Investigation of possible clonal transmission of carbapenemase-producing Klebsiella pneumoniae complex member isolates in Denmark using core genome MLST and National Patient Registry Data

Hammerum, Anette M.; Lauridsen, Caroline A S; Blem, Sanne L; Roer, Louise; Hansen, Frank; Henius, Anna Emilie; Holzknecht, Barbara Juliane; Søes, Lillian Marie; Andersen, Leif Percival; Røder, Bent; Justesen, Ulrik Stenz; Østergaard, Claus; Søndergaard, Turid Snekloth; Dzajic, Esad; Wang, Mikala; Fulgsang-Damgaard, David; Møller, Karina Lauenborg; Porsbo, Lone Jannok; Hasman, Henrik

doi:10.1016/j.ijantimicag.2020.105931

Cited by 8 publications

(3 citation statements)

References 39 publications

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“…Overall, 35 studies (83.3%) restricted attempts to identify transmission routes to the same hospital unit during a defined period. 9,[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][29][30][31][32][33][34][35][36][37][38][40][41][42][44][45][46][47] Only 7 studies (16.7%) examined other possible routes such as medical procedures or healthcare workers. [3][4][5]10,28,39,43 Several studies were notable for uncovering otherwise unidentified transmissions, which is the main goal of WGS surveillance.…”

Section: Resultsmentioning

confidence: 99%

Whole-genome sequencing surveillance and machine learning for healthcare outbreak detection and investigation: A systematic review and summary

Sundermann

Chen

Miller

et al. 2022

ASHE

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Background: Whole-genome sequencing (WGS) has traditionally been used in infection prevention to confirm or refute the presence of an outbreak after it has occurred. Due to decreasing costs of WGS, an increasing number of institutions have been utilizing WGS-based surveillance. Additionally, machine learning or statistical modeling to supplement infection prevention practice have also been used. We systematically reviewed the use of WGS surveillance and machine learning to detect and investigate outbreaks in healthcare settings. Methods: We performed a PubMed search using separate terms for WGS surveillance and/or machine-learning technologies for infection prevention through March 15, 2021. Results: Of 767 studies returned using the WGS search terms, 42 articles were included for review. Only 2 studies (4.8%) were performed in real time, and 39 (92.9%) studied only 1 pathogen. Nearly all studies (n = 41, 97.6%) found genetic relatedness between some isolates collected. Across all studies, 525 outbreaks were detected among 2,837 related isolates (average, 5.4 isolates per outbreak). Also, 35 studies (83.3%) only utilized geotemporal clustering to identify outbreak transmission routes. Of 21 studies identified using the machine-learning search terms, 4 were included for review. In each study, machine learning aided outbreak investigations by complementing methods to gather epidemiologic data and automating identification of transmission pathways. Conclusions: WGS surveillance is an emerging method that can enhance outbreak detection. Machine learning has the potential to identify novel routes of pathogen transmission. Broader incorporation of WGS surveillance into infection prevention practice has the potential to transform the detection and control of healthcare outbreaks.

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Section: Resultsmentioning

confidence: 99%

Whole-genome sequencing surveillance and machine learning for healthcare outbreak detection and investigation: A systematic review and summary

Sundermann

Chen

Miller

et al. 2022

ASHE

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“…Here, we aim to study the epidemiology and genomic characteristics of CRKP in Henan by making use of whole-genome sequencing (WGS), multilocus sequence typing (MLST), and core genome multilocus sequence typing (cgMLST) ( 10 , 11 ). With cgMLST, heterogeneous isolates of the same sequence type (ST) can be further classified, and linkage in clonal transmission can be inferred, as demonstrated previously ( 12 ). With whole-genome sequence data, it is also possible to identify and type outer membrane polysaccharide capsule antigen (K) and lipopolysaccharide (LPS; O) loci and thus predict clinically relevant antigen types ( 13 ).…”

Section: Introductionmentioning

confidence: 93%

Genomic Epidemiology and Characterization of Carbapenem-Resistant Klebsiella pneumoniae in ICU Inpatients in Henan Province, China: a Multicenter Cross-Sectional Study

Wang

Jin

et al. 2023

Microbiol Spectr

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“…In determining possible commonalities, patients may be assumed to have short duration (<90 days) of transmissibility with bacterial pathogens after the initial clinical infection. [1][2][3] However, some studies suggest that this assumption may not always hold true, potentially leading to the exclusion of patients as sources of transmission during outbreak investigations. [4][5][6][7] To accurately determine the length of infectiousness and transmission for bacterial pathogens, an ideal study would use serial whole genome sequencing (WGS) to identify genetically related isolates over a prospective period from individual patients.…”

Section: Introductionmentioning

confidence: 99%

Prolonged bacterial carriage and hospital transmission detected by whole genome sequencing surveillance

Sundermann,

Griffith,

Srinivasa

et al. 2024

ASHE

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Investigation of possible clonal transmission of carbapenemase-producing Klebsiella pneumoniae complex member isolates in Denmark using core genome MLST and National Patient Registry Data

Cited by 8 publications

References 39 publications

Whole-genome sequencing surveillance and machine learning for healthcare outbreak detection and investigation: A systematic review and summary

Whole-genome sequencing surveillance and machine learning for healthcare outbreak detection and investigation: A systematic review and summary

Genomic Epidemiology and Characterization of Carbapenem-Resistant Klebsiella pneumoniae in ICU Inpatients in Henan Province, China: a Multicenter Cross-Sectional Study

Prolonged bacterial carriage and hospital transmission detected by whole genome sequencing surveillance

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