Xin yixue (Mar 2024)
Predictive value of TEG for deep venous thrombosis of lower limbs in patients with chronic obstructive pulmonary disease
Abstract
Objective To analyze the value of thromboelastogram (TEG) parameters in predicting deep venous thrombosis of the lower limbs in patients with chronic obstructive pulmonary disease(COPD). Methods Thirty-five COPD patients complicated with deep venous thrombosis of the lower limbs were assigned into the observation group, and 35 COPD patients without deep venous thrombosis of the lower limbs of the same period were recruited in the control group. TEG parameters (R value of coagulation reaction time, K value of blood coagulation time, α angle of coagulation and MA value of maximum clot intensity), routine blood test, blood gas analysis and baseline data were collected within 24 hours after admission. Logistic regression analysis and receiver operating characteristic (ROC) curve analysis were used to analyze the predictive value of all parameters of TEG for deep venous thrombosis of the lower limbs in patients with COPD. Results R value, K value and α angle were significantly correlated with deep venous thrombosis of the lower limbs in patients with COPD (all P < 0.05). The area under the ROC curve (AUC) of R value was 0.787 (95%CI: 0.679-0.895), 0.758 for K value (95%CI: 0.646-0.870), 0.689 for α angle (95%CI: 0.565-0.812), and 0.660 for MA value (95%CI: 0.533-0.787), respectively. The combination of four parameters yielded higher sensitivity and specificity for predicting deep venous thrombosis of the lower limbs (AUC:0.882, 95%CI:0.796-0.969, all P < 0.001), the cut-off value was 0.436, the sensitivity was 94.3% and the specificity was 80%, respectively. Conclusions R value, K value and α angle in TEG are the independent predictors of deep venous thrombosis of the lower limbs in patients with COPD. R value, K value and α angle can properly predict deep venous thrombosis of the lower limbs in patients with COPD, and the combination of R value, K value, α angle and MA value yields higher sensitivity and specificity.
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