New model may predict muscular calf vein thrombosis risk in acute COPD exacerbation
China: Researchers from China have developed a nomogram for predicting muscular calf vein thrombosis (MCVT) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD).
"This model is capable of assisting clinicians in developing treatment recommendations and formulating corresponding prevention measures," Kaiyu Han, The Second Affiliated Hospital of Harbin Medical University, Harbin, People's Republic of China, and colleagues wrote in their study published in the International Journal of General Medicine.
Muscular calf vein thrombosis (MCVT) refers to thrombosis in the calf muscle veins and is most commonly found in fibula and halibut muscles. The incidence of MCVT accounts for 50% of isolated distal deep vein thrombosis (IDDVT) and 5.6–31.3% of DVT. Its symptoms include pain and lower limb swelling that are often inconspicuous as the inflammatory stimulation caused by thrombosis is light and the muscular calf vein branch is thin, this is not easily detected by patients and clinicians. Thus, MCVT patients may miss the best treatment timing and develop pulmonary embolism (PE).
Against the above background, the researchers aimed to establish a risk prediction model for muscular calf vein thrombosis in patients with acute exacerbation of the chronic obstructive pulmonary disease.
The study sample comprised 248 AECOPD patients and all of them underwent vascular ultrasounds of both lower limbs. Om factors with significant group differences in univariate analysis and multivariate logistic regression analysis were performed to screen for the independent risk factors of MCVT. A nomogram was constructed to predict the risk of MCVT and validated with bootstrap resampling.
240 patients were included for analysis and were divided into the MCVT group (n = 81) and the non-MCVT group (n = 159), according to the exclusion criteria.
Based on the study, the researchers found the following:
- 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 the receiver operating characteristic (ROC) curve for the prediction model and the simplified Wells score was 0.784 and 0.659, respectively.
- The cut-off value and Youden index of the 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.
"We found hypertension, reduced albumin, elevated MPV, bed rest ≥3 days, and elevated D-dimer to be independent risk factors of AECOPD patients with MCVT," the researchers wrote in their study.
Based on the above, a novel nomogram was developed and internally validated for predicting MCVT risk for AECOPD patients. According to the authors, the nomogram is easy to use, exhibits excellent calibration, and is highly accurate. This nomogram might be helpful for clinical to make individualized predictions of MCVT and improve treatment recommendations for AECOPD patients with high-risk factors.
Reference:
Hu X, Li X, Xu H, Zheng W, Wang J, Wang W, Li S, Zhang N, Wang Y, Han K. Development of Risk Prediction Model for Muscular Calf Vein Thrombosis with Acute Exacerbation of Chronic Obstructive Pulmonary Disease. Int J Gen Med. 2022;15:6549-6560. https://doi.org/10.2147/IJGM.S374777
Disclaimer: This website is primarily for healthcare professionals. The content here does not replace medical advice and should not be used as medical, diagnostic, endorsement, treatment, or prescription advice. Medical science evolves rapidly, and we strive to keep our information current. If you find any discrepancies, please contact us at corrections@medicaldialogues.in. Read our Correction Policy here. Nothing here should be used as a substitute for medical advice, diagnosis, or treatment. We do not endorse any healthcare advice that contradicts a physician's guidance. Use of this site is subject to our Terms of Use, Privacy Policy, and Advertisement Policy. For more details, read our Full Disclaimer here.
NOTE: Join us in combating medical misinformation. If you encounter a questionable health, medical, or medical education claim, email us at factcheck@medicaldialogues.in for evaluation.