ArticleInternational journal of general medicine2026
Machine Learning-Based Prediction of Lower Extremity Deep Vein Thrombosis in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease.
Article in International journal of general medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) have an increased risk of lower-extremity deep vein thrombosis (DVT) due to inflammation, immobility, and coagulation abnormalities. This study aimed to develop a machine learning model for predicting DVT risk in AECOPD patients and identify important predictors. Methods: This single-center retrospective study included 1,500 patients with AECOPD who were classified into DVT (n = 126) and non-DVT groups (n = 1,374) according to lower-limb venous ultrasonography findings. Clinical characteristics and laboratory parameters were collected. The Boruta algorithm was applied for feature selection, and three machine learning models, including decision tree (DT), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost), were developed. Model performance was assessed using the receiver operating characteristic (ROC) curve, area under the curve (AUC), and multiple classification metrics. TOPSIS was used for comprehensive model evaluation, and SHAP analysis was performed to interpret the optimal model. Results: Compared with the non-DVT group, patients with DVT had higher levels of C-reactive protein, D-dimer, and systemic inflammation-coagulation index (SCI) (all P < 0.05). The Boruta algorithm identified 14 key features. XGBoost showed the best performance in the testing set, with an AUC of 0.707, compared with MLP (0.684) and DT (0.667). TOPSIS analysis ranked XGBoost highest, with an overall score of 0.806. SHAP analysis identified D-dimer as the most influential predictor, followed by SCI, albumin, mean corpuscular volume, and C-reactive protein. Conclusion: The XGBoost model showed potential for DVT risk assessment in patients with AECOPD. SCI, as an integrated marker of inflammation and coagulation, may provide complementary information for risk stratification. However, further validation in independent cohorts is required before its potential clinical application.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.