Quantification of midline shift as a predictor of poor outcome following head injury

Quattrocchi, Keith B.; Prasad, P. Rajendra; Willits, Neil H.; Wagner, Franklin C.

doi:10.1016/0090-3019(91)90069-l

Cited by 53 publications

(38 citation statements)

References 36 publications

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“…For instance, Quattrocchi et al (1991) used logistic regression to analyze CT scans and concluded that 34% of cases with intracranial hemorrhage revealed by CT scans acquired early after trauma were associated with poor outcome, compared to 61% of cases which additionally exhibited midline shifts and to 88% of cases where the midline shift was ‘out of proportion’ to the intracranial hemorrhage. By contrast, a logistic regression analysis undertaken to determine the incidence of traumatic subarachnoid hemorrhage in TBI patients as detected by CT (Mattioli et al, 2003) showed that this condition and its grading alone do not assume significance in predicting unfavorable outcome.…”

Section: Statistical Models For Tbi Outcome Prediction Using Structurmentioning

confidence: 99%

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction

Irimia

Wang

Aylward

et al. 2012

NeuroImage: Clinical

108

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Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.

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Section: Statistical Models For Tbi Outcome Prediction Using Structurmentioning

confidence: 99%

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction

Irimia

Wang

Aylward

et al. 2012

NeuroImage: Clinical

108

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show abstract

“…A search on "head injury," "CT scan," and "prognosis" resulted in 27 articles, and a search on "head injury," "CT scan," and "outcome" in 55 articles. Only English language literature and papers reporting on adult head injury were reviewed.…”

Section: Search Processmentioning

confidence: 99%

Computed Tomography Scan Features

2000

Journal of Neurotrauma

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A. Which feature of the parameter is supported by class I and strong class II evidence and has at least a 70% positive predictive value in severe head injury? 1. Presence of abnormalities on initial CT examination. 2. CT classification. 3. Compressed or absent basal cisterns. 4. Traumatic subarachnoid hemorrhage (tSAH): • Blood in the basal cisterns. • Extensive tSAH. B. Parameter measurement:1. How should it be measured?• Compressed or absent basal cisterns measured at the midbrain level.• tSAH should be noted in the basal cisterns or over the convexity.• Midline shift should be measured at the level of the septum pellucidum.2. When should it be measured?• Within 12 h of injury. The full extent of intracranial pathology may, however, not be disclosed on early CT examination.3. Who should measure it?• A neuroradiologist or other qualified physician, experienced in reading CT scans of the brain. II. OVERVIEWThe classical clinical features with prognostic significance in patients with severe TBI include age, GCS, pupil reactivity, brainstem reflexes, and the presence of posttraumatic hypotension or hypoxemia. Many patients today arrive in the hospital already intubated, paralyzed, and ventilated. Accurate estimation of the GCS and changes in GCS in the initial hours after trauma are, therefore, often difficult to obtain. In a recent survey on patients with severe and moderate head injury conducted by the European Brain Injury Consortium, the full GCS was only testable in 56% of patients on admission to neurosurgery.2 Prognostic features based on the results of technical examinations are therefore needed. CT scanning is routinely performed in all patients with severe TBI and provides information with im-portant therapeutic implications for operative intervention or indications for ICP monitoring, and may provide information concerning prognostic significance.

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“…2–9 This research has focused primarily on unfavorable outcome without considering factors that might predict awakening. 10–12 …”

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confidence: 99%

Neuroanatomical predictors of awakening in acutely comatose patients

Kowalski¹,

Buitrago²,

Duckworth³

et al. 2015

Annals of Neurology

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Objective Lateral brain displacement has been associated with loss of consciousness and poor outcome in a range of acute neurologic disorders. We studied the association between lateral brain displacement and awakening from acute coma. Methods This prospective observational study included all new onset coma patients admitted to the Neurosciences Critical Care Unit (NCCU) over 12 consecutive months. Head computed tomography (CT) scans were analyzed independently at coma onset, after awakening, and at follow-up. Primary outcome measure was awakening, defined as the ability to follow commands before hospital discharge. Secondary outcome measures were discharge Glasgow Coma Scale (GCS), modified Rankin Scale, Glasgow Outcome Scale, and hospital and NCCU lengths of stay. Results Of the 85 patients studied, the mean age was 58 ± 16 years, 51% were female, and 78% had cerebrovascular etiology of coma. Fifty-one percent of patients had midline shift on head CT at coma onset and 43 (51%) patients awakened. In a multivariate analysis, independent predictors of awakening were younger age (odds ratio [OR] = 1.039, 95% confidence interval [CI] = 1.002–1.079, p = 0.040), higher GCS score at coma onset (OR = 1.455, 95% CI = 1.157–1.831, p = 0.001), nontraumatic coma etiology (OR = 4.464, 95% CI = 1.011–19.608, p = 0.048), lesser pineal shift on follow-up CT (OR = 1.316, 95% CI = 1.073–1.615, p = 0.009), and reduction or no increase in pineal shift on follow-up CT (OR = 11.628, 95% CI = 2.207–62.500, p = 0.004). Interpretation Reversal and/or limitation of lateral brain displacement are associated with acute awakening in comatose patients. These findings suggest objective parameters to guide prognosis and treatment in patients with acute onset of coma.

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Quantification of midline shift as a predictor of poor outcome following head injury

Cited by 53 publications

References 36 publications

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction

Computed Tomography Scan Features

Neuroanatomical predictors of awakening in acutely comatose patients

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