Preprint
Regression-based modeling of pairwise genomic linkage data identifies risk factors for healthcare-associated infection transmission: Application to carbapenem-resistant Klebsiella pneumoniae transmission in a long-term care facility
medRxiv
Cold Spring Harbor Laboratory Press, 1.1
05/06/2025
DOI: 10.1101/2025.05.06.25327000
PMID: 40385455
Abstract
Pathogen whole genome sequencing (WGS) has significant potential for improving healthcare-associated infection (HAI) outcomes. However, methods for integrating WGS with epidemiologic data to quantify risks for pathogen spread remain underdeveloped.
To identify analytic strategies for conducting WGS-based HAI surveillance in high-burden settings, we modeled patient- and facility-level transmission risks of carbapenem-resistant Klebsiella pneumoniae (CRKP) in a long-term acute care hospital (LTACH). Using rectal surveillance data collected over one year, we fit three pairwise regression models with three different metrics of genomic relatedness for pairs of case isolates, a proxy for transmission linkage: 1) single-nucleotide variant genomic distance, 2) closest genomic donor, 3) common genomic cluster. To assess the performance of these approaches under real-world conditions defined by passive surveillance, we conducted a sensitivity study including only cases detected by admission surveillance or clinical symptoms.
Genomic relatedness between pairs of isolates was associated with room sharing in two of the three models and overlapping stays on a high-acuity unit in all models, echoing previous findings from LTACH settings. In our sensitivity analysis, qualitative findings were robust to the exclusion of cases that would not have been identified with a passive surveillance strategy, however uncertainty in all estimates also increased markedly.
Taken together, our results demonstrate that pairwise regression models combining relevant genomic and epidemiologic data are useful tools for identifying HAI transmission risks.
Whole genome sequencing of healthcare associated infections (HAI) is becoming more common and new methods are necessary to integrate these data with epidemiologic risk factors to quantify transmission drivers.
We demonstrate how pairwise regression models, in which the outcome of a regression model represents genomic similarity between a pair of isolates, can identify known transmission risk factors of carbapenem-resistant Klebsiella pneumoniae in a long-term acute care facility.
Pairwise regression models could be used with rich epidemiologic data in other settings to identify risk factors of endemic HAI transmission.
Details
- Title: Subtitle
- Regression-based modeling of pairwise genomic linkage data identifies risk factors for healthcare-associated infection transmission: Application to carbapenem-resistant Klebsiella pneumoniae transmission in a long-term care facility
- Creators
- Hannah Steinberg - University of MichiganTimileyin Adediran - University of MichiganMary K. Hayden - Rush University Medical CenterEvan Snitkin - University of MichiganJon Zelner - University of Michigan
- Resource Type
- Preprint
- Publication Details
- medRxiv
- Edition
- 1.1
- DOI
- 10.1101/2025.05.06.25327000
- PMID
- 40385455
- Publisher
- Cold Spring Harbor Laboratory Press
- Number of pages
- 25
- Language
- English
- Date posted
- 05/06/2025
- Academic Unit
- Epidemiology
- Record Identifier
- 9985134748102771
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