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| 新生儿重症监护病房多重耐药菌反复感染的危险因素分析 |
| Risk factors analysis of repeated infection of multiple drug-resistant bacteria in neonatal intensive care unit |
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| DOI：10.3969/j.issn.1007-8134.2023.03.010 |
| 中文关键词: 多重耐药菌 反复感染 贝叶斯网络模型 危险因素 |
| 英文关键词: multiple resistant bacteria repeated infection Bayesian network risk factor |
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| 中文摘要: |
| 目的?探讨新生儿重症监护病房多重耐药菌反复感染的危险因素。方法?回顾性分析2018年11月—2021年11月我院收治的100例多重耐药菌感染的新生儿，根据治疗后感染情况分为反复感染组（20例）和非反复感染组（80例）。分析患儿的感染部位和病原菌种类；比较2组患儿的一般资料；多因素Logistic回归分析影响患儿反复感染的危险因素；构建贝叶斯网络模型，并使用Netica软件进行贝叶斯网络推理；采用ROC曲线和校准曲线评价模型的区分度和准确度。结果?100例患儿主要以呼吸道感染为主，检测出多重耐药菌菌株共158株，其中革兰阴性菌占75.95%，革兰阳性菌占24.05%。多因素Logistic回归分析结果显示，住院天数≥7 d、机械通气、抗菌药物种类≥3种和抗菌药物使用时间≥7 d是患儿反复感染的独立危险因素，而出生胎龄≥37周、出生体质量≥2500 g是患儿反复感染的保护因素（P＜0.05）。ROC曲线和校准曲线显示贝叶斯网络模型具有良好的区分度和准确度。结论?临床应对患儿胎龄、出生体质量、住院天数等危险因素进行重点关注，以降低多重耐药菌反复感染的发生率。 |
| 英文摘要: |
| Objective To investigate the risk factors of repeated infection of multiple resistant bacteria in neonatal intensive care unit. Methods?A retrospective analysis was conducted on 100 neonates admitted to our hospital between November 2018 and November 2021, who were infected with multiple drug-resistant bacteria. Based on the post-treatment infection, they were classified into two groups: the recurrent infection group (20 cases) and the non-recurrent infection group (80 cases). The site of infection and the pathogen species were analyzed, and the general data of both groups were compared. Multivariate logistic regression was performed to identify risk factors associated with recurrent infections. Furthermore, a Bayesian network model was constructed using Netica software and used for inference. The model’ s differentiation and accuracy were evaluated using receiver operating characteristic (ROC) curve and calibration curve. Results?Among the 100 children included in this study, respiratory tract infections were the most common. A total of 158 strains of multiple drug-resistant bacteria were detected, with gram-negative bacteria accounting for 75.95% and gram-positive bacteria accounting for 24.05%. Multivariate Logistic regression analysis showed that the length of hospital stay≥7 d, mechanical ventilation, types of antibiotics≥3 and duration of antibiotics≥7 d were independent risk factors for recurrent infection, while gestational age≥37 weeks and birth weight≥2500 g were protective factors for recurrent infection (P ＜ 0.05). ROC curve and calibration curve show that the Bayesian network model has good discrimination and accuracy. Conclusion?Clinical attention should be paid to gestational age, birth weight, length of hospital stay and other risk factors in order to reduce the incidence of multiple resistant bacteria repeated infection. |
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