机器人外科学杂志（中英文）2025，Vol.6Issue(7)：1128-1134,7.DOI:10.12180/j.issn.2096-7721.2025.07.009
机器人辅助根治性前列腺切除术后静脉血栓栓塞症预测模型的构建与验证
Construction and validation of a predictive model for venous thromboembolism after robot-assisted radical prostatectomy
摘要
Abstract
Objective:To analyze risk factors for venous thromboembolism(VTE)in patients undergoing Da Vinci robot-assisted radical prostatectomy(RARP)and develop a predictive model for clinical prevention guidance.Methods:Clinical data of patients who underwent RARP at the First Affiliated Hospital of Air Force Medical University from May 2022 to December 2023 were retrospectively analyzed.Patients with postoperative VTE were assigned to the VTE group(n=27),and non-VTE patients served as the control group(n=54).Basic information,imaging data,laboratory test results,and surgical records of patients were collected.Univariate and multivariate logistic regression were performed to identify independent VTE risk factors,and a predictive model was constructed.Results:The VTE group showed significantly higher mean age,BMI,postoperative bedridden duration,proportion of hypertension history,proportion of prior surgery history,and Caprini scores compared to the control group(P<0.05).Multivariate analysis confirmed age,postoperative bedridden days,prior surgery history,and Caprini score as independent predictors(P<0.01).The predictive model Logit(P)=0.091×Age+0.773×Bedridden days+1.571×Prior surgery+1.472×Caprini score-19.998 showed excellent predictive performance.Conclusion:The VTE risk prediction model established in this study suggests intensified monitoring for patients with advanced age,prolonged postoperative immobilization,prior surgical history,or elevated Caprini scores to mitigate VTE risk.关键词
机器人辅助手术/前列腺癌/静脉血栓栓塞症/风险预测模型/防护措施Key words
Robot-assisted Surgery/Prostate Cancer/Venous Thromboembolism/Risk Prediction Model/Prevention Procedure分类
医药卫生引用本文复制引用
侯敏,韩东晖,张梦娇,吴东娟..机器人辅助根治性前列腺切除术后静脉血栓栓塞症预测模型的构建与验证[J].机器人外科学杂志（中英文）,2025,6(7):1128-1134,7.基金项目
国家自然科学基金(82202933)National Natural Science Foundation of China(82202933) （82202933）
