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.