Predicting Outcome after Traumatic Brain Injury: Development of Prognostic Scores Based on the IMPACT and the APACHE II

Raj, Rahul; Siironen, Jari; Kivisaari, Riku; Hernesniemi, Juha; Skrifvars, Markus B.

doi:10.1089/neu.2014.3361

Cited by 33 publications

(20 citation statements)

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“…To adjust for case-mix and injury severity differences between the BAC groups, we used the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) prognostic model together with the APACHE II scoring system by using the newly developed IMPACT-APACHE II prediction model. 18 The IMPACT-APACHE II prediction model has shown superior performance in predicting six-month outcome in patients with TBI compared to the individual IMPACT and APACHE II models. 18 We used the IMPACTlab-APACHE II model to achieve best possible case-mix adjustment.…”

Section: Discussionmentioning

confidence: 96%

“…18 The IMPACT-APACHE II prediction model has shown superior performance in predicting six-month outcome in patients with TBI compared to the individual IMPACT and APACHE II models. 18 We used the IMPACTlab-APACHE II model to achieve best possible case-mix adjustment. The IMPACT lab portion of the IMPACTlab-APACHE II prediction model includes several admission characteristics strongly associated with long-term outcome after TBI: age, motor score, pupillary light reaction, Marshall CT score, presence of traumatic subarachnoid hemorrhage, presence of epidural hematoma, hypotensive or hypoxic insults, glucose and hemoglobin concentrations.…”

Section: Discussionmentioning

confidence: 96%

“…However, in the present study we adjusted for both admission characteristics (by the IMPACT model) and early ICU care predictors (by the APACHE II) by using a novel prediction model, the IMPACT-APACHE II prediction model. 18 The IMPACT-APACHE II model diminishes the GCS score-lowering effects of alcohol as it includes both admission GCS score (motor score component) and a GCS score from the first 24 h in the ICU. Furthermore, there were no differences in TBI severity as measured by the Rotterdam CT score between the BAC groups.…”

Section: Raj Et Almentioning

confidence: 99%

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Acute Alcohol Intoxication and Long-Term Outcome in Patients with Traumatic Brain Injury

Raj

Skrifvars

Kivisaari

et al. 2015

Journal of Neurotrauma

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The effect of blood alcohol concentration (BAC) on outcome after traumatic brain injury (TBI) is controversial. We sought to assess the independent effect of positive BAC on long-term outcome in patients with TBI treated in the intensive care unit (ICU). We performed a retrospective analysis of 405 patients with TBI, admitted to the ICU of a large urban Level 1 trauma center between January 2009 and December 2012. Outcome was six-month mortality and unfavorable neurological outcome (defined as a Glasgow Outcome Scale score of 1 [death], 2, [vegetative state], or 3 [severe disability]). Patients were categorized by admission BAC into: no BAC (0.0&; n = 99), low BAC ( < 2.3&; n = 140) and high BAC ( ‡ 2.3&; n = 166). Logistic regression analysis, adjusting for baseline risk and severity of illness, was used to assess the independent effect of BAC on outcome (using the no BAC group as the reference). Overall six-month mortality was 25% and unfavorable outcome was 46%. Multivariate analysis showed low BAC to independently reduce risk of sixmonth mortality compared with no BAC (low BAC adjusted odds ratio [AOR] 0.41, 95% confidence interval [CI] 0.19-0.88, p = 0.021) and high BAC (AOR 0.58, 95% CI 0.29-1.15, p = 0.120). Furthermore, a trend towards reduced risk of six-month unfavorable neurological outcome for patients with positive BAC, compared to patients with negative BAC, was noted, although this did not reach statistical significance (low BAC AOR 0.65, 95% CI 0.34-1.22, p = 0.178, and high BAC AOR 0.59, 95% CI 0.32-1.09, p = 0.089). In conclusion, low admission BAC ( < 2.3&) was found to independently reduce risk of six-month mortality for patients with TBI, and a trend towards improved long-term neurological outcome was found for BAC-positive patients. The role of alcohol as a neuroprotective agent warrants further studies.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 96%

Section: Raj Et Almentioning

confidence: 99%

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Acute Alcohol Intoxication and Long-Term Outcome in Patients with Traumatic Brain Injury

Raj

Skrifvars

Kivisaari

et al. 2015

Journal of Neurotrauma

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“…Fourth, the FICC does not include data on pupillary reactivity, a prognostic factor that is highly relevant in TBI patients [36]. However, previous studies have shown that a prognostic model based only upon age and GCS is accurate for predicting mortality in TBI patients, even in the absence of pupillary reactivity [20, 37]. Fifth, the FICC database does not include data on overall injury severity measures, such as the injury severity score.…”

Section: Discussionmentioning

confidence: 99%

Traumatic brain injury patient volume and mortality in neurosurgical intensive care units: a Finnish nationwide study

Raj

Bendel

Reinikainen

et al. 2016

Scand J Trauma Resusc Emerg Med

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BackgroundDifferences in outcomes after traumatic brain injury (TBI) between neurosurgical centers exist, although the reasons for this are not clear. Thus, our aim was to assess the association between the annual volume of TBI patients and mortality in neurosurgical intensive care units (NICUs). MethodsWe collected data on all patients treated in the five Finnish university hospitals to examine all patients with TBI treated in NICUs in Finland from 2009 to 2012. We used a random effect logistic regression model to adjust for important prognostic factors to assess the independent effect of ICU volume on 6-month mortality. Subgroup analyses were performed for patients with severe TBI, moderate-to-severe TBI, and those who were undergoing mechanical ventilation or intracranial pressure monitoring.ResultsAltogether 2,328 TBI patients were treated during the study period in five NICUs. The annual TBI patient volume ranged from 61 to 206 patients between the NICUs. Univariate analysis, showed no association between the NICUs’ annual TBI patient volume and 6-month mortality (p = 0.063). The random effect model showed no independent association between the NICUs’ annual TBI patient volume and 6-month mortality (OR = 1.000, 95% CI = 0.996–1.004, p = 0.876). None of the pre-defined subgroup analyses indicated any association between NICU volume and patient mortality (p > 0.05 for all).Discussion and ConclusionWe did not find any association between annual TBI patient volume and 6-month mortality in NICUs. These findings should be interpreted taking into account that we only included NICUs, which by international standards all treated high volumes of TBI patients, and that we were not able to study the effect of NICU volume on neurological outcome.Electronic supplementary materialThe online version of this article (doi:10.1186/s13049-016-0320-6) contains supplementary material, which is available to authorized users.

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“…In the present study, the GiViTI calibration belt was used. Even though the GiViTI belt and H‐L test have been found to generate similar results, the different methods of exploring model performance might have contributed to the different results observed.…”

Section: Discussionmentioning

confidence: 96%

Validation of the Norwegian survival prediction model in trauma (NORMIT) in Swedish trauma populations

Ghorbani¹,

Troëng

Brattström

et al. 2020

Journal of British Surgery

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Background Trauma survival prediction models can be used for quality assessment in trauma populations. The Norwegian survival prediction model in trauma (NORMIT) has been updated recently and validated internally (NORMIT 2). The aim of this observational study was to compare the accuracy of NORMIT 1 and 2 in two Swedish trauma populations. Methods Adult patients registered in the national trauma registry during 2014–2016 were eligible for inclusion. The study populations comprised the total national trauma (NT) population, and a subpopulation of patients admitted to a single level I trauma centre (TC). The primary outcome was 30‐day mortality. Model validation included receiver operating characteristic (ROC) curve analysis and GiViTI calibration belts. The calibration was also assessed in subgroups of severely injured patients (New Injury Severity Score (NISS) over 15). Results A total of 26 504 patients were included. Some 18·7 per cent of patients in the NT population and 2·6 per cent in the TC subpopulation were excluded owing to missing data, leaving 21 554 and 3972 respectively for analysis. NORMIT 1 and 2 showed excellent ability to distinguish between survivors and non‐survivors in both populations, but poor agreement between predicted and observed outcome in the NT population with overestimation of survival, including in the subgroup with NISS over 15. In the TC subpopulation, NORMIT 1 underestimated survival irrespective of injury severity, but NORMIT 2 showed good calibration both in the total subpopulation and the subgroup with NISS over 15. Conclusion NORMIT 2 is well suited to predict survival in a Swedish trauma centre population, irrespective of injury severity. Both NORMIT 1 and 2 performed poorly in a more heterogeneous national population of injured patients.

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Predicting Outcome after Traumatic Brain Injury: Development of Prognostic Scores Based on the IMPACT and the APACHE II

Cited by 33 publications

References 45 publications

Acute Alcohol Intoxication and Long-Term Outcome in Patients with Traumatic Brain Injury

Acute Alcohol Intoxication and Long-Term Outcome in Patients with Traumatic Brain Injury

Traumatic brain injury patient volume and mortality in neurosurgical intensive care units: a Finnish nationwide study

Validation of the Norwegian survival prediction model in trauma (NORMIT) in Swedish trauma populations

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