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Background <p>This study aimed to explore the risk factors for preoperative deep vein thrombosis (DVT) in patients with traumatic lower-extremity fracture (TLEF), analyze the correlation and predictive efficacy of the systemic immune-inflammation index (SII) in DVT development, and establish a corresponding nomogram prediction model.</p> Method <p>This study retrospectively analyzed the clinical data of 950 patients who underwent surgery for TLEF at our hospital between May 2022 and April 2024. The enrolled patients were randomly divided into the training group (665 cases) and the validation group (285 cases) at a 7:3 ratio. We performed univariate and multivariate logistic regression analyses in the training dataset to screen independent risk factors for preoperative DVT in TLEF patients. R Studio was used to construct the nomogram model. The goodness-of-fit test and ROC curve were adopted to evaluate the efficacy of the model.</p> Results <p>Age, fracture site, SII, D-dimer and APTT were identified as independent predictive factors for preoperative DVT. The nomogram exhibited good agreement with the ideal model in both the training and validation datasets.</p> Conclusions <p>The nomogram prediction model for preoperative DVT risk in TLEF patients was developed by incorporating SII and other risk factors. It can assist clinical practitioners in early assessment of DVT development.</p>

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Construction and validation of a nomogram model incorporating the systemic immune-inflammation index for preoperative deep vein thrombosis risk in patients with traumatic lower limb fractures

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Ailing Yang,
  • Yamin Feng,
  • Yang Shen,
  • Sitong Liu,
  • Huaiping Zhao,
  • Hongli Guo,
  • Tiantian Ren,
  • Xiaoli Guan,
  • Zhihong Wei

摘要

Background

This study aimed to explore the risk factors for preoperative deep vein thrombosis (DVT) in patients with traumatic lower-extremity fracture (TLEF), analyze the correlation and predictive efficacy of the systemic immune-inflammation index (SII) in DVT development, and establish a corresponding nomogram prediction model.

Method

This study retrospectively analyzed the clinical data of 950 patients who underwent surgery for TLEF at our hospital between May 2022 and April 2024. The enrolled patients were randomly divided into the training group (665 cases) and the validation group (285 cases) at a 7:3 ratio. We performed univariate and multivariate logistic regression analyses in the training dataset to screen independent risk factors for preoperative DVT in TLEF patients. R Studio was used to construct the nomogram model. The goodness-of-fit test and ROC curve were adopted to evaluate the efficacy of the model.

Results

Age, fracture site, SII, D-dimer and APTT were identified as independent predictive factors for preoperative DVT. The nomogram exhibited good agreement with the ideal model in both the training and validation datasets.

Conclusions

The nomogram prediction model for preoperative DVT risk in TLEF patients was developed by incorporating SII and other risk factors. It can assist clinical practitioners in early assessment of DVT development.