van Kessel S A M, Wielders C C H, van de Kassteele J, Verbon A, Schoffelen A F
Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
J Hosp Infect. 2025 Aug;162:62-67. doi: 10.1016/j.jhin.2025.04.025. Epub 2025 May 6.
Despite the low prevalence of infections due to vancomycin-resistant enterococci (VRE) in the Netherlands, VRE is a frequent source of hospital outbreaks. We investigated whether a Poisson hidden Markov model (PHMM) can detect in-hospital VRE outbreaks in routine data from the Dutch Infectious Diseases Surveillance Information System for Antimicrobial Resistance (ISIS-AR).
We performed a retrospective data linkage study from 2013 up to 2023, including data from 89 hospitals on VRE isolates from ISIS-AR. A PHMM was used to detect potential outbreaks based on weekly VRE counts at hospital level. Per week t, the model provides the probability P that the observed count arose from an outbreak. Thresholds of P(t) >0.5, P(t) >0.7, and P(t) >0.9 for at least two consecutive weeks were used. The PHMM's results were compared with outbreaks voluntarily reported to the 'Early warning and response meeting on highly resistant microorganism outbreaks in healthcare institutes'. Detection percentages were calculated and VRE counts of reported but undetected outbreaks, and detected but unreported outbreaks were described.
Of the 85 reported outbreaks, the model detected 87%, 86%, and 81% for thresholds P(t) >0.5, P(t) >0.7, and P(t) >0.9, respectively. Undetected outbreaks were mainly small outbreaks. The PHMM detected 66, 55, and 44 unreported potential outbreaks, respectively, with 44%, 35%, and 30% involving only one to two VRE-positive patients.
Overall, the PHMM shows potential for detecting in-hospital VRE outbreaks in routine surveillance data, with high detection rates. A prospective study is needed for further optimization for clinical practice.
尽管荷兰耐万古霉素肠球菌（VRE）感染的患病率较低，但VRE仍是医院暴发的常见源头。我们调查了泊松隐马尔可夫模型（PHMM）能否在荷兰抗菌药物耐药性传染病监测信息系统（ISIS-AR）的常规数据中检测到医院内的VRE暴发。
我们进行了一项从2013年至2023年的回顾性数据关联研究，纳入了89家医院来自ISIS-AR的VRE分离株数据。基于医院层面每周的VRE计数，使用PHMM检测潜在的暴发。对于每周t，该模型提供观察到的计数由暴发引起的概率P。使用至少连续两周P(t)>0.5、P(t)>0.7和P(t)>0.9的阈值。将PHMM的结果与自愿报告给“医疗机构高度耐药微生物暴发预警与应对会议”的暴发情况进行比较。计算检测百分比，并描述已报告但未检测到的暴发以及检测到但未报告的暴发的VRE计数。
在85起报告的暴发中，对于阈值P(t)>0.5、P(t)>0.7和P(t)>0.9，该模型的检测率分别为87%、86%和81%。未检测到的暴发主要是小规模暴发。PHMM分别检测到66起、55起和44起未报告的潜在暴发，其中44%、35%和30%仅涉及一至两名VRE阳性患者。
总体而言，PHMM在常规监测数据中检测医院内VRE暴发显示出潜力，检测率较高。需要进行前瞻性研究以进一步优化用于临床实践。
