Lean back and wait for the alarm? Testing an automated alarm system for nosocomial outbreaks to provide support for infection control professionals

Abstract

Introduction: Outbreaks of communicable diseases in hospitals need to be quickly detected in order to enable immediate control. The increasing digitalization of hospital data processing offers potential solutions for automated outbreak detection systems (AODS). Our goal was to assess a newly developed AODS.

Methods: Our AODS was based on the diagnostic results of routine clinical microbiological examinations. The system prospectively counted detections per bacterial pathogen over time for the years 2016 and 2017. The baseline data covers data from 2013-2015. The comparative analysis was based on six different mathematical algorithms (normal/Poisson and score prediction intervals, the early aberration reporting system, negative binomial CUSUMs, and the Farrington algorithm). The clusters automatically detected were then compared with the results of our manual outbreak detection system.

Results: During the analysis period, 14 different hospital outbreaks were detected as a result of conventional manual outbreak detection. Based on the pathogens' overall incidence, outbreaks were divided into two categories: outbreaks with rarely detected pathogens (sporadic) and outbreaks with often detected pathogens (endemic). For outbreaks with sporadic pathogens, the detection rate of our AODS ranged from 83% to 100%. Every algorithm detected 6 of 7 outbreaks with a sporadic pathogen. The AODS identified outbreaks with an endemic pathogen were at a detection rate of 33% to 100%. For endemic pathogens, the results varied based on the epidemiological characteristics of each outbreak and pathogen.

Conclusion: AODS for hospitals based on routine microbiological data is feasible and can provide relevant benefits for infection control teams. It offers in-time automated notification of suspected pathogen clusters especially for sporadically occurring pathogens. However, outbreaks of endemically detected pathogens need further individual pathogen-specific and setting-specific adjustments.

Fig 1. Classification of outbreaks into two types.

Datasets with endemically detected pathogens and datasets with sporadic pathogens. In the endemic dataset at least one pathogen occurred more than 30% of the time. In the sporadic dataset at least one pathogen occurred 30% or less of the time.

Fig 2. Two examples of outbreaks detected manually vs. outbreaks detected by AODS.

Left: Outbreak in an endemic dataset (outbreak 2, vancomycin-resistant E. faecium). Right: Outbreak in a sporadic dataset (outbreak I, Klebisella spp., MDR). Depicted is the course of pathogen detection on the ward during a year when an outbreak was manually detected. The manually detected outbreak is in the center and is indicated by a light blue box. Each bar represents the number of pathogens detected per time interval (14 days). If a bar is colored, an algorithm detected an aberration. Shown are the results for all six algorithms (top down in different colors): normal prediction interval, Poisson prediction interval, score prediction interval, early aberration report system, negative binomial CUSUMs, and the Farrington algorithm.