Cancer-associated venous thromboembolism

doi:10.1038/s41572-022-00345-x

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…However, it also has limitations. Firstly, variables such as the severity of stroke upon admission, levels of C-reactive protein, 19 cancer history, 23 , 55 mechanical ventilation, and markers of severity are common risk factors for secondary VTE in stroke patients, but we did not analyze them, which may have influenced the results. Secondly, the incidence of VTE in stroke patients is lower than in the general population, 15 which may lead to model overfitting and wider confidence intervals.…”

Section: Discussionmentioning

confidence: 99%

Nomogram for Risk of Secondary Venous Thromboembolism in Stroke Patients: A Study Based on the MIMIC-IV Database

Lan,

Liu,

Guan

et al. 2024

Clin Appl Thromb Hemost

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This study aims to identify risk factors for secondary venous thromboembolism (VTE) in stroke patients and establish a nomogram, an accurate predictor of probability of VTE occurrence during hospitalization in stroke patients. Medical Information Mart for Intensive Care IV (MIMIC-IV) database of critical care medicine was utilized to retrieve information of stroke patients admitted to the hospital between 2008 and 2019. Patients were randomly allocated into train set and test set at 7:3. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for secondary VTE in stroke patients. A predictive nomogram model was constructed, and the predictive ability of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). This study included 266 stroke patients, with 26 patients suffering secondary VTE after stroke. A nomogram for predicting risk of secondary VTE in stroke patients was built according to pulmonary infection, partial thromboplastin time (PTT), log-formed D-dimer, and mean corpuscular hemoglobin (MCH). Area under the curve (AUC) of the predictive model nomogram was 0.880 and 0.878 in the train and test sets, respectively. The calibration curve was near the diagonal, and DCA curve presented positive net benefit. This indicates the model's good predictive performance and clinical utility. The nomogram effectively predicts the risk probability of secondary VTE in stroke patients, aiding clinicians in early identification and personalized treatment of stroke patients at risk of developing secondary VTE.

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Section: Discussionmentioning

confidence: 99%