中国血液净化

中国血液净化 ›› 2025, Vol. 24 ›› Issue (06): 469-473.doi: 10.3969/j.issn.1671-4091.2025.06.006

• 临床研究 • 上一篇    下一篇

腹膜透析导管出口感染的危险因素评估

王怡璇   张晓兰   刘识鉴   赵 耀   鲍运霞   刘国建   纪天蓉   孔凡武   

  1. 150001 哈尔滨， 1哈尔滨医科大学附属第二医院肾内科
  • 收稿日期:2024-12-16 修回日期:2025-01-28 出版日期:2025-06-12 发布日期:2025-06-12
  • 通讯作者: 孔凡武 E-mail:kidney1979@163.com
  • 基金资助:
    PRO润基金青年基金(KYJ202206—0003-12)

Risk factor assessment for peritoneal dialysis catheter exit-site infections

WANG Yi-xuan 1, ZHANG Xiao-lan , LIU Shi-jian , ZHAO Yao, BAO Yun-xia, LIU Guo-jian, JI Tian-rong, KONG Fan-wu   

  1. Department of Nephrology, The Second-Affiliated Hospital of Harbin Medical University, Harbin 150001,China 
  • Received:2024-12-16 Revised:2025-01-28 Online:2025-06-12 Published:2025-06-12
  • Contact: 150001 哈尔滨，1哈尔滨医科大学附属第二医院肾内科 E-mail:kidney1979@163.com

摘要： 目的 探究腹膜透析(peritoneal dialysis，PD)患者导管出口处感染(exit site infection，ESI)的相关危险因素，构建预测模型，便于早期识别高风险患者，制定个性化的预防策略。 方法 选取2023年10月—2024年11月在哈尔滨医科大学附属第二医院腹膜透析门诊定期随访的PD患者为研究对象。根据2023年国际腹膜透析学会(International Society for Peritoneal Dialysis，ISPD)ESI的诊断标准，将其分为PD导管ESI组和非ESI组。采用二元Logistic回归筛选ESI的危险因素，建立ESI风险列线图模型，并评估模型预测效率和临床适用性。 结果 共纳入245例患者，其中ESI组37例，非ESI组208例。ESI组患者的年龄(Z=-4.199,P＜0.001)、透析龄(Z=-4.908,P＜0.001)、血红蛋白 (Z=-4.445,P＜0.001)、血清白蛋白(Z=-5.271,P＜0.001)，外Cuff脱出人数(χ2=27.038，P＜0.001)、腹膜透析导管曾发生牵拉(χ2=24.797，P＜0.001)与非ESI组比较差异有统计学意义。二元Logistic回归分析结果显示：年龄(OR=0.796，95% CI：0.705～0.899，P＜0.001)、透析龄(OR=1.036，95% CI：1.010～1.064，P=0.007)、血红蛋白(OR=0.951，95% CI：0.914～0.990，P=0.013)、血清白蛋白(OR=0.796，95% CI：0.705～0.899，P＜0.001)、外Cuff是否脱出(OR=6.636，95% CI：1.156～38.114，P=0.034)、腹膜透析导管是否曾发生牵拉(OR=6.530，95% CI：1.275～33.454，P=0.024)为发生腹膜透析导管出口处感染的独立危险因素。根据Logistic回归分析建立的列线图风险预测模型显示出较好的预测能力，AUC为0.935 (95% CI：0.8916～0.978)。Hosmer-Lemeshow拟合优度检验结果(χ²=12.712,P=0.122)及临床决策曲线分析均表明预测模型具有较好的拟合一致性和较高的临床应用价值。 结论 年龄、透析龄、血红蛋白水平、血清白蛋白水平、外Cuff距出口距离、外Cuff脱出及腹膜透析导管是否曾发生牵拉是发生ESI的独立危险因素。本研究建立的ESI风险预测模型可有效评估腹膜透析导管出口感染的发生风险，为临床早期干预和个性化管理提供科学依据。

关键词: 腹膜透析, 腹膜透析导管出口处感染, 危险因素, 预测价值R459.5

Abstract: Objective  To investigate risk factors for exit site infection (ESI) in peritoneal dialysis (PD) patients, develop a predictive model for early identification of high-risk individuals, and guide personalized preventive strategies. Methods PD patients regularly followed at the peritoneal dialysis clinic of the Second Affiliated Hospital of Harbin Medical University from October 2023 to November 2024 were enrolled. Based on the 2023 International Society for Peritoneal Dialysis (ISPD) diagnostic criteria for ESI, patients were categorized into ESI and non-ESI groups. Binary logistic regression was used to identify ESI risk factors, followed by the construction of a nomogram model. Model performance was evaluated through predictive accuracy and clinical applicability. Results  Among 245 patients, 37 were classified into the ESI group and 208 into the non-ESI group. Significant differences were observed between groups in age (Z =-4.199, P ＜0.001), dialysis vintage (Z =-4.908, P ＜0.001), hemoglobin (Z =-4.445, P ＜0.001), serum albumin (Z =-5.271, P ＜0.001), external cuff extrusion (χ²=27.038, P ＜0.001), and history of catheter traction (χ²=24.797, P ＜0.001). Binary logistic regression identified. Based on binary logistic regression analysis, age (OR=0.796, 95% CI: 0.705～0.899, P＜0.001), dialysis vintage (OR=1.036, 95% CI: 1.010～1.064, P=0.007), hemoglobin (OR=0.951, 95% CI:0.914～0.990, P=0.013), serum albumin (OR=0.796, 95% CI: 0.705～0.899, P＜0.001), external cuff extrusion (OR=6.636, 95% CI:1.156～38.114, P=0.034), and history of catheter traction (OR=6.530, 95% CI: 1.275～33.454, P=0.024) were identified as independent risk factors for peritoneal dialysis catheter exit-site infection. The nomogram model demonstrated excellent predictive performance (AUC= 0.935, 95% CI: 0.8916～0.978). Hosmer-Lemeshow goodness-of-fit test (χ²=12.712, P=0.122) and decision curve analysis confirmed robust calibration and clinical utility.  Conclusion Age, dialysis vintage, hemoglobin, serum albumin, external cuff distance, external cuff extrusion, and catheter traction are independent risk factors for ESI. The developed predictive model effectively stratifies ESI risk in PD patients, providing a scientific basis for early intervention and personalized management.

Key words: Peritoneal dialysis, Peritoneal dialysis catheter exit-site infection, Risk factors, Predictive value

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