RESUMO
A new functional deficit caused by a stroke can be understood as a situation of uncertainty that has to prompt deficit discovery and subsequent incorporation into an altered self-perception. Anosognosia for visual field deficits is frequent after stroke. For hemiplegia, patients' performance in a riddle test provided evidence that the inability to generate and adjust beliefs in face of uncertainty contributes to anosognosia for hemiplegia. In this prospective study, the same riddles are used in patients with homonymous hemianopia due to a first-ever stroke in the posterior cerebral artery territory and in an age-matched control cohort. The riddles create a situation of uncertainty that is resolved with five successive clues which progressively delimit the target word. After each clue, patients have to guess the target word and rate their confidence in the answer's correctness. Patients were tested once during the hospital stay. According to the Bisiach score for anosognosia, 12 out of 29 patients were unaware of their visual field deficits. All patients with anosognosia for hemianopia had right hemisphere lesions. Patients with and without anosognosia did not differ significantly in global cognitive impairment, mental flexibility or memory function. Importantly, patients with anosognosia showed higher confidence ratings than patients without anosognosia and controls in the first two clues (situations of uncertainty). This was demonstrated by a significant interaction effect in a mixed ANOVA with the factors group (anosognosia, nosognosia, controls) and riddle clues. An exploratory lesion subtraction analysis showed a high proportion of deficit unawareness in patients with lesions in the right fusiform and (para)hippocampal gyri. Our findings suggest that overconfidence in situations of uncertainty might contribute to the appearance of anosognosia for hemianopia. Because this has been demonstrated before in anosognosia for hemiplegia, we suggest that overconfidence is a supra-modal contributor to deficit unawareness.
RESUMO
Perfusion CT is established to aid selection of patients with proximal intracranial vessel occlusion for thrombectomy in the extended time window. Selection is mostly based on simple thresholding of perfusion parameter maps, which, however, does not exploit the full information hidden in the high-dimensional perfusion data. We implemented a multiparametric mass-univariate logistic model to predict tissue outcome based on data from 405 stroke patients with acute proximal vessel occlusion in the anterior circulation who underwent mechanical thrombectomy. Input parameters were acute multimodal CT imaging (perfusion, angiography, and non-contrast) as well as basic demographic and clinical parameters. The model was trained with the knowledge of recanalization status and final infarct localization. We found that perfusion parameter maps (CBF, CBV, and Tmax) were sufficient for tissue outcome prediction. Compared with single-parameter thresholding-based models, our logistic model had comparable volumetric accuracy, but was superior with respect to topographical accuracy (AUC of receiver operating characteristic). We also found higher spatial accuracy (Dice index) in an independent internal but not external cross-validation. Our results highlight the value of perfusion data compared with non-contrast CT, CT angiography and clinical information for tissue outcome-prediction. Multiparametric logistic prediction has high potential to outperform the single-parameter thresholding-based approach. In the future, the combination of tissue and functional outcome prediction might provide an individual biomarker for the benefit from mechanical thrombectomy in acute stroke care.
