Vital Images, Minnetonka, MN, USA.
Department of Neurology, San Camillo de' Lellis District General Hospital, Rieti, Italy.
Neurocrit Care. 2020 Apr;32(2):539-549. doi: 10.1007/s12028-019-00783-8.
Rapid diagnosis and proper management of intracerebral hemorrhage (ICH) play a crucial role in the outcome. Prediction of the outcome with a high degree of accuracy based on admission data including imaging information can potentially influence clinical decision-making practice.
We conducted a retrospective multicenter study of consecutive ICH patients admitted between 2012-2017. Medical history, admission data, and initial head computed tomography (CT) scan were collected. CT scans were semiautomatically segmented for hematoma volume, hematoma density histograms, and sphericity index (SI). Discharge unfavorable outcomes were defined as death or severe disability (modified Rankin Scores 4-6). We compared (1) hematoma volume alone; (2) multiparameter imaging data including hematoma volume, location, density heterogeneity, SI, and midline shift; and (3) multiparameter imaging data with clinical information available on admission for ICH outcome prediction. Multivariate analysis and predictive modeling were used to determine the significance of hematoma characteristics on the outcome.
We included 430 subjects in this analysis. Models using automated hematoma segmentation showed incremental predictive accuracies for in-hospital mortality using hematoma volume only: area under the curve (AUC): 0.85 [0.76-0.93], multiparameter imaging data (hematoma volume, location, CT density, SI, and midline shift): AUC: 0.91 [0.86-0.97], and multiparameter imaging data plus clinical information on admission (Glasgow Coma Scale (GCS) score and age): AUC: 0.94 [0.89-0.99]. Similarly, severe disability predictive accuracy varied from AUC: 0.84 [0.76-0.93] for volume-only model to AUC: 0.88 [0.80-0.95] for imaging data models and AUC: 0.92 [0.86-0.98] for imaging plus clinical predictors.
Multiparameter models combining imaging and admission clinical data show high accuracy for predicting discharge unfavorable outcome after ICH.
快速诊断和适当的管理对脑出血（ICH）的结果至关重要。基于入院数据（包括影像学信息）进行高度准确的预后预测，可能会影响临床决策实践。
我们进行了一项回顾性多中心研究，纳入了 2012 年至 2017 年间连续收治的 ICH 患者。收集了病史、入院数据和初始头部计算机断层扫描（CT）扫描。对 CT 扫描进行半自动血肿体积、血肿密度直方图和球形指数（SI）分割。出院不良结局定义为死亡或严重残疾（改良 Rankin 评分 4-6）。我们比较了（1）血肿体积单独；（2）包括血肿体积、位置、密度异质性、SI 和中线移位的多参数影像学数据；（3）入院时可获得的多参数影像学数据与临床信息对 ICH 结局预测的影响。多变量分析和预测模型用于确定血肿特征对结局的意义。
本分析纳入了 430 例患者。使用自动血肿分割的模型显示，仅使用血肿体积预测住院死亡率的预测准确性有所提高：曲线下面积（AUC）：0.85 [0.76-0.93]，多参数影像学数据（血肿体积、位置、CT 密度、SI 和中线移位）：AUC：0.91 [0.86-0.97]，以及多参数影像学数据加上入院时的临床信息（格拉斯哥昏迷量表（GCS）评分和年龄）：AUC：0.94 [0.89-0.99]。同样，严重残疾预测准确性从体积模型的 AUC：0.84 [0.76-0.93]到影像学数据模型的 AUC：0.88 [0.80-0.95]和影像学加临床预测因子的 AUC：0.92 [0.86-0.98]不等。
结合影像学和入院临床数据的多参数模型对预测 ICH 后出院不良结局具有很高的准确性。
