Skip to content
DraconiticResearch
Search Sources API
197,788 studies31 sourcesUpdated 47 min ago
Peer-reviewedCC BY-NC-NDThrombosis journal

Developing a screening tool for in-hospital deep venous thrombosis risk in severe pneumonia patients admitted to respiratory intensive care unit.

  • Puhan Song1,2
  • Yan Li3
  • Yulei Gong4
  • Hongzhen Wang4
  • Hong Chang5,6
6 affiliations
  1. 1School of Nursing, Capital Medical University, Beijing, 100069, China. [email protected].
  2. 2Department of Pulmonary and Critical Care Medicine, Xuanwu Hospital, Capital Medical University, Beijing, 100021, China. [email protected].
  3. 3Department of Cardiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China.
  4. 4Department of Pulmonary and Critical Care Medicine, Xuanwu Hospital, Capital Medical University, Beijing, 100021, China.
  5. 5School of Nursing, Capital Medical University, Beijing, 100069, China. [email protected].
  6. 6Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100021, China. [email protected].
Read full textPDFDOICite

Abstract

Background

The incidence of deep venous thrombosis (DVT) is higher for severe pneumonia patients hospitalized in respiratory intensive care unit (RICU). Currently, no validated risk assessment tool specifically exists for evaluating DVT risk in this population.Aims

This study aimed to develop a screening tool to estimate the risk of in-hospital DVT in patients with severe pneumonia admitted to the RICU.Methods

In this retrospective cohort study, least absolute shrinkage and selection operator (LASSO) regression was used to identify candidate predictors of in-hospital DVT. We then developed two prediction models, a support vector machine (SVM) algorithm and a nomogram model, to predict DVT risk in patients with severe pneumonia in the RICU.Results

We included 368 patients with severe pneumonia admitted to the RICU. DVT occurred in 124 patients (33.7%) during hospitalization. The area under the Receiver operating characteristic (ROC) curve (AUC) of Padua scores, SVM algorithm and nomogram model to predict the risk of DVT were 0.504 [95% confidence interval (CI): 0.442-0.566], 0.526 (95%CI: 0.466-0.587) and 0.754 (95%CI: 0.703-0.805) respectively. The radar chart also indicated that nomogram model had better performance in multiple metrics. For nomogram model, the calibration plot displayed good concordance between predicted and actual outcomes. Clinical utility was assessed using decision curve analysis (DCA). With a range of threshold probability values between 0.1 and 0.8, the nomogram model provided greater net clinical benefit to predict the risk of DVT than Padua scores and SVM algorithm.Conclusions

We developed a nomogram to predict the risk of in-hospital DVT in severe pneumonia patients admitted to RICU. The nomogram showed moderate discriminatory performance and may help identify patients who warrant intensified surveillance or confirmatory ultrasonographic assessment.

Related

  1. Improving Antibiotic Stewardship Through a Retrospective Study of the Diagnostic Value of Nasal Methicillin-Resistant Staphylococcus aureus Polymerase Chain Reaction for Pneumonia in Intensive Care Unit Settings
    Jasmin K Bhullar
    CureusPeer-reviewedCC BYFull textGeneral medicineEurope PMC
  2. Concurrent Cold Autoimmune Hemolytic Anemia, Painless Thyroiditis, and Deep Vein Thrombosis Following Mycoplasma pneumoniae Pneumonia: A Rare Case of Multi-Organ Immune Complications
    Makiko Ogawa, Yuki Osada, Yuya Miwa +4
    Internal MedicinePeer-reviewedCC BY-NC-NDFull textGeneral medicineJ-STAGE
  3. Developing the supportive care bundle for mothers of infants hospitalised in the neonatal intensive care unit: protocol for a multimethod study
    Fatemeh Mehrabirad, Mandana Saki, Mohammad Gholami +2
    BMJ OpenPeer-reviewedCC BY-NC 4.0Full textObstetrics, gynaecology & paediatricsCrossref
  4. Alternative to invasive bronchoscopy: validating tracheal aspirates for microbiome profiling in intensive care unit pneumonia patients
    Parshatd Govindasamy, Jun Yeun Cho, Jiyoul Yang +9
    Scientific ReportsPeer-reviewedCC BYFull textGeneral medicineDOAJ
  5. Developing a multi-component immune model for evaluating the risk of respiratory illness in athletes
    Maree Gleeson, John Attia, Lisa J Elkington +5
    PubMedPeer-reviewedLicence not stated34 citedPhysiologyOpenAlex

Cite

BibTeXRIS
BibTeX
@article{song2026developing,
  title = {{Developing a screening tool for in-hospital deep venous thrombosis risk in severe pneumonia patients admitted to respiratory intensive care unit.}},
  author = {Puhan Song and Yan Li and Yulei Gong and Hongzhen Wang and Hong Chang},
  journal = {Thrombosis journal},
  year = {2026},
  month = aug,
  doi = {10.1186/s12959-026-00909-x},
  url = {https://europepmc.org/article/MED/42972371},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Developing a screening tool for in-hospital deep venous thrombosis risk in severe pneumonia patients admitted to respiratory intensive care unit.
AU  - Puhan Song
AU  - Yan Li
AU  - Yulei Gong
AU  - Hongzhen Wang
AU  - Hong Chang
PY  - 2026
DA  - 2026/08/05
JO  - Thrombosis journal
DO  - 10.1186/s12959-026-00909-x
UR  - https://europepmc.org/article/MED/42972371
AB  - Background  The incidence of deep venous thrombosis (DVT) is higher for severe pneumonia patients hospitalized in respiratory intensive care unit (RICU). Currently, no validated risk assessment tool specifically exists for evaluating DVT risk in this population.Aims  This study aimed to develop a screening tool to estimate the risk of in-hospital DVT in patients with severe pneumonia admitted to the RICU.Methods  In this retrospective cohort study, least absolute shrinkage and selection operator (LASSO) regression was used to identify candidate predictors of in-hospital DVT. We then developed two prediction models, a support vector machine (SVM) algorithm and a nomogram model, to predict DVT risk in patients with severe pneumonia in the RICU.Results  We included 368 patients with severe pneumonia admitted to the RICU. DVT occurred in 124 patients (33.7%) during hospitalization. The area under the Receiver operating characteristic (ROC) curve (AUC) of Padua scores, SVM algorithm and nomogram model to predict the risk of DVT were 0.504 [95% confidence interval (CI): 0.442-0.566], 0.526 (95%CI: 0.466-0.587) and 0.754 (95%CI: 0.703-0.805) respectively. The radar chart also indicated that nomogram model had better performance in multiple metrics. For nomogram model, the calibration plot displayed good concordance between predicted and actual outcomes. Clinical utility was assessed using decision curve analysis (DCA). With a range of threshold probability values between 0.1 and 0.8, the nomogram model provided greater net clinical benefit to predict the risk of DVT than Padua scores and SVM algorithm.Conclusions  We developed a nomogram to predict the risk of in-hospital DVT in severe pneumonia patients admitted to RICU. The nomogram showed moderate discriminatory performance and may help identify patients who warrant intensified surveillance or confirmatory ultrasonographic assessment.
SN  - 1477-9560
ER  -