Prediction of coma and anisocoria based on computerized tomography findings in patients with supratentorial intracerebral hemorrhage
详细信息查看全文 | 推荐本文 |
摘要

Objectives

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.

Patients and 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 卤 13.0 ml, 53.6 卤 12.6 ml and 80.5 卤 24.6 ml, the mean amplitudes of midline shift were 1.3 卤 2.0 mm, 5.9 卤 4.9 mm and 10.1 卤 5.5 mm, and the mean IVH score was 0.8 卤 1.3, 3.3 卤 3.3 and 5.9 卤 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 Logit P = 0.279XHV + 0.521XIVH 鈭?#xA0;18.164 and Logit P = 0.125XHV + 0.326XIVH 鈭?#xA0;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.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700