ArticleFrontiers in neurology2025
Construction of an early warning model for venous thromboembolism risk in patients with severe cerebral hemorrhage based on ultrasound spontaneous imaging.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Development of an interpretable machine learning model for predicting venous thromboembolism in intensive care unit patients with intracerebral hemorrhage.Frontiers in neurology · 2025Article
- Risk factors for lower limb deep vein thrombosis in patients with intracerebral hemorrhage: a retrospective study using LASSO regression to develop a nomogram.Frontiers in neurologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To investigate the role of ultrasound spontaneous echo contrast (SEC) in venous thromboembolism (VTE) in patients with severe spontaneous cerebral hemorrhage (ICH) and to construct a clinical prediction model. Methods: A total of 69 critically ill ICH patients admitted to the Department of Critical Care Medicine of Liangjiang Hospital of Chongqing Medical University between January 2022 and March 2024 were included in the study. Datas were collected prospectively, including general information, test data, clinical outcomes, and lower limb vascular ultrasound images within 48 h of admission. The statistical analysis was conducted using SPSS 22.0, and the model was constructed using binary logistic regression analysis. The efficacy of the model was assessed using subject operating (ROC) curves and the Hosmer-Lemeshow goodness-of-fit test. Results: The SEC, Albumin and age were identified as independent risk factors for thrombosis in patients with severe ICH. The joint prediction model, constructed based on the indicators, is given by the following equation: Logit(P) = 0.477-0.216 * Albumin + 1.43 * SEC + 0.044 * age. The model demonstrated consistent predictive performance, exhibiting good discrimination (AUC = 0.900) and calibration (Hosmer-Lemeshow χ2 = 5.231, Conclusion: The ICH-VTE early warning model constructed on the basis of SEC, Albumin and age performs well and helps clinicians to dynamically assess the risk of VTE to determine the timing of anticoagulation, which provides therapeutic ideas to reduce the incidence of VTE and improve the clinical outcome of ICH.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.