title: Combining acute diffusion-weighted imaging and mean transmit time lesion volumes with National Institutes of Health Stroke Scale Score improves the prediction of acute stroke outcome.
abstract: OBJECTIVE
  The purpose of this study was to determine whether acute diffusion-weighted imaging (DWI) and mean transit time (MTT) lesion volumes and presenting National Institutes of Health Stroke Scale (NIHSS) can identify patients with acute ischemic stroke who will have a high probability of good and poor outcomes.
  
  
  METHODS
  Fifty-four patients with acute ischemic stroke who had MRI within 9 hours of symptom onset and 3-month follow-up with modified Rankin scale were evaluated. Acute DWI and MTT lesion volumes and baseline NIHSS scores were calculated. Clinical outcomes were considered good if the modified Rankin Scale was 0 to 2.
  
  
  RESULTS
  The 33 of 54 (61%) patients with good outcomes had significantly smaller DWI lesion volumes (P=0.0001), smaller MTT lesion volumes (P<0.0001), and lower NIHSS scores (P<0.0001) compared with those with poor outcomes. Receiver operating characteristic curves for DWI, MTT, and NIHSS relative to poor outcome had areas under the curve of 0.889, 0.854, and 0.930, respectively, which were not significantly different. DWI and MTT lesion volumes predicted outcome better than mismatch volume or percentage mismatch. All patients with a DWI volume >72 mL (13 of 54) and an NIHSS score >20 (6 of 54) had poor outcomes. All patients with an MTT volume of <47 mL (16 of 54) and an NIHSS score <8 (17 of 54) had good outcomes. Combining clinical and imaging thresholds improved prognostic yield (70%) over clinical (43%) or imaging (54%) thresholds alone (P=0.01).
  
  
  CONCLUSIONS
  Combining quantitative DWI and MTT with NIHSS predicts good and poor outcomes with high probability and is superior to NIHSS alone.
authors.affiliation: Department of Radiology, Massachusetts General Hospital, Boston, Mass 02114, USA.
authors.forename: Albert J
authors.initials: AJ
authors.lastname: Yoo
authors.forename: Elizabeth R
authors.initials: ER
authors.lastname: Barak
authors.forename: William A
authors.initials: WA
authors.lastname: Copen
authors.forename: Shahmir
authors.initials: S
authors.lastname: Kamalian
authors.forename: Leila Rezai
authors.initials: LR
authors.lastname: Gharai
authors.forename: Muhammad A
authors.initials: MA
authors.lastname: Pervez
authors.forename: Lee H
authors.initials: LH
authors.lastname: Schwamm
authors.forename: R Gilberto
authors.initials: RG
authors.lastname: González
authors.forename: Pamela W
authors.initials: PW
authors.lastname: Schaefer
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001921:Brain / Q000473:pathology* / Q000503:physiopathology; D002545:Brain Ischemia / Q000473:pathology* / Q000503:physiopathology; D038524:Diffusion Magnetic Resonance Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008875:Middle Aged; D009316:National Institutes of Health (U.S.); D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D012720:Severity of Illness Index; D018709:Statistics, Nonparametric; D020521:Stroke / Q000473:pathology* / Q000503:physiopathology; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States