机器人辅助宫颈癌根治术后基线NLR、PLR与尿路感染的关系及其风险预测研究  

Relationship between baseline NLR,PLR and urinary tract infection after robot-assisted radical surgery for cervical cancer and its risk prediction

作　　者：方前进[1] 桑琳[1] 王润秋[1] 王青元[2] FANG Qianjin;SANG Lin;WANG Runqiu;WANG Qingyuan(Department of Obstetrics and Gynecology,the Second People’s Hospital of Hefei/Hefei Hospital Affiliated to Anhui Medical University,Hefei 230011,China;Department of Obstetrics and Gynecology,the Second Hospital of Anhui Medical University,Hefei 230000,China)

机构地区：[1]合肥市第二人民医院·安徽医科大学附属合肥医院妇产科,安徽合肥230011 [2]安徽医科大学第二附属医院妇产科,安徽合肥230000

出　　处：《机器人外科学杂志(中英文)》2025年第3期464-470,共7页Chinese Journal of Robotic Surgery

基　　金：安徽省临床医学研究转化专项科研项目(202304295107020093);2022年度安徽省妇幼保健协会“母婴营养与健康研究项目”(JKZD202206)。

摘　　要：目的:探讨机器人辅助宫颈癌根治术后基线中性粒细胞与淋巴细胞比值(NLR)、血小板与淋巴细胞比值(PLR)与尿路感染的关系,构建尿路感染风险预测模型并进行效能分析。方法:选取2021年8月—2024年8月于本中心行机器人辅助宫颈癌根治术患者100例,根据术后是否并发尿路感染分为发生组(n=29)与未发生组(n=71)。比较两组术后基线NLR、PLR水平差异,采用二元Logistic回归分析尿路感染的影响因素,建立ROC曲线分析NLR、PLR及两项联合对尿路感染的预测效能。结果:发生组术后基线NLR、PLR水平均高于未发生组(P<0.05)。术后尿潴留、尿管留置时间长、术后基线NLR越高、PLR越高均为尿路感染的独立危险因素(P<0.05)。根据危险因素构建术后尿路感染风险预测列线图模型,ROC曲线分析显示,术后NLR、PLR、两项联合及列线图模型预测尿路感染的预测曲线AUC分别为0.827、0.794、0.920、0.981,Delong法检验显示,两项联合的AUC均高于单项的AUC(P<0.05),列线图模型的AUC均高于NLR、PLR及两项联合的AUC(P<0.05)。当取cut-off值时,两项联合的灵敏度、特异度分别为0.828、0.944;列线图模型的灵敏度、特异度分别为0.931、0.958。经内部验证显示列线图模型稳定性良好且有正向净收益率。结论:机器人辅助宫颈癌根治术后基线NLR、PLR为患者术后尿路感染的危险因素,临床中NLR、PLR监测结合术后尿潴留、尿管留置时间构建的列线图模型可较好地评估尿路感染风险。Objective:To investigate the relationship between baseline neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR)and urinary tract infection(UTI)after robot-assisted radical cervical cancer surgery,and to develop a risk prediction model for UTI.Methods:A total of 100 patients who underwent robot-assisted radical cervical cancer surgery at our center from August 2021 to August 2024 were enrolled.They were divided into the UTI group(n=29)and the non-UTI group(n=71)based on whether postoperative UTI occurred or not.Differences in postoperative baseline NLR and PLR levels between the two groups were compared.Binary Logistic regression analysis was performed to identify risk factors for UTI.Receiver operating characteristic(ROC)curves were constructed to assess the predictive efficacy of NLR,PLR,and their combination for UTI.Results:Postoperative baseline NLR and PLR levels were significantly higher in the UTI group than those in the non-UTI group(P<0.05).Postoperative urinary retention,prolonged catheterization time,elevated baseline NLR,and elevated PLR were identified as independent risk factors for UTI(P<0.05).A nomogram model for UTI risk prediction was constructed based on these factors.ROC analysis showed that the AUC values for NLR,PLR,their combination,and the nomogram model in predicting UTI were 0.827,0.794,0.920,and 0.981,respectively.DeLong’s test revealed that the combined AUC was significantly higher than those of individual markers(P<0.05),and the nomogram model’s AUC was significantly higher than those of NLR,PLR,and their combination(P<0.05).At the optimal cut-off value,the combined markers achieved a sensitivity and specificity of 0.828 and 0.944,respectively,while the nomogram model achieved 0.931 and 0.958.Internal validation confirmed the nomogram model’s stability and positive net benefit.Conclusion:Elevated postoperative baseline NLR and PLR are risk factors for UTI after robot-assisted radical cervical cancer surgery.A nomogram model incorporating NLR,PLR,postoperative uri

关 键 词：宫颈癌根治术 机器人辅助手术 腹腔镜手术 尿路感染 风险预测模型 

分 类 号：R737.33[医药卫生—肿瘤]