Lan Duo, Guo Yibing, Zhang Xiaoming, Huang Xiangqian, Zhou Da, Ji Xunming, Meng Ran
Department of Neurology, National Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, Beijing, China.
Advanced Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.
Clin Appl Thromb Hemost. 2024 Jan-Dec;30:10760296241304777. doi: 10.1177/10760296241304777.
The stage of cerebral venous thrombosis (CVT) is crucial to guide treatment decisions. This study aims to examine changes in fibrinolytic indicators throughout CVT onset and validate a predictive model using admission fibrinolytic indicators to estimate the CVT stage.
Retrospective analysis was conducted on data from 292 CVT patients. We utilized linear regression, time series, and univariate ANOVA analyses to explore characteristics of change in fibrinolytic indicators with CVT duration and identified time point at which fibrinolysis indexes showed significant changes as the time point for acute and chronic stages of CVT. A nomogram was employed to construct a prediction model using a training set, which was then evaluated for discrimination, calibration, and clinical utility.
Prolonged onset duration independently correlated with decreased fibrinogen and D-dimer after adjusting for all variables, with adjusted correlation coefficients of -0.003 (-0.005, -0.001) and -0.004 (-0.007, -0.001), respectively. Significant changes in fibrinolytic indicators were observed around 14 days after CVT onset. The training set demonstrated an area under the curve (AUC) of 0.851 (95% CI: 0.7989-0.904) for the prediction model. Internal validation showed that the nomogram accurately predicted acute CVT with an AUC of 0.828 (95% CI: 0.738-0.918).
According to the trend of fibrinolysis index, 14 days of onset can be used as the dividing point of acute and chronic stages of CVT. For patients with unclear onset, the present model, based on admission fibrinogen and D-dimer values, can accurately predict the stage of CVT. The high discriminative ability indicates the potential of this model for classifying the acute patient.
脑静脉血栓形成(CVT)的分期对于指导治疗决策至关重要。本研究旨在探讨CVT发病过程中纤溶指标的变化,并验证一种使用入院时纤溶指标来估计CVT分期的预测模型。
对292例CVT患者的数据进行回顾性分析。我们利用线性回归、时间序列和单因素方差分析来探索纤溶指标随CVT病程的变化特征,并将纤溶指标出现显著变化的时间点确定为CVT急性和慢性阶段的时间点。使用训练集通过列线图构建预测模型,然后对其进行鉴别、校准和临床实用性评估。
在对所有变量进行调整后,发病持续时间延长与纤维蛋白原和D - 二聚体降低独立相关,调整后的相关系数分别为 -0.003(-0.005,-0.001)和 -0.004(-0.007,-0.001)。在CVT发病后约14天观察到纤溶指标有显著变化。预测模型的训练集曲线下面积(AUC)为0.851(95%CI:0.7989 - 0.904)。内部验证表明,列线图准确预测急性CVT的AUC为0.828(95%CI:0.738 - 0.918)。
根据纤溶指标的变化趋势,发病14天可作为CVT急性和慢性阶段的分界点。对于发病情况不明的患者,基于入院时纤维蛋白原和D - 二聚体值的本模型能够准确预测CVT分期。高鉴别能力表明该模型在对急性患者进行分类方面具有潜力。