title: Prediction of coma and anisocoria based on computerized tomography findings in patients with supratentorial intracerebral hemorrhage.
abstract: OBJECTIVE
  Coma and anisocoria are the two common signs of a crucial state of neurological dysfunction. The ability to forecast the occurrence of these conditions would help clinicians make clinical risk assessments and decisions.
  
  
  METHODS
  From October 2006 to September 2008, 118 patients with supratentorial intracerebral hemorrhage (SICH) were enrolled in this retrospective investigation. Patients were distributed into 3 groups according to occurrence of the signs of coma and/or anisocoria in the observation unit during a 30-day period. Group 1 included 52 patients who had normal or impaired consciousness, group 2 included 27 patients who had coma with no anisocoria and group 3 consisted of 39 patients who had coma with anisocoria. The clinical characteristics and parameters on computerized tomography (CT) findings were compared using univariate analysis to determine the factors that were related to the level of consciousness. Logistic regression models established the predictive equations for coma and anisocoria.
  
  
  RESULTS
  Univariate analysis revealed that hematoma volume, the score of intraventricular hemorrhage (IVH score) and the amplitude of midline shift were the factors related to coma and anisocoria. Mean hematoma volume was 24.0 ± 13.0 ml, 53.6 ± 12.6 ml and 80.5 ± 24.6 ml, the mean amplitudes of midline shift were 1.3 ± 2.0 mm, 5.9 ± 4.9 mm and 10.1 ± 5.5 mm, and the mean IVH score was 0.8 ± 1.3, 3.3 ± 3.3 and 5.9 ± 3.4 in groups 1, 2 and 3, respectively. Multivariate analysis showed that hematoma volume and IVH score were independent prognostic factors for coma and anisocoria. The predictive equations for coma and anisocoria were LogitP = 0.279X(HV) + 0.521X(IVH)-18.164 and LogitP = 0.125X(HV)+0.326X(IVH)-6.864, respectively.
  
  
  CONCLUSIONS
  Hematoma volume and IVH score were the independent prognostic factors for coma and anisocoria. Logistic regression models established the fitted predictive equations, which could help clinicians make clinical risk assessments and decisions.
authors.affiliation: Department of Neurology, the Third Xiangya Hospital, Central South University, Changsha 410013, PR China. docsong@126.com
authors.forename: Zhi
authors.initials: Z
authors.lastname: Song
authors.forename: Wen
authors.initials: W
authors.lastname: Zheng
authors.forename: Haixia
authors.initials: H
authors.lastname: Zhu
authors.forename: Yiwei
authors.initials: Y
authors.lastname: Chen
authors.forename: Xuejun
authors.initials: X
authors.lastname: Fan
authors.forename: Deren
authors.initials: D
authors.lastname: Hou
authors.forename: Hao
authors.initials: H
authors.lastname: Deng
mesh_terms: D000328:Adult; D000368:Aged; D015875:Anisocoria / Q000209:etiology*; D001921:Brain / Q000000981:diagnostic imaging; D002543:Cerebral Hemorrhage / Q000150:complications* / Q000000981:diagnostic imaging*; D003128:Coma / Q000209:etiology*; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D059906:Neuroimaging; D011237:Predictive Value of Tests; D011379:Prognosis; D012189:Retrospective Studies; D018570:Risk Assessment; D020521:Stroke / Q000209:etiology; D014057:Tomography, X-Ray Computed