Zhang Chuanliang, Hou Yifang, Wu Xiaohua, Zeng Guowei, Zhao Lei, Xiao Ming
Peking University Shenzhen Hospital, 1120 Lianhua Road, Futian District, Shenzhen, Guangdong, China.
J Robot Surg. 2025 Sep 16;19(1):611. doi: 10.1007/s11701-025-02784-6.
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.
为了确定机器人辅助根治性前列腺切除术(RARP)后下肢深静脉血栓形成(DVT)的独立危险因素,建立风险预测模型,并提出针对性的护理干预策略。对2023年1月至2024年4月期间接受治疗的199例RARP患者的临床资料进行回顾性分析。采用单因素和多因素逻辑回归分析来确定危险因素,随后建立并验证预测模型。非O血型(OR = 3.058)、术前D-二聚体水平升高(OR = 13.729)和术中体温过低(OR = 3.497)被确定为DVT的独立危险因素。复合预测模型的曲线下面积(AUC)为0.777。基于这些发现,制定了包括术中体温管理、早期活动方案和个性化抗凝方案在内的护理策略。护理人员应优先关注高危患者并实施多维度干预措施以降低DVT发生率。这些发现的普遍性可能受到回顾性单中心设计和相对较少的DVT事件数量的限制。