54 research outputs found

    Diffuse axonal injury after traumatic brain injury is a prognostic factor for functional outcome:A systematic review and meta-analysis

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    Objective: To determine the prognosis of adult patients with traumatic brain injury (TBI) and diffuse axonal injury (DAI).Methods: Online search (PubMed, Embase and Ovid Science Direct) of articles providing information about outcome in (1) patients with DAI in general, (2) DAI vs. non-DAI, (3) related to magnetic resonance imaging (MRI) classification and (4) related to lesion location/load. A reference check and quality assessment were performed.Results: A total of 32 articles were included. TBI patients with DAI had a favourable outcome in 62%. The risk of unfavourable outcome in TBI with DAI was three times higher than in TBI without DAI. Odds ratio (OR) for unfavourable outcome was 2.9 per increase of DAI grade on MRI. Lesions located in the corpus callosum were associated with an unfavourable outcome. Other specific lesion locations and lesions count showed inconsistent results regarding outcome. Lesion volume was predictive for outcome only on apparent diffusion coefficient and fluid attenuation inversion recovery MRI sequences.Conclusions: Presence of DAI on MRI in patients with TBI results in a higher chance of unfavourable outcome. With MRI grading, OR for unfavourable outcome increases threefold with every grade. Lesions in the corpus callosum in particular are associated with an unfavourable outcome

    Risk of Intracranial Complications in Minor Head Injury:The Role of Loss of Consciousness and Post-Traumatic Amnesia in a Multi-Center Observational Study

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    Various guidelines for minor head injury focus on patients with a Glasgow Coma Scale (GCS) score of 13-15 and loss of consciousness (LOC) or post-traumatic amnesia (PTA), while clinical management for patients without LOC or PTA is often unclear. We aimed to investigate the effect of presence and absence of LOC or PTA on intracranial complications in minor head injury. A prospective multi-center cohort study of all patients with blunt head injury and GCS score of 15 was conducted at six Dutch centers between 2015 and 2017. Five centers used the national guideline and one center used a local guideline-both based on the CT in Head Injury Patients (CHIP) prediction model-to identify patients in need of a computed tomography (CT) scan. We studied the presence of traumatic findings and neurosurgical interventions in patients with and without LOC or PTA. In addition, we assessed the association of LOC and PTA with traumatic findings with logistic regression analysis and the additional predictive value of LOC and PTA compared with other risk factors in the CHIP model. Of 3914 patients, 2249 (58%) experienced neither LOC nor PTA and in 305 (8%) LOC and PTA was unknown. Traumatic findings were present in 153 of 1360 patients (11%) with LOC or PTA and in 67 of 2249 patients (3%) without LOC and PTA. Five patients without LOC and PTA had potential neurosurgical lesions and one patient underwent a neurosurgical intervention. LOC and PTA were strongly associated with traumatic findings on CT, with adjusted odds ratios of 2.9 (95% confidence interval [CI] 2.2-3.8) and 3.5 (95% CI 2.7-4.6), respectively. To conclude, patients who had minor head injury with neither LOC nor PTA are at risk of intracranial complications. Clinical guidelines should include clinical management for patients without LOC and PTA, and they should include LOC and PTA as separate risk factors rather than as diagnostic selection criteria

    Update of the CHIP (CT in Head Injury Patients) decision rule for patients with minor head injury based on a multicenter consecutive case series

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    OBJECTIVE: To update the existing CHIP (CT in Head Injury Patients) decision rule for detection of (intra)cranial findings in adult patients following minor head injury (MHI).METHODS: The study is a prospective multicenter cohort study in the Netherlands. Consecutive MHI patients of 16 years and older were included. Primary outcome was any (intra)cranial traumatic finding on computed tomography (CT). Secondary outcomes were any potential neurosurgical lesion and neurosurgical intervention. The CHIP model was validated and subsequently updated and revised. Diagnostic performance was assessed by calculating the c-statistic.RESULTS: Among 4557 included patients 3742 received a CT (82%). In 383 patients (8.4%) a traumatic finding was present on CT. A potential neurosurgical lesion was found in 73 patients (1.6%) with 26 (0.6%) patients that actually had neurosurgery or died as a result of traumatic brain injury. The original CHIP underestimated the risk of traumatic (intra)cranial findings in low-predicted-risk groups, while in high-predicted-risk groups the risk was overestimated. The c-statistic of the original CHIP model was 0.72 (95% CI 0.69-0.74) and it would have missed two potential neurosurgical lesions and one patient that underwent neurosurgery. The updated model performed similar to the original model regarding traumatic (intra)cranial findings (c-statistic 0.77 95% CI 0.74-0.79, after crossvalidation c-statistic 0.73). The updated CHIP had the same CT rate as the original CHIP (75%) and a similar sensitivity (92 versus 93%) and specificity (both 27%) for any traumatic (intra)cranial finding. However, the updated CHIP would not have missed any (potential) neurosurgical lesions and had a higher sensitivity for (potential) neurosurgical lesions or death as a result of traumatic brain injury (100% versus 96%).CONCLUSIONS: Use of the updated CHIP decision rule is a good alternative to current decision rules for patients with MHI. In contrast to the original CHIP the update identified all patients with (potential) neurosurgical lesions without increasing CT rate.</p

    Development of a quality indicator set to measure and improve quality of ICU care for patients with traumatic brain injury.

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    BACKGROUND: We aimed to develop a set of quality indicators for patients with traumatic brain injury (TBI) in intensive care units (ICUs) across Europe and to explore barriers and facilitators for implementation of these quality indicators. METHODS: A preliminary list of 66 quality indicators was developed, based on current guidelines, existing practice variation, and clinical expertise in TBI management at the ICU. Eight TBI experts of the Advisory Committee preselected the quality indicators during a first Delphi round. A larger Europe-wide expert panel was recruited for the next two Delphi rounds. Quality indicator definitions were evaluated on four criteria: validity (better performance on the indicator reflects better processes of care and leads to better patient outcome), feasibility (data are available or easy to obtain), discriminability (variability in clinical practice), and actionability (professionals can act based on the indicator). Experts scored indicators on a 5-point Likert scale delivered by an electronic survey tool. RESULTS: The expert panel consisted of 50 experts from 18 countries across Europe, mostly intensivists (N = 24, 48%) and neurosurgeons (N = 7, 14%). Experts agreed on a final set of 42 indicators to assess quality of ICU care: 17 structure indicators, 16 process indicators, and 9 outcome indicators. Experts are motivated to implement this finally proposed set (N = 49, 98%) and indicated routine measurement in registries (N = 41, 82%), benchmarking (N = 42, 84%), and quality improvement programs (N = 41, 82%) as future steps. Administrative burden was indicated as the most important barrier for implementation of the indicator set (N = 48, 98%). CONCLUSIONS: This Delphi consensus study gives insight in which quality indicators have the potential to improve quality of TBI care at European ICUs. The proposed quality indicator set is recommended to be used across Europe for registry purposes to gain insight in current ICU practices and outcomes of patients with TBI. This indicator set may become an important tool to support benchmarking and quality improvement programs for patients with TBI in the future

    External validation of computed tomography decision rules for minor head injury: Prospective, multicentre cohort study in the Netherlands

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    Objective To externally validate four commonly used rules in computed tomography (CT) for minor head injury. Design Prospective, multicentre cohort study. Setting Three university and six non-university hospitals in the Netherlands. Participants Consecutive adult patients aged 16 years and over who presented with minor head injury at the emergency department with a Glasgow coma scale score of 13-15 between March 2015 and December 2016. Main outcome measures The primary outcome was any intracrania

    Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

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    Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations

    Variation in Structure and Process of Care in Traumatic Brain Injury: Provider Profiles of European Neurotrauma Centers Participating in the CENTER-TBI Study.

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    INTRODUCTION: The strength of evidence underpinning care and treatment recommendations in traumatic brain injury (TBI) is low. Comparative effectiveness research (CER) has been proposed as a framework to provide evidence for optimal care for TBI patients. The first step in CER is to map the existing variation. The aim of current study is to quantify variation in general structural and process characteristics among centers participating in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. METHODS: We designed a set of 11 provider profiling questionnaires with 321 questions about various aspects of TBI care, chosen based on literature and expert opinion. After pilot testing, questionnaires were disseminated to 71 centers from 20 countries participating in the CENTER-TBI study. Reliability of questionnaires was estimated by calculating a concordance rate among 5% duplicate questions. RESULTS: All 71 centers completed the questionnaires. Median concordance rate among duplicate questions was 0.85. The majority of centers were academic hospitals (n = 65, 92%), designated as a level I trauma center (n = 48, 68%) and situated in an urban location (n = 70, 99%). The availability of facilities for neuro-trauma care varied across centers; e.g. 40 (57%) had a dedicated neuro-intensive care unit (ICU), 36 (51%) had an in-hospital rehabilitation unit and the organization of the ICU was closed in 64% (n = 45) of the centers. In addition, we found wide variation in processes of care, such as the ICU admission policy and intracranial pressure monitoring policy among centers. CONCLUSION: Even among high-volume, specialized neurotrauma centers there is substantial variation in structures and processes of TBI care. This variation provides an opportunity to study effectiveness of specific aspects of TBI care and to identify best practices with CER approaches
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