摘要
目的:探讨体外膜肺氧合（ECMO）治疗患者相关血流感染的危险因素，并建立风险预测模型。方法:回顾性分析2016年1月至2021年12月浙江大学医学院附属杭州市第一人民医院ICU接受ECMO辅助治疗的108例患者的临床资料，分析ECMO相关血流感染率及感染病原菌分布。采用单因素及多因素Logistic回归分析ECMO相关血流感染的危险因素，并建立风险预测模型，应用Hosmer-Lemeshow检验评估模型拟合度，受试者工作特征（ROC）曲线分析Logistic回归模型的预测价值。结果:108例患者中，发生ECMO相关血流感染31例，感染率28.70%；共应用ECMO治疗1 076 d，ECMO相关血流感染率为28.81/千ECMO日。31例ECMO相关血流感染患者共分离病原菌43株，其中革兰阳性菌15株（34.88%），革兰阴性菌24株（55.81%），真菌4株（9.30%）。多因素Logistic回归分析结果显示，ECMO运行时间[比值比（OR）=1.154，95%置信区间（CI）（1.013，1.314），P = 0.031]、血管内导管数≥ 4根[OR = 8.607，95%CI（2.176，34.046），P = 0.002]、主动脉内球囊反搏术应用[OR = 4.467，95%CI（1.111，17.957），P = 0.035]、连续性肾脏替代治疗[OR = 4.963，95%CI（1.241，19.843），P = 0.023]、降钙素原[OR = 1.052，95%CI（1.004，1.103），P = 0.035]均是ECMO治疗患者相关血流感染的独立危险因素。根据Logistic回归结果建立风险预测模型，经Hosmer-Lemeshow检验拟合度较高（χ2 = 3.672，P = 0.885）。ROC曲线分析结果显示，ECMO相关血流感染的风险预测模型预测使用ECMO治疗后发生血流感染的曲线下面积为0.910[95%CI（0.856，0.963），P < 0.001]。结论:血流感染是ECMO支持治疗的常见危重并发症，主要感染病原菌是革兰阴性菌。多因素Logistic回归模型可较好地预测ECMO相关血流感染发生，临床应加强ECMO相关血流感染危险因素监测并制定相应的预防控制措施，降低ECMO相关血流感染的发生率，改善患者预后。
Abstract
Objective:To analyze risk factors of bloodstream infection in patients treated with extracorporeal membrane oxygenation (ECMO), and to establish a risk prediction model.Methods:The clinical data of 108 patients receiving ECMO adjuvant therapy in the ICU of Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine from January 2016 to December 2021 were retrospectively analyzed. The incidence of ECMO-related bloodstream infection and distribution of pathogens were explored, univariate analysis and multivariate Logistic regression analysis were performed for risk factors of ECMO-related bloodstream infection, and then a risk prediction model was established. The fitting degree of the model was evaluated by the Hosmer-Lemeshow test, and its predictive value was analyzed by means of the receiver operating characteristic (ROC) curve.Results:Of 108 patients receiving ECMO, 31 patients experienced ECMO-related bloodstream infection, with an infection rate of 28.70%. The ECMO treatment was applied for 1 076 d, and the ECMO-related bloodstream infection rate was 28.81 per thousand ECMO days. In these 31 patients, a total of 43 pathogens were isolated, including 15 strains of gram-positive bacteria (34.88%), 24 strains of gram-negative bacteria (55.81%) and four strains of fungus (9.30%). The multivariate Logistic regression analysis showed that the ECMO running time [odds ratio (OR) = 1.154, 95% confidence interval (CI) (1.013, 1.314), P = 0.031], number of intravascular catheters ≥ 4 [OR = 8.607, 95%CI (2.176, 34.046), P = 0.002], intra-aortic balloon pump application [OR = 4.467, 95%CI (1.111, 17.957), P = 0.035], continuous renal replacement therapy treatment [OR = 4.963, 95%CI (1.241, 19.843), P = 0.023] and procalcitonin [OR = 1.052, 95%CI (1.004, 1.103), P = 0.035] were independent risk factors for ECMO-related bloodstream infection. A risk prediction model was established based on these Logistic regression results, with a high goodness-of-fit according to the Hosmer-Lemeshow test (χ2 = 3.672, P = 0.885). ROC curve analysis showed that the area under the curve for the risk prediction model of ECMO-related bloodstream infection was 0.910 [95%CI (0.856, 0.963), P < 0.001].Conclusions:Bloodstream infection is one of common critical complications in ECMO treatment. The gram-negative bacteria are dominant among the pathogens causing infection. The multivariate Logistic regression model can achieve favourable effect on the prediction of ECMO-related bloodstream infection. In clinical practice, the monitoring of risk factors of ECMO-related bloodstream infection should be strengthened and corresponding preventive control measures should be formulated to reduce its incidence and improve patients' prognosis.
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