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河北医学  2023, Vol. 29 Issue (4): 572-578    DOI: 10.3969/j.issn.1006-6233.2023.04.09
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广西某三甲医院获得性军团菌感染现状及其风险因素分析
丁凯宏, 冯玉青, 唐芳玫, 刁志宏
广西中医药大学附属瑞康医院, 广西 南宁 530011
Analysis of the Current Situation of Acquired Legionella Infection and Its Risk Factors in a Tertiary Care Hospital in Guangxi
DING Kaihong, FENG Yuqing, TANG Fangmei, et al
Ruikang Hospital, Guangxi University of Traditional Chinese Medicine, Guangxi Nanning 530011, China
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摘要 目的: 调查分析广西某三甲医院获得性军团菌感染现状及其风险因素。方法: 选取2021年6月至2022年6月广西某三甲医院480例住院患者,统计军团菌感染情况,通过单因素、Logistic多因素模型分析军团菌感染的影响因素,基于影响因素构建Logistic预测模型,通过受试者工作特征(ROC)曲线评价该模型预测军团菌感染发生的价值。结果: 480例住院患者中军团菌感染率为12.08%(58/480),其中41.38%(32/58)合并其他细菌或真菌感染;发生军团菌感染患者年龄及吸烟、糖尿病、呼吸系统疾病、夏秋季节入院、近期使用集中空调、应用免疫抑制剂占比均高于未发生军团菌感染患者(P<0.05);年龄(OR=1.633)、吸烟(OR=2.297)、糖尿病(OR=2.512)、呼吸系统疾病(OR=5.118)、夏秋季节入院(OR=5.638)、近期使用集中空调(OR=4.710)、应用免疫抑制剂(OR=3.933)均为军团菌感染的影响因素(P<0.05);基于影响因素构建Logistic预测模型为:Logit(P)=-8.054+0.490×年龄+0.832×吸烟+0.921×糖尿病+1.633×呼吸系统疾病+1.730×夏秋季节入院+1.550×近期使用集中空调+1.369×应用免疫抑制剂;该模型预测军团菌感染发生的曲线下面积(AUC)为0.918(95%CI为0.890~0.941),截断值为0.163,预测敏感度、特异度分别为89.67%、84.65%。结论: 广西某三甲医院获得性军团菌感染发生率为12.08%,其影响因素包括年龄、吸烟、糖尿病、呼吸系统疾病、夏秋季节入院、近期使用集中空调、应用免疫抑制剂,根据影响因素构建Logistic预测模型可辅助临床筛查军团菌感染高危患者。
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丁凯宏
冯玉青
唐芳玫
刁志宏
关键词 军团菌感染影响因素预测模型    
AbstractObjective: To investigate and analyze the current status of acquired Legionella infection and its risk factors in a tertiary care hospital in Guangxi. Methods: 480 inpatients in a tertiary care hospital in Guangxi from June 2021 to June 2022 were selected, Legionella infections were counted, the influencing factors of Legionella infections were analyzed by single-factor and logistic multi-factor models, a logistic prediction model was constructed based on the influencing factors, and the value of the model in predicting the occurrence of Legionella infections was evaluated by subject work characteristic (ROC) curves. Results: The rate of Legionella infection was 12.08% (58/480) in 480 hospitalized patients, of which 41.38% (32/58) were combined with other bacterial or fungal infections; the age of patients who developed Legionella infection and the proportion of patients who smoked, had diabetes, had respiratory diseases, were admitted in summer and autumn, had recently used centralized air conditioning, and applied immunosuppressive drugs were higher than those who did not develop Legionella infection (P<0.05). Age (OR=1.633), smoking (OR=2.297), diabetes (OR=2.512), respiratory disease (OR=5.118), admission in summer and autumn (OR=5.638), recent use of centralized air conditioning (OR=4.710), and application of immunosuppressive drugs (OR=3.933) were all factors influencing Legionella infection (P<0.05). The logistic prediction model was constructed based on the influencing factors as: Logit (P) = -8.054 + 0.490 × age + 0.832 × smoking + 0.921 × diabetes mellitus + 1.633 × respiratory disease + 1.730 × hospital admission in summer and autumn + 1.550 × recent use of centralized air conditioning + 1.369 × application of immunosuppressive drugs. The model predicted the occurrence of Legionella infection with an area under the curve (AUC) of 0.918 (95% CI 0.890 to 0.941), a cut-off value of 0.163, and a predictive sensitivity and specificity of 89.67% and 84.65%, respectively. Conclusion: The incidence of acquired Legionella infection in a tertiary care hospital in Guangxi was 12.08%, and the influencing factors included age, smoking, diabetes, respiratory diseases, admission in summer and autumn, recent use of centralized air conditioning, and application of immunosuppressive drugs, and the construction of a Logistic prediction model based on the influencing factors could assist in clinical screening of patients at high risk of Legionella infection.
Key wordsLegionella    Infection    Influencing factors    Predictive model
    
基金资助:广西卫生健康委员会自筹经费科研课题,(编号:Z20190283)
引用本文:   
丁凯宏, 冯玉青, 唐芳玫, 刁志宏. 广西某三甲医院获得性军团菌感染现状及其风险因素分析[J]. 河北医学, 2023, 29(4): 572-578.
DING Kaihong, FENG Yuqing, TANG Fangmei, et al. Analysis of the Current Situation of Acquired Legionella Infection and Its Risk Factors in a Tertiary Care Hospital in Guangxi. HeBei Med, 2023, 29(4): 572-578.
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http://www.hbyxzzs.cn/CN/10.3969/j.issn.1006-6233.2023.04.09     或     http://www.hbyxzzs.cn/CN/Y2023/V29/I4/572
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