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Construction and validation of risk prediction model for deep vein thrombosis in acute exacerbations of chronic obstructive pulmonary disease based on serum angiopoietin 2 levels.

BACKGROUND
This study aims to establish a predictive risk model for deep vein thrombosis (DVT) in patients with acute exacerbation chronic obstructive pulmonary disease (AECOPD) based on serum angiopoietin 2 (Ang-2) levels.


METHODS
The research sample consisted of 650 patients with AECOPD admitted to the First Affiliated Hospital of Chengdu Medical College from January 2019 to January 2021, who were subsequently divided into a modeling group and a verification group. A univariate analysis was performed on the identified risk factors for DVT in AECOPD, and the significant factors were then incorporated into a multivariate logistic regression model to screen for the independent predictors of DVT. A nomogram was constructed, and a receiver operating characteristic curve (ROC), Hosmer-Lemeshow test, decision curve, and clinical impact curve in the modeling and validation cohort were used to analyze the discrimination power, calibration, and clinical validity of the predictive risk nomogram model of AECOPD with comorbid DVT.


RESULTS
Univariate and multivariate logistic regression analyses showed that lower limb edema, BMI, diabetes, respiratory failure, D-dimer, and serum Ang-2 were risk factors for DVT in AECOPD. A nomogram model for predicting AECOPD with comorbid DVT was successfully established. The AUC values for the modeling group and the verification group were 0.844 (95% CI: 0.808-0.932) and 0.755 (95% CI: 0.679-0.861), respectively. According to the Hosmer-Lemeshow test, the P values of the nomogram in the modeling group and the verification group were 0.124 and 0.086, respectively. The decision curve and clinical impact curve suggested that most patients can benefit from this prediction model, and the predicted probability of the model was essentially the same as the actual clinical probability of DVT.


CONCLUSIONS
The predictive risk nomogram model of AECOPD with comorbid DVT based on serum Ang-2 levels has good discrimination power, calibration, and clinical influence. The model is a good fit and has a high predictive value, which helps clinicians identify AECOPD patients at high risk of DTV and formulate corresponding prevention and treatment measures.