster development of PTE in patients older than 24 years (P<0.0001) and in men (P=0.03). Complex partial seizures evolving to generalized seizures were predominant in patients after moderate (37.7%) and severe (48.8%) TBIs, and simple partial seizures evolving to general­ized seizures in mild TBIs (45.5%). Conclusions. Our data suggest that the first year after a TBI is the most critical period for PTE de­velopment and those males older than 24 years could have a faster development of PTE.

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