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Peer-reviewedCC BY-NC-NDExperimental and therapeutic medicine

Analysis of risk factors and construction of a prediction model for postoperative deep vein thrombosis in patients with periarticular knee fractures.

  • Jie Liang1
  • Shuangli Lan2
  • Shunjie Dang1
  • Guiwen Luo1
  • Yunkang Yang1
  1. 1Department of Bone and Joint Surgery, The Affiliated Hospital, Southwest Medical University, Luzhou, Sichuan 646000, P.R. China.
  2. 2Department of Hepatobiliary Surgery, The Affiliated Hospital, Southwest Medical University, Luzhou, Sichuan 646000, P.R. China.
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Abstract

The present study aimed to identify risk factors for postoperative deep vein thrombosis (DVT) in patients with periarticular knee fractures. A prediction model was constructed for the early identification of high-risk patients. Data from 319 patients at who underwent surgery at the Affiliated Hospital of Southwest Medical University (Luzhou, China) between January 2020 and December 2022 were analyzed. Based on ultrasound results, patients were divided into a DVT group (n=83) and a non-DVT group (n=236). Logistic regression was employed to identify independent risk factors. Subsequently, a nomogram model was established, which was validated via receiver operating characteristic curves, calibration curves and decision curve analysis (DCA). The DVT rate was 26.0%. Old age (OR, 1.0228), high American Society of Anesthesiologists grade (OR, 2.2764), greater blood loss (OR, 1.0015), blood transfusion (OR, 2.7917) and high D-dimer levels (OR, 1.0251) were identified as independent risk factors (P<0.05), while living in cities, high hemoglobin, high prealbumin, high Barthel index and high Braden scores served as protective factors (P<0.05). The model yielded an area under the curve value of 0.837. Additionally, the calibration curve showed high accuracy, and DCA validated the favorable clinical applicability of this predictive model. In conclusion, the model performs well in helping doctors to identify high-risk patients and optimize individualized therapeutic strategies.

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Cite

BibTeXRIS
BibTeX
@article{liang2026analysis,
  title = {{Analysis of risk factors and construction of a prediction model for postoperative deep vein thrombosis in patients with periarticular knee fractures.}},
  author = {Jie Liang and Shuangli Lan and Shunjie Dang and Guiwen Luo and Yunkang Yang},
  journal = {Experimental and therapeutic medicine},
  year = {2026},
  month = oct,
  doi = {10.3892/etm.2026.13328},
  url = {https://europepmc.org/article/MED/42923880},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Analysis of risk factors and construction of a prediction model for postoperative deep vein thrombosis in patients with periarticular knee fractures.
AU  - Jie Liang
AU  - Shuangli Lan
AU  - Shunjie Dang
AU  - Guiwen Luo
AU  - Yunkang Yang
PY  - 2026
DA  - 2026/10/06
JO  - Experimental and therapeutic medicine
DO  - 10.3892/etm.2026.13328
UR  - https://europepmc.org/article/MED/42923880
AB  - The present study aimed to identify risk factors for postoperative deep vein thrombosis (DVT) in patients with periarticular knee fractures. A prediction model was constructed for the early identification of high-risk patients. Data from 319 patients at who underwent surgery at the Affiliated Hospital of Southwest Medical University (Luzhou, China) between January 2020 and December 2022 were analyzed. Based on ultrasound results, patients were divided into a DVT group (n=83) and a non-DVT group (n=236). Logistic regression was employed to identify independent risk factors. Subsequently, a nomogram model was established, which was validated via receiver operating characteristic curves, calibration curves and decision curve analysis (DCA). The DVT rate was 26.0%. Old age (OR, 1.0228), high American Society of Anesthesiologists grade (OR, 2.2764), greater blood loss (OR, 1.0015), blood transfusion (OR, 2.7917) and high D-dimer levels (OR, 1.0251) were identified as independent risk factors (P<0.05), while living in cities, high hemoglobin, high prealbumin, high Barthel index and high Braden scores served as protective factors (P<0.05). The model yielded an area under the curve value of 0.837. Additionally, the calibration curve showed high accuracy, and DCA validated the favorable clinical applicability of this predictive model. In conclusion, the model performs well in helping doctors to identify high-risk patients and optimize individualized therapeutic strategies.
SN  - 1792-0981
SN  - 1792-1015
ER  -