Soochow University, Suzhou, Jiangsu, China; Department of Neurosurgery, Shengli Oilfield Central Hospital, Dongying, Shandong.
Graduate School, Qinghai University, Xining, Qinghai.
World Neurosurg. 2020 May;137:e470-e478. doi: 10.1016/j.wneu.2020.02.004. Epub 2020 Feb 10.
To establish a new nomogram model and provide a new theoretical basis for the diagnosis and treatment of spontaneous intracerebral hemorrhage.
The clinical data and noncontrast computed tomography images of patients with spontaneous intracerebral hemorrhage in 3 tertiary medical centers were collected continuously. Univariate and binary logistic regression analysis were performed to screen out the independent predictors that were significantly associated with hematoma expansion. The nomogram model was drawn by R programming language. According to the related risk factors of nomogram, decision curve analysis and clinical impact curve were established.
The numbers of the 3 cooperative units were 554, 582, and 202, respectively. Island sign, blend sign, swirl sign, intraventricular hemorrhage, history of diabetes, time to baseline computed tomography scan, and baseline hematoma volume were independent predictors of hematoma expansion. Baseline hematoma volume >20 mL (odds ratio, 4.088; 95% confidence interval, 2.802-5.964; P < 0.0001) was the most dangerous factor for predicting hematoma expansion, followed by the time to baseline computed tomography scan ≤1 hour (odds ratio, 4.188; 95% confidence interval, 2.598-6.750; P < 0.0001). Decision curve analysis showed that the net benefit of patients was the highest when nomogram score existed. When the threshold probability was >40%, the prediction probability of hematoma expansion was close to the actual probability.
This nomogram model could accurately predict hematoma expansion of spontaneous intracerebral hemorrhage, which provided a theoretical basis for clinicians to intervene in the early stage.
建立新的列线图模型，为自发性脑出血的诊断和治疗提供新的理论依据。
连续收集 3 家三级医疗中心自发性脑出血患者的临床资料和非对比 CT 图像。采用单因素和二元逻辑回归分析筛选与血肿扩大显著相关的独立预测因子。使用 R 编程语言绘制列线图模型。根据列线图的相关风险因素，建立决策曲线分析和临床影响曲线。
3 个合作单位的病例数分别为 554、582 和 202 例。岛征、混合征、漩涡征、脑室内出血、糖尿病史、基线 CT 扫描时间和基线血肿体积是血肿扩大的独立预测因子。基线血肿体积>20 mL（比值比，4.088；95%置信区间，2.802-5.964；P<0.0001）是预测血肿扩大最危险的因素，其次是基线 CT 扫描时间≤1 小时（比值比，4.188；95%置信区间，2.598-6.750；P<0.0001）。决策曲线分析显示，当列线图评分存在时，患者的净收益最高。当阈值概率>40%时，血肿扩大的预测概率接近实际概率。
该列线图模型能够准确预测自发性脑出血的血肿扩大，为临床医生早期干预提供了理论依据。
