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<p>To identify independent risk factors for lower extremity deep vein thrombosis (DVT) following robot-assisted radical prostatectomy (RARP), develop a risk prediction model, and propose targeted nursing intervention strategies. A retrospective analysis was conducted on clinical data from 199 RARP patients treated between January 2023 and April 2024. Univariate and multivariate logistic regression analyses were employed to identify risk factors, followed by the development and validation of a predictive model. Non-O blood type (OR = 3.058), elevated preoperative D-dimer levels (OR = 13.729), and intraoperative hypothermia (OR = 3.497) were identified as independent risk factors for DVT. The composite prediction model demonstrated an Area Under the Curve (AUC) of 0.777. Based on these findings, nursing strategies including intraoperative temperature management, early mobilization protocols, and personalized anticoagulation regimens were formulated. Nursing practitioners should prioritize high-risk patients and implement multidimensional interventions to reduce DVT incidence. The generalizability of these findings may be limited by the retrospective single-centre design and relatively small number of DVT events.</p>

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Analysis of risk factors for lower extremity DVT following robot-assisted radical prostatectomy and nursing intervention strategies: a single centre retrospective cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月16日

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  • Chuanliang Zhang,
  • Yifang Hou,
  • Xiaohua Wu,
  • Guowei Zeng,
  • Lei Zhao,
  • Ming Xiao

摘要

To identify independent risk factors for lower extremity deep vein thrombosis (DVT) following robot-assisted radical prostatectomy (RARP), develop a risk prediction model, and propose targeted nursing intervention strategies. A retrospective analysis was conducted on clinical data from 199 RARP patients treated between January 2023 and April 2024. Univariate and multivariate logistic regression analyses were employed to identify risk factors, followed by the development and validation of a predictive model. Non-O blood type (OR = 3.058), elevated preoperative D-dimer levels (OR = 13.729), and intraoperative hypothermia (OR = 3.497) were identified as independent risk factors for DVT. The composite prediction model demonstrated an Area Under the Curve (AUC) of 0.777. Based on these findings, nursing strategies including intraoperative temperature management, early mobilization protocols, and personalized anticoagulation regimens were formulated. Nursing practitioners should prioritize high-risk patients and implement multidimensional interventions to reduce DVT incidence. The generalizability of these findings may be limited by the retrospective single-centre design and relatively small number of DVT events.