Pan Xi, Wang Zhi, Fang Qi, Li Tan, Xu Lan, Deng Shengming
Departments of Neurology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Nursing department, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Clin Neurol Neurosurg. 2021 Apr 12;205:106638. doi: 10.1016/j.clineuro.2021.106638.
To develop and validate a nomogram to predict the probability of distal deep venous thrombosis (DVT) within first 14 days of stroke onset in patients by using easily obtainable parameters.
This is a retrospective study. The presence of distal DVT was evaluated using ultrasonography within the first 14 days. Data were randomly assigned to either a modelling data set or a validation data set. Univariable and multivariate logistic regression analysis was used to determine risk scores to predict distal DVT in the modelling data set, and nomogram and calibration curve were constructed by R project.
A total of 1620 patients with acute stroke were enrolled in the study. The multivariate analysis revealed that the old age, female gender, haemorrhagic stroke, coronary heart disease, lower limb weakness, a low serum albumin level, and a high D-dimer level are highly predictive of 14-day risk of distal DVT. The AUC of the nomogram to predict the 14-day risk of distal DVT was 0.785 (95% CI, 0.742-0.827) and 0.813 (0.766-0.860) for the modelling cohort and external validation cohort, respectively. Moreover, the calibration of the nomogram showed a nonsignificant Hosmer-Lemeshow test statistic in the modelling (P = 0.876) and validation (P = 0.802) sets. With respect to decision curve analyses, the nomogram exhibited preferable net benefit gains than the staging system across a wide range of threshold probabilities.
The established nomogram displayed a superior performance in terms of predictive accuracy, discrimination capability, and clinical utility, may be helpful for clinicians to identify high-risk groups of distal DVT.
通过使用易于获得的参数,开发并验证一种列线图,以预测卒中发病后14天内患者发生远端深静脉血栓形成(DVT)的概率。
这是一项回顾性研究。在发病后的前14天内,使用超声检查评估远端DVT的存在情况。数据被随机分配到建模数据集或验证数据集中。采用单变量和多变量逻辑回归分析来确定预测建模数据集中远端DVT的风险评分,并通过R项目构建列线图和校准曲线。
本研究共纳入1620例急性卒中患者。多变量分析显示,老年、女性、出血性卒中、冠心病、下肢无力、低血清白蛋白水平和高D-二聚体水平是14天远端DVT风险的高度预测因素。预测14天远端DVT风险的列线图在建模队列和外部验证队列中的AUC分别为0.785(95%CI,0.742-0.827)和0.813(0.766-0.860)。此外,列线图的校准在建模组(P = 0.876)和验证组(P = 0.802)中显示出非显著的Hosmer-Lemeshow检验统计量。关于决策曲线分析,在广泛的阈值概率范围内,列线图显示出比分期系统更好的净效益增益。
所建立的列线图在预测准确性、鉴别能力和临床实用性方面表现优异,可能有助于临床医生识别远端DVT的高危人群。