Supplementary Material for: Diagnostic Accuracy of CT Perfusion Imaging for Detecting Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

<b><i>Background:</i></b> The aim of the current study was to determine the sensitivity and specificity of CT perfusion (CTP) for the detection of ischemic stroke<b> </b>by performing a systematic review and meta-analysis of published reports. <b><i>Methods:</i></b> We searched PubMed, Embase and the Cochrane library using the terms ‘perfusion computed tomography', ‘ischemic stroke' and synonyms. We included studies that: (1) reported original data, (2) studied the diagnostic value of CTP for detecting ischemic stroke, (3) used MRI-DWI, follow-up MRI or follow-up CT as the reference standard, (4) included at least 10 patients who were suspected of ischemic stroke, and (5) reported the number of true positives, true negatives, false positives and false negatives for the diagnosis of ischemic stroke. <b><i>Results:</i></b> Fifteen studies were finally included in the current review with a total of 1,107 patients. A pooled analysis resulted in a sensitivity of 80% (95% confidence interval, CI: 72-86%) and a specificity of 95% (95% CI: 86-98%). Almost two thirds of the false negatives were due to small lacunar infarcts; the remaining false negatives were mostly due to limited coverage. <b><i>Conclusions:</i></b> The current systematic review shows that CTP has a high sensitivity and a very high specificity for detecting infarcts

Supplementary Material for: The Prognostic Value of CT Angiography and CT Perfusion in Acute Ischemic Stroke

<br><strong><em>Background:</em></strong> CT angiography (CTA) and CT perfusion (CTP) are important diagnostic tools in acute ischemic stroke. We investigated the prognostic value of CTA and CTP for clinical outcome and determined whether they have additional prognostic value over patient characteristics and non-contrast CT (NCCT). <b><i>Methods:</i></b> We included 1,374 patients with suspected acute ischemic stroke in the prospective multicenter Dutch acute stroke study. Sixty percent of the cohort was used for deriving the predictors and the remaining 40% for validating them. We calculated the predictive values of CTA and CTP predictors for poor clinical outcome (modified Rankin Scale score 3-6). Associations between CTA and CTP predictors and poor clinical outcome were assessed with odds ratios (OR). Multivariable logistic regression models were developed based on patient characteristics and NCCT predictors, and subsequently CTA and CTP predictors were added. The increase in area under the curve (AUC) value was determined to assess the additional prognostic value of CTA and CTP. Model validation was performed by assessing discrimination and calibration. <b><i>Results:</i></b> Poor outcome occurred in 501 patients (36.5%). Each of the evaluated CTA measures strongly predicted outcome in univariable analyses: the positive predictive value (PPV) was 59% for Alberta Stroke Program Early CT Score (ASPECTS) ≤7 on CTA source images (OR 3.3; 95% CI 2.3-4.8), 63% for presence of a proximal intracranial occlusion (OR 5.1; 95% CI 3.7-7.1), 66% for poor leptomeningeal collaterals (OR 4.3; 95% CI 2.8-6.6), and 58% for a >70% carotid or vertebrobasilar stenosis/occlusion (OR 3.2; 95% CI 2.2-4.6). The same applied to the CTP measures, as the PPVs were 65% for ASPECTS ≤7 on cerebral blood volume maps (OR 5.1; 95% CI 3.7-7.2) and 53% for ASPECTS ≤7 on mean transit time maps (OR 3.9; 95% CI 2.9-5.3). The prognostic model based on patient characteristics and NCCT measures was highly predictive for poor clinical outcome (AUC 0.84; 95% CI 0.81-0.86). Adding CTA and CTP predictors to this model did not improve the predictive value (AUC 0.85; 95% CI 0.83-0.88). In the validation cohort, the AUC values were 0.78 (95% CI 0.73-0.82) and 0.79 (95% CI 0.75-0.83), respectively. Calibration of the models was satisfactory. <b><i>Conclusions:</i></b> In patients with suspected acute ischemic stroke, admission CTA and CTP parameters are strong predictors of poor outcome and can be used to predict long-term clinical outcome. In multivariable prediction models, however, their additional prognostic value over patient characteristics and NCCT is limited in an unselected stroke population