ArticleMediators of inflammation2025
Risk Assessment of Venous Thromboembolism in Neurocritical Patients: Construction and Validation of a Clinical Prediction Model.
Article in Mediators of inflammation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- 7. Developing high-performance prediction models for medical outcomes.Journal of postgraduate medicine · 2026Review
- Risk Assessment of Venous Thromboembolism in Neurocritical Patients: Construction and Validation of a Clinical Prediction Model.Mediators of inflammation · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background/Objective: Venous thromboembolism (VTE) remains a significant challenge in neurocritical care, with limited tailored risk assessment tools available for clinical practice. This study aimed to develop and validate a practical prediction model to support VTE prevention strategies in neurocritical patients. Methods: A total of 605 neurocritical patients were retrospectively enrolled in the neurologic intensive care unit (NICU) from May 2022 to April 2024. The eligible patients were randomly divided into a training dataset and a testing dataset in a ratio of 7:3. Variables with significant univariate effects in the training dataset were selected for multivariable stepwise regression analysis. The model fitting goodness was tested using the Hosmer-Lemeshow test, the area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the model discrimination, and decision curve analysis (DCA) was used to test the clinical value of the model. Results: The final model identified age, length of stay in the NICU, activated partial thromboplastin time (APTT), D-dimer, tracheotomy duration, pulmonary infection, antibiotic use, and dehydrating agents as key predictors. The nomogram demonstrated excellent discrimination (AUC 0.763, 95% CI: 0.714-0.811 in the training dataset; AUC 0.809, 95% CI: 0.732-0.885 in the testing cohort), strong calibration (Hosmer-Lemeshow test: Conclusions: The risk prediction model developed in this study can effectively predict VTE occurrence in neurocritical patients with good discrimination and clinical utility, providing a valuable tool for identifying high-risk individuals and performing early prevention and treatment measures.
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Registered trials
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