心脏手术中的医护人员相关感染:流行病学特征

E. E. Sadovnikov, N. Potseluev, O. Barbarash, E. B. Brusina
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摘要

目的确定一家心脏外科医院 2018 年至 2022 年所有入院手术患者的 HAI 流行病学特征,以实施基于风险的预防策略。从2018年到2022年,对一家大型心脏外科医院的患者(n = 6179)的HAI流行过程进行了描述性回顾流行病学研究。计算了分层指标。为显示未知关系并进行预测,进行了傅立叶频谱分析,随后使用了人工智能技术--神经网络。使用了 STATISTICA 自动神经网络(SANN)工具以及 StatTech v. 3.0.5。5 年间,HAI 的平均发生率为每 1000 个患者日 4.22 例。我们发现 HAIs 呈下降趋势。心肺旁路手术(CBS)的 HCAI 发生率是无 CBS 的 3 倍(分别为每 1000 个患者日 4.68 例和 1.51 例)。傅立叶分析显示,肺炎克雷伯氏菌占主导地位,其发病率呈 10、20、30 周期性变化,而其他病原体则没有相同的时间序列。神经网络建模技术没有发现适合描述预测的神经网络。肺炎克雷伯菌显示了医院人群的典型特征,导致了 35.49% 的 HAIs 病例,74.45% 的病例对抗生素具有多重耐药性,超过一半的菌株具有扩展耐药性,10.21% 的菌株具有泛耐药性。鲍曼不动杆菌(Acinetobacter baumanii)也表现出较高的流行活性,导致了近五分之一的 HAIs 病例,但其抗菌药耐药性特征不如肺炎克雷伯菌明显。HCAI 流行过程的流行病学特征是风险识别的必备要素之一。心脏外科医院 HCAI 流行过程的动态特征、风险群体和时间、微生物群的结构和特征应在 HCAI 风险管理系统中予以考虑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Healthcare-associated infections in cardiac surgery: epidemiological features
Aim. To identify the epidemiological features of HAIs in all patients admitted for surgery from 2018 to 2022. in a cardiac surgery hospital for the implementation of a risk-based prevention strategy.Materials and Methods. A descriptive retrospective epidemiological study of the HAI epidemic process was performed from 2018 to 2022. in patients of a large cardiac surgery hospital (n = 6179). Stratified indicators were calculated. To display unknown relationships and make a forecast, Fourier spectral analysis was performed, followed by the use of artificial intelligence technology - neural networks. The STATISTICA Automated Neural Networks (SANN) tool was used, as well as the StatTech v. 3.0.5.Results. The average rate of HAIs incidence over a 5-year period was 4.22 per 1000 patient days. We revealed decreasing trend of HAIs. Incidence of HCAI cardiopulmonary bypass surgery (CBS) was 3 times higher than without CBS (4.68 and 1.51 per 1000 patient-days, respectively). Fourier analysis revealed 10, 20, 30 cyclicity due to the dominant Klebsiella pneumoniae without the same time-series for other pathogens. The technology of neural network modeling did not reveal neural networks suitable for describing the forecast. Klebsiella pneumoniae showed properties typical of the hospital population and caused 35.49% of all cases of HAIs, had multidrug resistance to antibiotics in 74.45% of cases, with more than half of the strains having extended resistance, and 10.21% were pan-resistant. Acinetobacter baumanii also showed high epidemic activity, causing almost a fifth of all cases of HAIs, although its antimicrobial resistance characteristics were less pronounced than those of Klebsiella pneumoniae.Conclusion. The epidemiological characteristics of the epidemic process of HCAI is one of the mandatory components of risk identification. The identified features of the dynamics of the epidemic process of HCAI in a cardiac surgery hospital, risk groups and time, the structure and characteristics of the microbiota should be taken into account in the HCAI risk management system.
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