title: A simple brain atrophy measure improves the prediction of malignant middle cerebral artery infarction by acute DWI lesion volume.
abstract: In patients with malignant middle cerebral artery infarction (MMI) decompressive surgery within 48 h improves functional outcome. In this respect, early identification of patients at risk of developing MMI is crucial. While the acute diffusion weighted imaging (DWI) lesion volume was found to predict MMI with high predictive values, the potential impact of preexisting brain atrophy on the course of space-occupying middle cerebral artery (MCA) infarction and the development of MMI remains unclear. We tested the hypothesis that the combination of the acute DWI lesion volume with simple measures of brain atrophy improves the early prediction of MMI. Data from a prospective, multicenter, observational study, which included patients with acute middle cerebral artery main stem occlusion studied by MRI within 6 h of symptom onset, was analyzed retrospectively. The development of MMI was defined according to the European randomized controlled trials of decompressive surgery. Acute DWI lesion volume, as well as brain and cerebrospinal fluid volume (CSF) were delineated. The intercaudate distance (ICD) was assessed as a linear brain atrophy marker by measuring the hemi-ICD of the intact hemisphere to account for local brain swelling. Binary logistic regression analysis was used to identify significant predictors of MMI. Cut-off values were determined by Classification and Regression Trees analysis. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the resulting models were calculated. Twenty-one (18 %) of 116 patients developed a MMI. Malignant middle cerebral artery infarctions patients had higher National Institutes of Health Stroke Scale scores on admission and presented more often with combined occlusion of the internal carotid artery and MCA. There were no differences in brain and CSF volume between the two groups. Diffusion weighted imaging lesion volume was larger (p < 0.001), while hemi-ICD was smaller (p = 0.029) in MMI patients. Inclusion of hemi-ICD improved the prediction of MMI. Best cut-off values to predict the development of MMI were DWI lesion volume > 87 ml and hemi-ICD ≤ 9.4 mm. The addition of hemi-ICD to the decision tree strongly increased PPV (0.93 vs. 0.70) resulting in a reduction of false positive findings from 7/23 (30 %) to 1/15 (7 %), while there were only slight changes in specificity, sensitivity and NPV. The absolute number of correct classifications increased by 4 (3.4 %). The integration of hemi-ICD as a linear marker of brain atrophy, that can easily be assessed in an emergency setting, may improve the prediction of MMI by lesion volume based predictive models.
authors.affiliation: Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, Universitätsklinikum Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany, c.beck@uke.de.
authors.forename: Christoph
authors.initials: C
authors.lastname: Beck
authors.forename: Anna
authors.initials: A
authors.lastname: Kruetzelmann
authors.forename: Nils D
authors.initials: ND
authors.lastname: Forkert
authors.forename: Eric
authors.initials: E
authors.lastname: Juettler
authors.forename: Oliver C
authors.initials: OC
authors.lastname: Singer
authors.forename: Martin
authors.initials: M
authors.lastname: Köhrmann
authors.forename: Jan F
authors.initials: JF
authors.lastname: Kersten
authors.forename: Jan
authors.initials: J
authors.lastname: Sobesky
authors.forename: Christian
authors.initials: C
authors.lastname: Gerloff
authors.forename: Jens
authors.initials: J
authors.lastname: Fiehler
authors.forename: Peter D
authors.initials: PD
authors.lastname: Schellinger
authors.forename: Joachim
authors.initials: J
authors.lastname: Röther
authors.forename: Götz
authors.initials: G
authors.lastname: Thomalla
mesh_terms: D000368:Aged; D001284:Atrophy / Q000209:etiology; D001921:Brain / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D020244:Infarction, Middle Cerebral Artery / Q000150:complications* / Q000175:diagnosis*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests