WGS-Enabled Surveillance Improves Detection Of Transmission Events Within A Large Tertiary Care Hospital Trust In London
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Abstract
Infections caused by carbapenem-producing Enterobacterales (CPEs) are a persistent and growing threat in healthcare settings. Yet, current infection prevention and control (IPC) surveillance methods, which largely rely on the spatial and temporal proximity of patients, often misattribute or miss infection transmission events. Here, we develop and retrospectively evaluate an integrated methodology that combines analyses of ward-level patient movement data and whole-genome sequencing (WGS) data analyses, which provide measures of bacterial and plasmid similarity. Specifically, we evaluate this methodology across two datasets: a CPE outbreak of diverse carbapenem types (103 genomes, January 2021–March 2021) and an Imipenem-Hydrolysing β-lactamase-positive CPE outbreak (82 genomes, June 2016–October 2019), using standard clinical criteria and conservative genomic thresholds to quantify how often current IPC surveillance methods correctly identify genomically confirmed transmission events. Findings show that, across 3,423 patient contact–genome pairs, current IPC surveillance methods detected only 20.5% of genomically confirmed transmission events whilst maintaining 98.5% specificity, with missed events arising from temporal, spatial, and cross-species, mechanistic blindspots. In contrast, WGS-enabled IPC surveillance methods provided a 25–47-day earlier detection window and, in a linked economic evaluation, delivered annualised savings of up to £3.6 million, as well as a return on investment exceeding 2-fold in 7 of 8 cost scenarios. By operationalising high-throughput WGS data analysis with clinically relevant patient movement data, we evidence that it may be possible to disrupt and thereby mitigate the effects of AMR-driven CPE outbreaks, supporting investigations into the adoption of WGS-enabled IPC surveillance as a standard-of-care tool.
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This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/20018143.
This study evaluates whether integrating whole-genome sequencing with patient movement data can improve the detection and characterization of outbreaks caused by carbapenem-producing Enterobacterales in hospital settings, compared to standard infection prevention and control surveillance. Using retrospective data from two outbreak datasets, the authors compare WGS-enabled surveillance with standard IPC methods. The results show that standard approaches miss a substantial proportion of transmission events, while WGS integration significantly improves sensitivity and allows for earlier identification of transmission events. The integrated approach also uncovers transmission pathways that …
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/20018143.
This study evaluates whether integrating whole-genome sequencing with patient movement data can improve the detection and characterization of outbreaks caused by carbapenem-producing Enterobacterales in hospital settings, compared to standard infection prevention and control surveillance. Using retrospective data from two outbreak datasets, the authors compare WGS-enabled surveillance with standard IPC methods. The results show that standard approaches miss a substantial proportion of transmission events, while WGS integration significantly improves sensitivity and allows for earlier identification of transmission events. The integrated approach also uncovers transmission pathways that would not be detected through conventional methods. Additionally, the study suggests that implementing this strategy could lead to considerable economic benefits, supporting its potential value in healthcare settings.
This study has several notable strengths. First, it presents an innovative and integrative approach that combines genomic and epidemiological data, offering a more comprehensive understanding of transmission dynamics. Second, it provides clear quantitative comparisons between standard IPC methods and WGS-enabled surveillance, strengthening the validity of its conclusions. Third, the inclusion of an economic analysis enhances the practical relevance of the study and supports its potential implications for healthcare policy and resource allocation.
Despite these strengths, there are important limitations that should be considered. The study is based on retrospective data from a single healthcare system, which may limit the generalizability of the findings to other settings with different infrastructures, patient populations and infection control practices. In addition, the feasibility of implementing WGS in real time is not fully addressed, key aspects such as availability, sequencing turnaround times, costs, and integration into clinical workflows remain uncertain, and in practice, delays in data processing could reduce the impact on infection control decisions. These factors are critical for translating the proposed approach into standard practice.
Another important limitation is that the model infers transmission events based on genomic similarity and patient overlap, but these assumptions may not fully capture more complex transmission pathways such as those involving environmental reservoirs or indirect transmission. Furthermore, the limited incorporation of clinical context may restrict the interpretation of transmission relevance and outbreak dynamics.
In conclusion, this study provides valuable evidence supporting the integration of genomic data into hospital surveillance systems to enhance outbreak detection and inform infection control strategies. However, given its retrospective design, the model likely benefits from more complete datasets than would be available in real time, which may lead to an overestimation of its performance and practical impact in real-world healthcare settings.
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El/la autor/a declara que no tiene intereses en conflicto.
Uso de Inteligencia Artificial (IA)
El/la autor/a declara que no utilizó IA generativa para concebir nuevas ideas para su revisión.
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This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/19862973.
Summary:
This study evaluates whether adding whole-genome sequencing (WGS) to routine infection prevention and control (IPC) surveillance improves the detection of carbapenem-producing Enterobacterales (CPE) transmission in a large hospital system in London. The authors combine patient movement data with genomic data from bacteria and plasmids across two outbreaks with different characteristics. They found that standard IPC methods detected only about 20% of transmission events identified by genomic data. WGS-based surveillance could detect transmission 25 to 47 days earlier and may reduce costs, with estimated savings of up to £3.6 million annually.
Strengths:
1. Captures transmission …
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/19862973.
Summary:
This study evaluates whether adding whole-genome sequencing (WGS) to routine infection prevention and control (IPC) surveillance improves the detection of carbapenem-producing Enterobacterales (CPE) transmission in a large hospital system in London. The authors combine patient movement data with genomic data from bacteria and plasmids across two outbreaks with different characteristics. They found that standard IPC methods detected only about 20% of transmission events identified by genomic data. WGS-based surveillance could detect transmission 25 to 47 days earlier and may reduce costs, with estimated savings of up to £3.6 million annually.
Strengths:
1. Captures transmission pathways that standard genomic methods miss
A major strength is the inclusion of plasmid analysis alongside standard genome comparisons. This enables detection of cross-species transmission driven by shared resistance plasmids, which would likely be missed using genome-based methods alone. This adds important nuance to how transmission is conceptualized and highlights a potential blind spot in standard genomic surveillance.
2. Use of two distinct outbreak datasets improves generalizability
The study includes two outbreaks that differ in duration, species composition, and resistance mechanisms. This allows the authors to show that the performance of IPC surveillance varies depending on the outbreak context, which is highly relevant for real-world public health settings.
Major Concerns:
1. Early detection estimates do not account for real-world sequencing delays
The reported 25 to 47 day earlier detection assumes near real-time availability of genomic data. In practice, sequencing, processing, and interpretation often take 7 to 14 days or longer in routine healthcare settings. This delay is not trivial, since transmission chains for organisms like CPE can expand rapidly within days through patient transfers and shared hospital environments. A delay of even one week could allow multiple additional exposures, reducing the practical benefit of earlier detection and limiting the ability to intervene in time. The authors could strengthen their conclusions by conducting a sensitivity analysis that models realistic turnaround times and evaluates how much of the detection advantage remains under these conditions.
2. Plasmid analysis approach may introduce uncertainty in key findings
The study uses only the largest contig from each plasmid assembly, which may not fully capture complex plasmid structures. This could affect estimates of cross-species transmission, which is one of the study's main contributions. The authors could assess how this choice influences their results, for example, by comparing with long-read sequencing data on a subset of samples or by discussing the potential direction and magnitude of bias more explicitly.
3. Cost-effectiveness analysis may not reflect real implementation costs
The economic analysis assumes access to existing sequencing infrastructure and does not include costs such as staffing, training, and system setup. This may overestimate the return on investment, especially for hospitals without current sequencing capacity. Including a scenario that accounts for implementation costs in a setting without existing infrastructure would make the findings more applicable to a wider range of healthcare systems.
Overall Recommendation:
This study makes an important contribution by demonstrating how WGS, particularly plasmid analysis, can improve the detection of transmission events in hospital settings. However, key conclusions about early detection and cost-effectiveness would be stronger if they accounted for real-world implementation constraints. Addressing these issues would improve the study's relevance for public health decision-making.
Competing interests
The authors declare that they have no competing interests.
Use of Artificial Intelligence (AI)
The authors declare that they did not use generative AI to come up with new ideas for their review.
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