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作 者:罗安琪[1] 邓逸伦 齐铁伟[1] 郭少雷[1] 梁丰[1] 李竹浩[3] 王丽琴[3] 黄正松[1]
机构地区:[1]中山大学附属第一医院神经外科 [2]Division of Hematology and Medical Oncology, Department of Medicine, University of Texas Health Science Center, 7703 Floyd Curl Drive, MC 7880, San Antonio, Texas 78229-3900, USA [3]中山大学附属第一医院影像科
出 处:《中国神经精神疾病杂志》2015年第5期271-275,共5页Chinese Journal of Nervous and Mental Diseases
基 金:国家自然科学基金(编号:81171102)
摘 要:目的评价Spetzler-Martin补充分级法是否能更好地筛选手术患者、提高术后预判的准确性。方法回顾性分析我院手术治疗的221例脑动静脉畸形,记录改良Rankin量表评分以及相关临床、影像学资料。建立两种不同Logistic模型,比较不同模型的ROC(receiver operating characteristic curve)曲线下面积并进行统计学分析。结果 Spetzler-Martin补充分级模型ROC曲线下面积(0.901),Spetzler-Martin评分系统模型ROC曲线下面积为(0.774),两者间的差异有统计学意义(P=0.0362)。结论 Spetzler-Martin补充分级法能有效地进一步把患者根据手术风险进行细化分组,更加准确地评估手术风险。当Spetzler-Martin补充分级小于或者等于5分时(敏感性:70%,特异性:88.24%),患者手术风险较低,可以作为是否手术治疗的一个重要参考依据。Objective We evaluate if supplementary grading system can refine patient selection and accurately predict neurological outcome in BAVM. Methods We retrospectively study 221 BAVM patients who were treated micro-surgically by our hospital. The score of pre and post operation mRS and relative clinical, radiology data were collected. Two different logistic models (Spetzler-Martin, Supplementary Spetzler-Martin grading model) were constructed to com-pare the area under ROC. Results Some factors are significant different between worse outcome patients and good out-come patients:Non-hemorrhagic presentations prior surgery, AVM bigger than 3cm, diffuse shape of AVM and the elder patients. Predictive accuracy was higher for the supplementary model (ROC area, 0.91), than the Spetzler-Martin model (ROC area, 0.774). So the predictive accuracy of supplementary model was significantly better than that of the Spet-zler-Martin model (P=0.0362). Conclusions Supplementary Spetzler-Martin model can improve preoperative risk pre-diction and subgroup the patients more efficiently. When the score less than 5(including 5) in supplementary Spet-zler-Martin patients seem to have lower risk relative to surgery.
关 键 词:脑动脉畸形 Spetzler-Martin评分 LOGISTIC回归 显微外科手术
分 类 号:R743.4[医药卫生—神经病学与精神病学]
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