AUTHOR=Ge Wenjie , Chu Aiqin , Cao Zhimin , Zhu Xinyi , Zhu Shoujun 

TITLE=A nomogram for predicting hospital-acquired venous thromboembolism in ICU patients with mechanical ventilation: a retrospective cohort study

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1653481

DOI=10.3389/fmed.2025.1653481

ISSN=2296-858X

ABSTRACT=ObjectiveTo develop a predictive nomogram for early identification of hospital-acquired venous thromboembolism (HA-VTE) in adult ICU patients undergoing mechanical ventilation.MethodsThis study involved 472 ICU patients with mechanical ventilation in the Department of Intensive Care Unit of The First Affiliated Hospital of USTC from January 2021 to December 2022. The diagnosis of VTE was objectively confirmed by imaging studies. Clinical information and relevant laboratory test data were retrospectively collected. Logistic regression was utilized to pinpoint these patients’ independent risk factors for HA-VTE. Subsequently, a nomogram was established to predict HA-VTE risk. The efficacy of this model was assessed through the area under the receiver operating characteristic curve (AUC-ROC), alongside a calibration curve and the Hosmer–Lemeshow test to examine its fit. Additionally, decision curve analysis (DCA) was conducted to ascertain the clinical relevance of the predictive model.ResultsThe study incorporated 472 ICU patients with mechanical ventilation, with a HA-VTE rate of 12.50% (59 cases). Six independent predictors were identified and integrated into a predictive nomogram: stroke, bedridden for at least 3 days, caprini risk score, Glasgow Coma Scale, fibrinogen, and d-dimer. The nomogram demonstrated intense discrimination (AUC 0.909, 95% CI: 0.859–0.958). The calibration curve closely aligned with the ideal curve, and the Hosmer–Lemeshow goodness-of-fit test yielded a χ2 value of 6.398 with a p-value of 0.603, verifying the model’s high calibration accuracy. Additionally, the DCA indicated that the model provides a net benefit across a wide range of decision thresholds from 0 to 0.99, underscoring its clinical utility. Internal validation yielded a concordance index of 0.909, indicating robust reliability.ConclusionThis study established a validated nomogram incorporating six readily accessible clinical predictors to stratify HA-VTE risk in mechanically ventilated ICU patients. The tool facilitates early intervention and personalized prophylaxis strategies.Implications for clinical practiceThe nomogram provides doctors with a pragmatic, evidence-based instrument to enhance the prevention of hospital-acquired venous thromboembolism in critically ill individuals on mechanical ventilation. Facilitating focused surveillance and customized anticoagulation strategies can diminish HA-VTE-related morbidity and healthcare expenditures while enhancing patient outcomes.