Phan Thanh G, Chen Jian, Beare Richard, Ma Henry, Clissold Benjamin, Van Ly John, Srikanth Velandai
Neurosciences, Monash Health , Melbourne, VIC , Australia.
Department of Medicine, School of Clinical Sciences, Monash University , Clayton, VIC , Australia.
Front Neurol. 2017 Feb 28;8:64. doi: 10.3389/fneur.2017.00064. eCollection 2017.
Prognostication following intracerebral hemorrhage (ICH) has focused on poor outcome at the expense of lumping together mild and moderate disability. We aimed to develop a novel approach at classifying a range of disability following ICH.
The Virtual International Stroke Trial Archive collaboration database was searched for patients with ICH and known volume of ICH on baseline CT scans. Disability was partitioned into mild [modified Rankin Scale (mRS) at 90 days of 0-2], moderate (mRS = 3-4), and severe disabilities (mRS = 5-6). We used binary and trichotomy decision tree methodology. The data were randomly divided into training (2/3 of data) and validation (1/3 data) datasets. The area under the receiver operating characteristic curve (AUC) was used to calculate the accuracy of the decision tree model.
We identified 957 patients, age 65.9 ± 12.3 years, 63.7% males, and ICH volume 22.6 ± 22.1 ml. The binary tree showed that lower ICH volume (<13.7 ml), age (<66.5 years), serum glucose (<8.95 mmol/l), and systolic blood pressure (<170 mm Hg) discriminate between mild versus moderate-to-severe disabilities with AUC of 0.79 (95% CI 0.73-0.85). Large ICH volume (>27.9 ml), older age (>69.5 years), and low Glasgow Coma Scale (<15) classify severe disability with AUC of 0.80 (95% CI 0.75-0.86). The trichotomy tree showed that ICH volume, age, and serum glucose can separate mild, moderate, and severe disability groups with AUC 0.79 (95% CI 0.71-0.87).
Both the binary and trichotomy methods provide equivalent discrimination of disability outcome after ICH. The trichotomy method can classify three categories at once, whereas this action was not possible with the binary method. The trichotomy method may be of use to clinicians and trialists for classifying a range of disability in ICH.
脑出血（ICH）后的预后评估主要关注不良结局，而将轻度和中度残疾归为一类。我们旨在开发一种新方法来对ICH后的一系列残疾情况进行分类。
在虚拟国际卒中试验存档协作数据库中搜索有ICH且基线CT扫描已知ICH体积的患者。残疾程度分为轻度[90天时改良Rankin量表（mRS）为0 - 2]、中度（mRS = 3 - 4）和重度残疾（mRS = 5 - 6）。我们使用二元和三分法决策树方法。数据被随机分为训练集（数据的2/3）和验证集（数据的1/3）。采用受试者工作特征曲线下面积（AUC）来计算决策树模型的准确性。
我们纳入了957例患者，年龄65.9±12.3岁，男性占63.7%，ICH体积22.6±22.1ml。二元决策树显示，较低的ICH体积（<13.7ml）、年龄（<66.5岁）、血糖（<8.95mmol/L）和收缩压（<170mmHg）可区分轻度与中度至重度残疾，AUC为0.79（95%CI 0.73 - 0.85）。较大的ICH体积（>27.9ml）、较高年龄（>69.5岁）和较低的格拉斯哥昏迷量表评分（<15）可将重度残疾分类，AUC为0.80（95%CI 0.75 - 0.86）。三分法决策树显示，ICH体积、年龄和血糖可将轻度、中度和重度残疾组区分开来，AUC为0.79（95%CI 0.71 - 0.87）。
二元法和三分法在区分ICH后的残疾结局方面具有同等效果。三分法可一次性对三类情况进行分类，而二元法无法做到这一点。三分法可能对临床医生和试验人员在对ICH的一系列残疾情况进行分类时有用。
