Prediction of Malignant Course in MCA Infarction by PET and Microdialysis
- 1 September 2003
- journal article
- clinical trial
- Published by Wolters Kluwer Health in Stroke
- Vol. 34 (9) , 2152-2158
- https://doi.org/10.1161/01.str.0000083624.74929.32
Abstract
To predict malignant course in patients with large middle cerebral artery (MCA) infarction, we combined PET imaging and neuromonitoring, including microdialysis. Thirty-four patients with stroke of >50% of the MCA territory in early cerebral CT scan were included. Probes for microdialysis and measurement of intracranial pressure and tissue oxygen pressure (Pto2) were placed into the ipsilateral frontal lobe. PET was performed with 11C-flumazenil to assess CBF and irreversible neuronal damage. PET measurements within 24 hours after stroke showed larger volumes of ischemic core (mean, 144.5 versus 62.2 cm3) and larger volumes of irreversible neuronal damage (157.9 versus 47.0 cm3) in patients with malignant course (ie, edema formation with midline shift) than in patients with benign course. Mean cerebral blood flow values within the ischemic core were significantly lower and the volume of the ischemic penumbra was smaller in the malignant than in the benign group. In patients with malignant course, cerebral perfusion pressure dropped to <50 to 60 mm Hg 22 to 72 hours (mean, 52.0 hours) after onset of symptoms; subsequently, Pto2 dropped and glutamate increased, indicating secondary ischemia. Maximal changes in the monitored variables reached significant levels for glutamate, aspartate, GABA, glycerol, lactate-to-pyruvate ratio, hypoxanthine, intracranial pressure, cerebral perfusion pressure, and Pto2. PET allowed prediction of malignant MCA infarction within the time window suggested for hemicraniectomy. Neuromonitoring helped to classify the clinical courses by characterizing pathophysiological sequelae of malignant edema formation. In contrast to PET, however, it did not predict fatal outcome early enough for successful implementation of invasive therapies.Keywords
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