Abstract
PURPOSE: This study aims to establish a risk prediction model for muscular calf vein thrombosis (MCVT) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). METHODS: The research sample consisted of 248 patients with AECOPD and all of them underwent vascular ultrasounds of both lower limbs in this retrospective study. Univariate analysis and multivariate logistic regression analysis were conducted on factors with significant group differences to screen for the independent risk factors of MCVT. A nomogram to predict the risk of MCVT was constructed and validated with bootstrap resampling. RESULTS: According to the exclusion criteria, 240 patients were included for analysis, divided into the MCVT group (n = 81) and the non-MCVT group (n = 159). Multivariate logistic regression analyses showed that hypertension, elevated MPV, reduced albumin (ALB), elevated D-dimer and bed rest ≥3 days were independent risk factors for MCVT in AECOPD. A nomogram model for predicting AECOPD with MCVT was established based on them. The area under the curve (AUC) of receiver operating characteristic (ROC) curve for the prediction model and the simplified Wells score was 0.784 (95% CI: 0.722-0.847) and 0.659 (95% CI: 0.583-0.735), respectively. The cut-off value and Youden index of prediction model were 0.248 and 0.454, respectively. At the same time, the sensitivity, specificity, positive predictive value, and negative predictive value of the prediction model were 85.9%, 59.5%, 84.6%, and 77.4%, respectively. The sensitivity and specificity of the simplified Wells score were 67.9% and 56.3%, respectively. Validation by the use of bootstrap resampling revealed optimal discrimination and calibration, and the decision analysis curve (DAC) suggested that this prediction model involved high clinical practicability. CONCLUSION: We developed a nomogram that can predict the risk of MCVT for AECOPD patients. This model has the potential to assist clinicians in making treatment recommendations and formulating corresponding prevention measures.