Contrast extravasation on CT angiography predicts hematoma expansion and mortality in acute traumatic subdural hemorrhage.

BACKGROUND AND PURPOSE: The presence of active contrast extravasation at CTA predicts hematoma expansion and in-hospital mortality in patients with nontraumatic intracerebral hemorrhage. This study aims to determine the frequency and predictive value of the contrast extravasation in patients with aSDH. MATERIALS AND METHODS: We retrospectively reviewed 157 consecutive patients who presented to our emergency department over a 9-year period with aSDH and underwent CTA at admission and a follow-up NCCT within 48 hours. Two experienced readers, blinded to clinical data, reviewed the CTAs to assess for the presence of contrast extravasation. Medical records were reviewed for baseline clinical characteristics and in-hospital mortality. aSDH maximum width in the axial plane was measured on both baseline and follow-up NCCTs, with hematoma expansion defined as >20% increase from baseline. RESULTS: Active contrast extravasation was identified in 30 of 199 discrete aSDHs (15.1%), with excellent interobserver agreement (κ = 0.80; 95% CI, 0.7-0.9). The presence of contrast extravasation indicated a significantly increased risk of hematoma expansion (odds ratio, 4.5; 95% CI, 2.0-10.1; P = .0001) and in-hospital mortality (odds ratio, 7.6; 95% CI, 2.6-22.3; P = 0.0004). In a multivariate analysis controlled for standard risk factors, the presence of contrast extravasation was an independent predictor of aSDH expansion (P = .001) and in-hospital mortality (P = .0003). CONCLUSIONS: Contrast extravasation stratifies patients with aSDH into those at high risk and those at low risk of hematoma expansion and in-hospital mortality. This distinction could affect patient treatment, clinical trial selection, and possible surgical intervention.

Location-weighted CTP analysis predicts early motor improvement in stroke: a preliminary study.

OBJECTIVE: To develop multivariate models for prediction of early motor deficit improvement in acute stroke patients with focal extremity paresis, using admission clinical and imaging data. METHODS: Eighty consecutive patients with motor deficit due to first-ever unilateral stroke underwent CT perfusion (CTP) within 9 hours of symptom onset. Limb paresis was prospectively assessed using admission and discharge NIH Stroke Scale (NIHSS) scoring. CTP scans were coregistered to the MNI-152 brain space and subsegmented to 146 pairs of cortical/subcortical regions based on preset atlases. Stepwise multivariate binary logistic regressions were performed to determine independent clinical and imaging predictors of paresis improvement. RESULTS: The rates of early motor deficit improvement were 18/49 (37%), 15/42 (36%), 8/25 (32%), and 7/23 (30%) for the right arm, right leg, left arm, and left leg, respectively. Admission NIHSS was the only independent clinical predictor of early limb motor deficit improvement. Relative CTP values of the inferior frontal lobe white matter, lower insular cortex, superior temporal gyrus, retrolenticular portion of internal capsule, postcentral gyrus, precuneus parietal gyri, putamen, and caudate nuclei were also independent predictors of motor improvement of different limbs. The multivariate predictive models of motor function improvement for each limb had 84%-92% accuracy, 79%-100% positive predictive value, 75%-94% negative predictive value, 83%-88% sensitivity, and 80%-100% specificity. CONCLUSIONS: We developed pilot multivariate models to predict early motor functional improvement in acute stroke patients using admission NIHSS and atlas-based location-weighted CTP data. These models serve as a "proof-of-concept" for prospective location-weighted imaging prediction of clinical outcome in acute stroke.

Predicting language improvement in acute stroke patients presenting with aphasia: a multivariate logistic model using location-weighted atlas-based analysis of admission CT perfusion scans.

BACKGROUND AND PURPOSE: Prediction of functional outcome immediately after stroke onset can guide optimal management. Most prognostic grading scales to date, however, have been based on established global metrics such as total NIHSS score, admission infarct volume, or intracranial occlusion on CTA. Our purpose was to construct a more focused, location-weighted multivariate model for the prediction of early aphasia improvement, based not only on traditional clinical and imaging parameters, but also on atlas-based structure/function correlation specific to the clinical deficit, using CT perfusion imaging. MATERIALS AND METHODS: Fifty-eight consecutive patients with aphasia due to first-time ischemic stroke of the left hemisphere were included. Language function was assessed on the basis of the patients admission and discharge NIHSS scores and clinical records. All patients had brain CTP and CTA within 9 hours of symptom onset. For image analysis, all CTPs were automatically co-registered to MNI-152 brain space and parcellated into mirrored cortical and subcortical regions. Multiple logistic regression analysis was used to find independent imaging and clinical predictors of language recovery. RESULTS: By the time of discharge, 21 (36%) patients demonstrated improvement of language. Independent factors predicting improvement in language included rCBF of the angular gyrus GM (BA 39) and the lower third of the insular ribbon, proximal cerebral artery occlusion on admission CTA, and aphasia score on the admission NIHSS examination. Using these 4 variables, we developed a multivariate logistic regression model that could estimate the probability of early improvement in aphasia and predict functional outcome with 91% accuracy. CONCLUSIONS: An imaging-based location-weighted multivariate model was developed to predict early language improvement of patients with aphasia by using admission data collected within 9 hours of stroke onset. This pilot model should be validated in a larger, prospective study; however, the semiautomated atlas-based analysis of brain CTP, along with the statistical approach, could be generalized for prediction of other outcome measures in patients with stroke.