Development and Validation of a Nomogram for Lower Extremity Deep Venous Thrombosis in Patients after Acute Stroke

To develope and validate a nomogram to predict the probability of deep venous thrombosis (DVT) in patients after acute stroke during the first 14 days with clinical features and easily obtainable biochemical parameters.

This is a single-center prospective cohort study. The potential predictive variables for DVT at baseline were collected, and the presence of DVT was evaluated using ultrasonography within the first 14 days. Data were randomly assigned to either a modeling data set or a validation data set. Univariable and Multivariate logistic regression analysis was used to develop risk scores to predict DVT in the modeling data set and the area under the receiver operating characteristic curve to validate the score in the test data set, and nomogram and calibration curve were constructed by R project.

A total of 1651 patients with acute stroke were enrolled in the study. The overall incidence of DVT after acute stroke within two weeks was 14.4%. Multivariable analysis detected older age (≥65 years),female gender, hemorrhagic stroke, malignancy, lower limb muscle strength<3 grade, Albumin<40 g·L^-1 and D-dimer>0.5 mg·L^-1 were highly predictive of 14-day risk of DVT. The AUC of the nomogram with these above-mentioned independent risk factors to predict the 14-day risk of DVT was 0.756 (95% CI, 0.712-0.812) and 0.811 (95%CI, 0.762-0.859) for the modeling cohort and external validation cohort, respectively. Moreover, the calibration of the nomogram showed a nonsignificant Hosmer-Lemeshow test statistic in the modeling (P = 0.250) and validation sets (P = 0.995). With respect to decision curve analyses, the nomogram exhibited preferable net benefit gains than the staging system across a wide range of threshold probabilities.

This nomogram had a good performance in predictive accuracy, discrimination capability, and clinical utility, which was helpful for clinicians to identify high-risk groups of DVT and formulate relevant prevention and treatment measures.