手术患者医源性感染的时间模式和临床特征:中国一家大型三级医院的回顾性研究

Tianyi Zhang , Li Yan , Shan Wang , Ming Chen , Yunda Jiao , Zhuoqi Sheng , Jianchao Liu , Lihua Liu
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引用次数: 0

摘要

背景鉴于大多数医疗相关感染(HAIs)都是可以预防的,因此了解其特征和时间模式对减少其发生至关重要。方法对一家中国医院信息系统中2010年至2019年外科住院患者的病历封面进行了回顾性分析。采用关联规则挖掘(ARM)来探索疾病、手术和 HAIs 之间的关联。连接点模型用于估计每年的 HAI 趋势。研究纳入了10年间623,290名手术住院患者的数据,观察到HAI率显著下降。与未发生 HAI 的患者相比,发生 HAI 的患者住院时间更长(29 天对 9 天),医疗费用更高(96226.57 元对 22351.98 元),死亡风险更高(6.42% 对 0.18%)。虽然骨髓和脾脏手术是大多数 HAI 类型中最常见的手术,但每种 HAI 类型中最常见的疾病各不相同。ARM 发现,一些不常见的诊断可能与 HAI 密切相关。每种 HAI 的时间序列模式各不相同,呼吸系统感染的高峰期出现在 1 月份,手术部位感染和血流感染的高峰期分别出现在 8 月和 7 月。每种 HAI 的时间序列模式各不相同,这凸显了针对特定类型的 HAI 采取有针对性的监控策略的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Temporal patterns and clinical characteristics of healthcare-associated infections in surgery patients: A retrospective study in a major Chinese tertiary hospital

Temporal patterns and clinical characteristics of healthcare-associated infections in surgery patients: A retrospective study in a major Chinese tertiary hospital

Background

Given the preventable nature of most healthcare-associated infections (HAIs), it is crucial to understand their characteristics and temporal patterns to reduce their occurrence.

Methods

A retrospective analysis of medical record cover pages from a Chinese hospital information system was conducted for surgery inpatients from 2010 to 2019. Association rules mining (ARM) was employed to explore the association between disease, procedure, and HAIs. Joinpoint models were used to estimate the annual HAI trend. The time series of each type of HAI was decomposed to analyze the temporal patterns of HAIs.

Results

The study included data from 623,290 surgery inpatients over 10 years, and a significant decline in the HAI rate was observed. Compared with patients without HAIs, those with HAIs had a longer length of stay (29 days vs. 9 days), higher medical costs (96226.57 CNY vs. 22351.98 CNY), and an increased risk of death (6.42% vs. 0.18%). The most common diseases for each type of HAI differed, although bone marrow and spleen operations were the most frequent procedures for most HAI types. ARM detected that some uncommon diagnoses could strongly associate with HAIs. The time series pattern varied for each type of HAI, with the peak occurring in January for respiratory system infections, and in August and July for surgical site and bloodstream infections, respectively.

Conclusions

Our findings demonstrate that HAIs impose a significant burden on surgery patients. The differing time series patterns for each type of HAI highlight the importance of tailored surveillance strategies for specific types of HAI.

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