Explore the Staging of Cerebral Venous Thrombosis Through Fibrinolytic Indicators.

IF 2.3 4区 医学 Q2 HEMATOLOGY Clinical and Applied Thrombosis/Hemostasis Pub Date : 2024-01-01 DOI:10.1177/10760296241304777
Duo Lan, Yibing Guo, Xiaoming Zhang, Xiangqian Huang, Da Zhou, Xunming Ji, Ran Meng
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Abstract

Background: 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.

Methods: 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.

Results: 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).

Conclusion: 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.

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通过纤溶指标探讨脑静脉血栓的分期。
背景:脑静脉血栓形成(CVT)的分期对指导治疗决策至关重要。本研究旨在研究纤溶指标在CVT发病过程中的变化,并验证使用入院纤溶指标评估CVT分期的预测模型。方法:对292例CVT患者资料进行回顾性分析。我们利用线性回归、时间序列和单变量方差分析来探讨纤溶指标随CVT持续时间的变化特征,并确定纤溶指标在急性期和慢性期CVT中表现出显著变化的时间点。利用训练集采用nomogram来构建预测模型,然后对其进行判别、校准和临床应用评估。结果:调整各变量后,发病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)。CVT发生后14天左右,纤溶指标发生显著变化。训练集显示预测模型的曲线下面积(AUC)为0.851 (95% CI: 0.7989-0.904)。内部验证表明,nomogram准确预测急性CVT, AUC为0.828 (95% CI: 0.738 ~ 0.918)。结论:根据纤溶指数变化趋势,发病14 d可作为CVT急慢性分期的分界点。对于起病不明确的患者,本模型基于入院纤维蛋白原和d -二聚体的值可以准确预测CVT的分期。高判别能力表明了该模型对急性病人进行分类的潜力。
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来源期刊
CiteScore
4.40
自引率
3.40%
发文量
150
审稿时长
2 months
期刊介绍: CATH is a peer-reviewed bi-monthly journal that addresses the practical clinical and laboratory issues involved in managing bleeding and clotting disorders, especially those related to thrombosis, hemostasis, and vascular disorders. CATH covers clinical trials, studies on etiology, pathophysiology, diagnosis and treatment of thrombohemorrhagic disorders.
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