Scholarlink

Background <p>Lower extremity deep vein thrombosis (LEDVT) is a common and serious complication among patients undergoing orthopedic surgery. However, effective tools for preoperative risk assessment are still lacking. In this retrospective study, we analyzed 525 patients who underwent orthopedic surgery at a tertiary hospital between January 2022 and June 2024.</p> Methods <p>Using least absolute shrinkage and selection operator (LASSO) regression followed by multivariate logistic regression, we screened 66 clinical variables encompassing demographic characteristics, comorbidities, laboratory results, and imaging findings. Six independent predictors were identified: history of constipation, history of deep vein thrombosis, recent major surgery, absolute lymphocyte count, albumin level, and D-dimer level.</p> Results <p>A nomogram was developed based on these predictors to estimate the risk of preoperative LEDVT. The model showed good discriminative ability, with an area under the ROC curve (AUC) of 0.756 in the training cohort and 0.766 in the validation cohort, along with satisfactory calibration. Net reclassification improvement (NRI)and integrated discrimination improvement (IDI) analyses confirmed significant gains in risk reclassification, while decision curve analysis (DCA) indicated a greater net clinical benefit compared with a simplified reference model.</p> Conclusion <p>These findings suggest that the proposed nomogram provides a concise and practical tool for individualized risk prediction, enabling clinicians to identify high-risk patients and optimize perioperative management strategies. Although further validation in large, multicenter prospective studies is warranted, this model has the potential to improve clinical decision-making and patient outcomes by facilitating early recognition and prevention of LEDVT in orthopedic populations.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development and evaluation of a risk prediction model for preoperative lower extremity deep vein thrombosis in orthopedic patients

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月20日

查看全文
  • Cuiqing Liu,
  • Ruihua Huang,
  • Jie Hong,
  • Wenyan Chen,
  • Yulu Hao,
  • Binming Zhu,
  • Minghui Tan,
  • Yanfei Li,
  • Yichun Wang,
  • Hong Zhang,
  • Jun Lyu,
  • Shutong Tang,
  • Weiju Chen

摘要

Background

Lower extremity deep vein thrombosis (LEDVT) is a common and serious complication among patients undergoing orthopedic surgery. However, effective tools for preoperative risk assessment are still lacking. In this retrospective study, we analyzed 525 patients who underwent orthopedic surgery at a tertiary hospital between January 2022 and June 2024.

Methods

Using least absolute shrinkage and selection operator (LASSO) regression followed by multivariate logistic regression, we screened 66 clinical variables encompassing demographic characteristics, comorbidities, laboratory results, and imaging findings. Six independent predictors were identified: history of constipation, history of deep vein thrombosis, recent major surgery, absolute lymphocyte count, albumin level, and D-dimer level.

Results

A nomogram was developed based on these predictors to estimate the risk of preoperative LEDVT. The model showed good discriminative ability, with an area under the ROC curve (AUC) of 0.756 in the training cohort and 0.766 in the validation cohort, along with satisfactory calibration. Net reclassification improvement (NRI)and integrated discrimination improvement (IDI) analyses confirmed significant gains in risk reclassification, while decision curve analysis (DCA) indicated a greater net clinical benefit compared with a simplified reference model.

Conclusion

These findings suggest that the proposed nomogram provides a concise and practical tool for individualized risk prediction, enabling clinicians to identify high-risk patients and optimize perioperative management strategies. Although further validation in large, multicenter prospective studies is warranted, this model has the potential to improve clinical decision-making and patient outcomes by facilitating early recognition and prevention of LEDVT in orthopedic populations.