Generation of a Predictive Clinical Model for Isolated Distal Deep Vein Thrombosis (ICMVT) Detection

Abstract

Background: Isolated distal deep vein thrombosis (ICMVT) increases the risk of pulmonary embolism. Although predictive models are available, their utility in predicting the risk is unknown. To develop a clinical prediction model for isolated distal calf muscle venous thrombosis, data from 462 patients were used to assess the independent risk variables for ICMVT. Material/Methods: The area under curve (AUC) for Model A and Model B were calculated and other risk factors were based on age, pitting edema in the symptomatic leg, calf swelling with least 3 cm larger than the asymptomatic leg, recent bed rest for 3 days or more in the past 4 weeks, requiring general or major surgery with regional anesthesia, sex, and local tenderness distributed along the deep venous system as independent predictors of calf muscle venous thrombosis. Model A includes the risk variables for C-reactive protein and D-dimer. Results: The area under ROC curve for Model A training set was 0.924 (95% CI: 0.895-0.952), the area under ROC curve for Model B training set was 0.887 (95% CI: 0.852-0.922), and the AUC difference between the 2 models was statistically significant (P<0.001); the area under ROC curve for Model A obtained in the validation set was 0.902 (95% CI: 0.844-0.961), the area under ROC curve for Model B was 0.842 (95% CI: 0. 0.773-0.910), and the difference between the 2 models was statistically significant (P=0.012). Conclusions: Predictive Model A better predicts isolated calf muscle venous thrombosis and is able to help clinicians rapidly and early diagnose ICMVT, displaying higher utility for missed diagnosis prevention and disease therapy.