Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Nov 5, 2019
Date Accepted: Aug 2, 2020
Date Submitted to PubMed: Sep 23, 2020
Automated Cluster Detection of Healthcare-Associated Infection Based on the Multi-Source Surveillance of Process Data in the area network: Algorithm Development and Validation
ABSTRACT
Background:
Cluster detection of healthcare-associated infection (HAI) is crucial for identifying HAI outbreaks in early stages.
Objective:
To verify whether multi-source surveillance based on process data in the area network can be effective for the detection of HAI clusters.
Methods:
We retrospectively analyzed HAI incidence and three indicators of process data relative to infection — antibiotic utilization rate in combination (AUR), inspection rate of bacterial specimen (IRS), and positive rate of bacterial specimen (PRS) — from four independent high-risk units in a tertiary hospital in China. We utilized the Shewhart warning model to detect the peaks of time-series data. Subsequently, we designed five surveillance strategies based on process data for HAI clusters detection: (1) AUR only, (2) IRS only, (3) PRS only, (4) AUR+IRS+PRS in parallel, and (5) AUR+IRS+PRS in series. We used the receiver operating characteristic (ROC) and Youden’s index to evaluate the warning performance of these surveillance strategies for the detection of HAI clusters.
Results:
The ROCs of all five surveillance strategies were located above the standard line, and the area under the curve the of ROC was larger in the parallel strategy than in the series strategy and the single-indicator strategies. The optimal Youden’s indexes were 0.48 (95% CI: 0.29–0.67) at a threshold of 1.5 in the AUR-only strategy, 0.49 (95% CI: 0.45–0.53) at a threshold of 0.5 in the IRS only strategy, 0.50 (95% CI: 0.28–0.71) at a threshold of 1.1 in the PRS only strategy, 0.63 (95% CI: 0.49–0.77) at a threshold of 2.6 in the parallel strategy, and 0.32 (95% CI: 0.00–0.65) at a threshold of 0.0 in the series strategy. The warning performance of the parallel strategy was greater than that of the single-indicator strategies when the threshold exceeded 1.5.
Conclusions:
The multi-source surveillance of process data in the area network was an effective method for the early detection of HAI clusters. The combination of multi-source data and the threshold of the warning model were two important factors that influenced the performance of the model.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.