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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.
We performed a PubMed search using separate terms for WGS surveillance and/or machine-learning technologies for infection prevention through March 15, 2021.
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.
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.
Identifying routes of transmission among hospitalized patients during a healthcare-associated outbreak can be tedious, particularly among patients with complex hospital stays and multiple exposures. Data mining of the electronic health record (EHR) has the potential to rapidly identify common exposures among patients suspected of being part of an outbreak.
We retrospectively analyzed 9 hospital outbreaks that occurred during 2011–2016 and that had previously been characterized both according to transmission route and by molecular characterization of the bacterial isolates. We determined (1) the ability of data mining of the EHR to identify the correct route of transmission, (2) how early the correct route was identified during the timeline of the outbreak, and (3) how many cases in the outbreaks could have been prevented had the system been running in real time.
Correct routes were identified for all outbreaks at the second patient, except for one outbreak involving >1 transmission route that was detected at the eighth patient. Up to 40 or 34 infections (78% or 66% of possible preventable infections, respectively) could have been prevented if data mining had been implemented in real time, assuming the initiation of an effective intervention within 7 or 14 days of identification of the transmission route, respectively.
Data mining of the EHR was accurate for identifying routes of transmission among patients who were part of the outbreak. Prospective validation of this approach using routine whole-genome sequencing and data mining of the EHR for both outbreak detection and route attribution is ongoing.
Recovery of multidrug-resistant (MDR) Pseudomonas aeruginosa and Klebsiella pneumoniae from a cluster of patients in the medical intensive care unit (MICU) prompted an epidemiologic investigation for a common exposure.
Clinical and microbiologic data from MICU patients were retrospectively reviewed, MICU bronchoscopes underwent culturing and borescopy, and bronchoscope reprocessing procedures were reviewed. Bronchoscope and clinical MDR isolates epidemiologically linked to the cluster underwent molecular typing using pulsed-field gel electrophoresis (PFGE) followed by whole-genome sequencing.
Of the 33 case patients, 23 (70%) were exposed to a common bronchoscope (B1). Both MDR P. aeruginosa and K. pneumonia were recovered from the bronchoscope’s lumen, and borescopy revealed a luminal defect. Molecular testing demonstrated genetic relatedness among case patient and B1 isolates, providing strong evidence for horizontal bacterial transmission. MDR organism (MDRO) recovery in 19 patients was ultimately linked to B1 exposure, and 10 of 19 patients were classified as belonging to an MDRO pseudo-outbreak.
Surveillance of bronchoscope-derived clinical culture data was important for early detection of this outbreak, and whole-genome sequencing was important for the confirmation of findings. Visualization of bronchoscope lumens to confirm integrity should be a critical component of device reprocessing.
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