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. 2023 Dec;44(12):1948-1952.
doi: 10.1017/ice.2023.113. Epub 2023 Jun 23.

Clostridioides difficile outbreak detection: Evaluation by ribotyping and whole-genome sequencing of a surveillance algorithm based on ward-specific cutoffs

Jon E Edman-Wallér  1   2 , Michael Toepfer  3 , Johan Karp  1   4 , Kristina Rizzardi  5 , Gunnar Jacobsson  1   4 , Maria Werner  6
Affiliations

Clostridioides difficile outbreak detection: Evaluation by ribotyping and whole-genome sequencing of a surveillance algorithm based on ward-specific cutoffs

Jon E Edman-Wallér et al. Infect Control Hosp Epidemiol. 2023 Dec.

Abstract

Objective: We evaluated the performance of an early-warning algorithm, based on ward-specific incidence cutoffs for detecting Clostridioides difficile transmission in hospitals. We also sought to determine the frequency of intrahospital Clostridioides difficile transmission in our setting.

Design: Diagnostic performance of the algorithm was tested with confirmed transmission events as the comparison criterion. Transmission events were identified by a combination of high-molecular-weight typing, ward history, ribotyping, and whole-genome sequencing (WGS).

Setting: The study was conducted in 2 major and 2 minor secondary-care hospitals with adjacent catchment areas in western Sweden, comprising a total population of ∼480,000 and ∼1,000 hospital beds.

Patients: All patients with a positive PCR test for Clostridioides difficile toxin B during 2020 and 2021.

Methods: We conducted culturing and high-molecular-weight typing of all positive clinical samples. Ward history was determined for each patient to find possible epidemiological links between patients with the same type. Transmission events were determined by PCR ribotyping followed by WGS.

Results: We identified 4 clusters comprising a total of 10 patients (1.5%) among 673 positive samples that were able to be cultured and then typed by high-molecular-weight typing. The early-warning algorithm performed no better than chance; patient diagnoses were made at wards other than those where the transmission events likely occurred.

Conclusions: In surveillance of potential transmission, it is insufficient to consider only the ward where diagnosis is made, especially in settings with high strain diversity. Transmission within wards occurs sporadically in our setting.

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Conflict of interest statement

All authors report no conflicts of interest relevant to this article.

Figures

Figure 1.
Figure 1.
Flow chart depicting the steps for identifying transmission clusters.
Figure 2.
Figure 2.
Performance of the 2 evaluated algorithms for detecting transmission events. Note. Grey areas are confidence intervals; black dots are point estimates.
See this image and copyright information in PMC

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