Hyperoxemia and long-term outcome after traumatic brain injury

Raj, Rahul; Bendel, Stepani; Reinikainen, Matti; Kivisaari, Riku; Siironen, Jari; Lång, Maarit; Skrifvars, Markus B.

doi:10.1186/cc12856

Cited by 72 publications

(79 citation statements)

References 51 publications

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“…Second, we were limited to using hospital mortality as the primary end point, which may not be an appropriate outcome measure after TBI. 18,31,34 However, a recent study found low BAC levels also to significantly decrease risk of 6-month mortality. 36 Third, we could not assess the effect of different levels of BAC on outcome.…”

Section: Limitationsmentioning

confidence: 99%

Alcohol and mortality after moderate to severe traumatic brain injury: a meta-analysis of observational studies

Raj¹,

Mikkonen

Siironen³

et al. 2016

JNS

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T raumaTic brain injury (TBI) is defined as an alteration of brain function, or other evidence of brain pathology, caused by an external force.28 Moderate to severe TBI is a major cause of mortality and morbidity in the world. 27 Every year approximately 1.5 million people die and 10 million people are hospitalized due to TBI. 18 This leads to great suffering for patients and their relatives, with huge direct and indirect costs to society. 10,32 It has been estimated that the annual costs of TBI in the US are more than $76.5 billion.9 Numerous clinical trials have failed to demonstrate the efficacy of promising experimental neuroprotective agents despite convincing preclinical data. Over the past 2 decades there have not been clear improvements in the prognosis of patients with TBI. 24,25 Description of the interventionUp to half of all patients with TBI are under the influence of alcohol at the time of injury. 44,49 Clinical studies investigating the role of alcohol (ethanol, CH 3 CH 2 OH, referred to as alcohol in the present study) on the outcome of TBI have been either retrospective or observational, as it probably would be unethical, unless strong evidence can be laid out, to administer alcohol to patients presenting with TBI. This meta-analysis investigates the impact of the presence of blood alcohol concentration (BAC) on outcome after TBI. How Might alcohol work as a Neuroprotective agent?The neuroprotective properties of alcohol have been abbreviatioNs AIS = Abbreviated Injury Scale; BAC = blood alcohol concentration; GCS = Glasgow Coma Scale; NMDAR = N-methyl-d-aspartate receptor; TBI = traumatic brain injury. but with good study homogeneity (I 2 = 36% and 14%). coNclusioNs Positive BAC was significantly associated with lower mortality rates in moderate to severe TBI. Whether this observation is due to selection bias or neuroprotective effects of alcohol remains unknown. Future prospective studies adjusting for TBI heterogeneity is advocated to establish the potential favorable effects of alcohol on outcome after TBI.

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Section: Limitationsmentioning

confidence: 99%

Alcohol and mortality after moderate to severe traumatic brain injury: a meta-analysis of observational studies

Raj¹,

Mikkonen

Siironen³

et al. 2016

JNS

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“…[49][50][51][52][53] Ethnic origin and gender may also be associated with outcome in TBI patients. A meta-analysis found slightly poorer quality of life in female compared to male TBI survivors, 54 although this remains controversial, as the contrary has also been reported.…”

Section: Demographicsmentioning

confidence: 99%

“…142,143 Th is is a particular problem in patients with moderate to severe TBI, because as many as one third of hospital survivors die within six months following hospital discharge. 29,50 Moreover, diff erences in discharge policies vary not only between countries but also within them, and adjusting for those diff erences with a fi xedtime outcome measure is absolutely essential, lest hospitals feel pressured to discharge severely ill patients more rapidly to avoid them from dying to keep up their stats. 140,143 Furthermore, using mortality rates as the primary outcome measure aft er TBI neglects important aspects of patient outcome, such as functional and neurological recovery and quality of life.…”

Section: Quality Auditsmentioning

confidence: 99%

“…It is widely known that age is one of the strongest predictors of outcome aft er TBI. 29,[49][50][51][52][53] Th e relationship between older age and poor outcome has been suggested to be linear. 49 Th us, the increasing age of TBI patients is most likely a primary reason why poor outcome was more frequently noted in the present study.…”

Section: Pa Ent Outcome a Er Trauma C Brain Injurymentioning

confidence: 99%

“…142,143 Second, a substantial number of TBI patients die following hospital discharge, biasing outcome rates. 29,[50][51][52] Supporting these concerns, Study II showed a 14-day mortality rate of 11%, compared to a 33% six-month mortality rate in Study IV, for a staggering 200% increase. Th us, hospital mortality severely underestimates mortality rates aft er TBI and should be discouraged as a primary outcome measure in TBI research.…”

Section: Pa Ent Outcome a Er Trauma C Brain Injurymentioning

confidence: 99%

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Prognostic models in traumatic brain injury

Raj

2015

Acta Anaesthesiol Scand

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Background Prognostic models are important tools for heterogeneity adjustment in traumatic brain injury (TBI). Prognoses after TBI have been particularly challenging to predict, with limited availability of robust prognostic models. TBI patients are by definition trauma patients, and often treated in the intensive care unit (ICU). Several prognostic models for ICU and trauma patients have been developed, although their applicability in patients with TBI is uncertain. Recently, however, some new prognostic models specifically designed for patients with TBI were introduced. Still, the optimal type of prognostic model in TBI remains unknown. Aim This aims to investigate the applicability of different types of prognostic models in patients with TBI and to develop novel models with enhanced performance to previous models, focusing on long‐ term outcome prediction. Methods Four patient databases of patients with TBI treated in the ICU were used to validate three TBI specific models, two computerized tomography (CT) scoring systems, one trauma scoring system, and three intensive care scoring systems. Models were validated by assessing their discrimination using area under the curve (AUC), calibration, and explanatory variation. Logistic regression was used for model customization and development. Models were internally validated using a resample bootstrap technique or a split‐sample technique. Primary outcome was 6‐month mortality and unfavorable neurological outcome by the Glasgow Outcome Scale. 30‐day in‐hospital mortality was used for the trauma scoring system. Results Study populations ranged from 342 to 9915 patients. The TBI models showed the best performance with AUCs between 0.80 and 0.85, followed by the intensive care scoring systems and the CT scores with AUCs between 0.68 to 0.80 and 0.63 to 0.70, respectively. Most models showed poor calibration, although good calibration was achieved following customization. The trauma scoring system exhibited modest to good discrimination (AUC 0.76–0.89) for short‐term mortality prediction but poor calibration. Several new prognostic models, with statistically significant superior performance to previous models were created, among them a combined TBI‐ICU model (‘IMPACT‐APACHE’) and a novel CT scoring system (‘The Helsinki CT score’). Using a TBI‐specific model, based on admission characteristics, up to 40% of the patient's final long‐term outcome could be predicted. Conclusion The TBI models showed superior predictive performance to the intensive care and trauma scoring systems, showing that TBI patients are a highly specific population in the trauma and ICU setting. Thus, the use of a TBI‐specific model is advocated in the setting of TBI. The newly proposed models were found to be significant improvements over previous models, but require external validation to show generalizability.

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