BACKGROUND
Most hospitals use traditional infection prevention (IP) methods for outbreak detection. We developed the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), which combines whole genome sequencing (WGS) surveillance and machine learning (ML) of the electronic health record (EHR) to identify undetected outbreaks and the responsible transmission routes, respectively.


METHODS
We performed WGS surveillance of healthcare-associated bacterial pathogens from November 2016 to November 2018. EHR ML was used to identify the transmission routes for WGS-detected outbreaks, which were investigated by an IP expert. Potential infections prevented were estimated and compared to traditional IP practice during the same period.


RESULTS
Of 3,165 isolates, there were 2,752 unique patient isolates in 99 clusters involving 297 (10.8%) patient isolates were identified by WGS; clusters ranged from 2-14 patients. At least one transmission route was detected for 65.7% of clusters. During the same time, traditional IP investigation prompted WGS for 15 suspected outbreaks involving 133 patients, for which transmission events were identified for 5 (3.8%). If EDS-HAT had been running in real-time, 25-63 transmissions could have been prevented. EDS-HAT was found to be cost-saving and more effective than traditional IP practice, with overall savings of $192,408 - $692,532.


CONCLUSION
EDS-HAT detected multiple outbreaks not identified using traditional IP methods, correctly identified the transmission routes for most outbreaks, and would save the hospital substantial costs. Traditional IP practice misidentified outbreaks for which transmission did not occur. WGS surveillance combined with EHR ML has the potential to save costs and enhance patient safety.

Whole Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection.

Background and Aim: The majority of animal-derived food safety studies have focused on foodborne zoonotic agents; however, members of the opportunistic Enterobacteriaceae (Ops) family are increasingly implicated in foodborne and public health crises due to their robust evolution of acquiring antimicrobial resistance and biofilms, consequently require thorough characterization, particularly in the Egyptian food sector. Therefore, this study aimed to determine the distribution and prevalence of Enterobacteriaceae family members in animal-derived foods, as well as their resistance to important antimicrobials and biofilm-forming potential. Materials and Methods: A total of 274 beef, rabbit meat, chicken meat, egg, butter, and milk samples were investigated for the presence of Enterobacteriaceae. All isolated strains were first recognized using traditional microbiological techniques. Following that, matrix-assisted laser desorption ionization-time of flight mass spectrometry was used to validate the Enterobacteriaceae’s identity. The isolated enterobacteria strains were tested on disk diffusion and crystal violet quantitative microtiter plates to determine their antibiotic resistance and capacity to form biofilms. Results: There have been thirty isolates of Enterobacteriaceae from seven different species and four genera. Out of the three food types, Pseudomonas aeruginosa had the highest prevalence rate (4.1%). With three species, Enterobacter genera had the second-highest prevalence (3.28%) across five different food categories. In four different food types, the Klebsiella genera had the second-highest distribution and third-highest incidence (2.55%). Almost all isolates, except three Proteus mirabilis, showed prominent levels of resistance, particularly to beta-lactam antibiotics. Except for two Enterobacter cloacae and three P. mirabilis isolates, all isolates were classified as multidrug-resistant (MDR) orextensively multidrug-resistant (XDR). The multiple antibiotic resistance index (MARI) of the majority of isolates dropped between 0.273 and 0.727. The highest MARI was conferred by Klebsiella pneumoniae, at 0.727. Overall, 83.33% of the isolates had strong biofilm capacity, while only 16.67% exhibited moderate capacity. Conclusion: The MDR, XDR, and strong biofilm indicators confirmed in 83.33% of the currently tested Enterobacteriaceae from animal-derived foods suggest that, if not addressed, there may be rising risks to Egypt’s economy and public health.

/pdf/prevalence-antibiotic-resistance-patterns-and-biofilm-2ftqjsn8.pdf

Prevalence, antibiotic resistance patterns, and biofilm formation ability of Enterobacterales recovered from food of animal origin in Egypt

Background and study aims  Duodenoscopes that are contaminated due to inadequate reprocessing are well-documented. However, studies have demonstrated poor reprocessing of other kinds of endoscopes as well, including echoendoscopes, gastroscopes, and colonoscopes. We estimated the contamination rate beyond the elevator of gastrointestinal endoscopes based on available data. Methods  We searched PubMed and Embase from January 1, 2010 to October 10, 2020, for studies investigating contamination rates of reprocessed gastrointestinal endoscopes. A random-effects model was used to calculate the contamination rate of patient-ready gastrointestinal endoscopes. Subgroup analyses were conducted to investigate differences among endoscope types, countries, and colony-forming unit (CFU) thresholds. Results  Twenty studies fulfilled the inclusion criteria, including 1,059 positive cultures from 7,903 samples. The total contamination rate was 19.98 % ± 0.024 (95 % confidence interval [Cl]: 15.29 %–24.68 %; I 2  = 98.6 %). The contamination rates of colonoscope and gastroscope channels were 31.95 % ± 0.084 and 28.22 % ± 0.076, respectively. Duodenoscope channels showed a contamination rate of 14.41 % ± 0.029. The contamination rates among studies conducted in North America and Europe were 6.01 % ± 0.011 and 18.16% ± 0.053 %, respectively. The contamination rate among studies using a CFU threshold > 20 showed contamination of 30.36 % ± 0.094, whereas studies using a CFU threshold < 20 showed a contamination rate of 11 % ± 0.026. Conclusions  On average, 19.98 % of reprocessed gastrointestinal endoscopes may be contaminated when used in patients and varies between different geographies. These findings highlight that the elevator mechanism is not the only obstacle when reprocessing reusable endoscopes; therefore, guidelines should recommend more surveillance of the endoscope channels as well.

http://www.thieme-connect.de/products/ejournals/pdf/10.1055/a-1795-8883.pdf

Gastrointestinal endoscope contamination rates – elevators are not only to blame: a systematic review and meta-analysis

We describe 2 cases of extensively drug-resistant Pseudomonas aeruginosa infection caused by a strain of public health concern, as it was recently associated with a nationwide outbreak of contaminated artificial tears. Both cases were detected through database review of genomes in the Enhanced Detection System for Hospital-Associated Transmission (EDS-HAT), a routine genome sequencing-based surveillance program. We generated a high-quality reference genome for the outbreak strain from an isolate from our center and examined the mobile elements encoding blaVIM-80 and bla-GES-9 carbapenemases. We used publicly available Pseudomonas aeruginosa genomes to explore the genetic relatedness and antimicrobial resistance genes of the outbreak strain.

Two Artificial Tears Outbreak-Associated Cases of Extensively Drug-Resistant Pseudomonas aeruginosa Detected Through Whole Genome Sequencing-Based Surveillance.

The National Healthcare Safety Network (NHSN) definitions are critical for standardizing healthcare-associated infection surveillance in US healthcare facilities. However, their use in accurately detecting healthcare-associated transmission (HAT) has not been measured. Using whole-genome sequencing surveillance data, we show that the NHSN has a sensitivity of 44.4% in detecting HAT.

/pdf/sensitivity-of-national-healthcare-safety-network-17ehgbs6.pdf

Sensitivity of National Healthcare Safety Network definitions to capture healthcare-associated transmission identified by whole-genome sequencing surveillance.

Background Traditional methods of outbreak investigations utilize reactive whole genome sequencing (WGS) to confirm or refute the outbreak. We have implemented WGS surveillance and a machine learning (ML) algorithm for the electronic health record (EHR) to retrospectively detect previously unidentified outbreaks and to determine the responsible transmission routes. Methods We performed WGS surveillance to identify and characterize clusters of genetically-related Pseudomonas aeruginosa infections during a 24-month period. ML of the EHR was used to identify potential transmission routes. A manual review of the EHR was performed by an infection preventionist to determine the most likely route and results were compared to the ML algorithm. Results We identified a cluster of 6 genetically related P. aeruginosa cases that occurred during a 7-month period. The ML algorithm identified gastroscopy as a potential transmission route for 4 of the 6 patients. Manual EHR review confirmed gastroscopy as the most likely route for 5 patients. This transmission route was confirmed by identification of a genetically-related P. aeruginosa incidentally cultured from a gastroscope used on 4of the 5 patients. Three infections, 2 of which were blood stream infections, could have been prevented if the ML algorithm had been running in real-time. Conclusions WGS surveillance combined with a ML algorithm of the EHR identified a previously undetected outbreak of gastroscope-associated P. aeruginosa infections. These results underscore the value of WGS surveillance and ML of the EHR for enhancing outbreak detection in hospitals and preventing serious infections.

Outbreak of Pseudomonas aeruginosa Infections from a Contaminated Gastroscope Detected by Whole Genome Sequencing Surveillance.

BACKGROUND Most hospitals use traditional infection prevention (IP) methods for outbreak detection. We developed the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), which combines whole genome sequencing (WGS) surveillance and machine learning (ML) of the electronic health record (EHR) to identify undetected outbreaks and the responsible transmission routes, respectively. METHODS We performed WGS surveillance of healthcare-associated bacterial pathogens from November 2016 to November 2018. EHR ML was used to identify the transmission routes for WGS-detected outbreaks, which were investigated by an IP expert. Potential infections prevented were estimated and compared to traditional IP practice during the same period. RESULTS Of 3,165 isolates, there were 2,752 unique patient isolates in 99 clusters involving 297 (10.8%) patient isolates were identified by WGS; clusters ranged from 2-14 patients. At least one transmission route was detected for 65.7% of clusters. During the same time, traditional IP investigation prompted WGS for 15 suspected outbreaks involving 133 patients, for which transmission events were identified for 5 (3.8%). If EDS-HAT had been running in real-time, 25-63 transmissions could have been prevented. EDS-HAT was found to be cost-saving and more effective than traditional IP practice, with overall savings of $192,408 - $692,532. CONCLUSION EDS-HAT detected multiple outbreaks not identified using traditional IP methods, correctly identified the transmission routes for most outbreaks, and would save the hospital substantial costs. Traditional IP practice misidentified outbreaks for which transmission did not occur. WGS surveillance combined with EHR ML has the potential to save costs and enhance patient safety.

https://academic.oup.com/cid/advance-article-pdf/doi/10.1093/cid/ciab946/41355455/ciab946.pdf

Healthcare-associated infections (HAIs) cause mortality, morbidity, and waste of healthcare resources. HAIs are also an important driver of antimicrobial resistance, which is increasing around the world. Beginning in November 2016, we instituted an initiative to detect outbreaks of HAI using prospective whole genome sequencing-based surveillance of bacterial pathogens collected from hospitalized patients. Here we describe the biodiversity of bacteria sampled from hospitalized patients at a single center, as revealed through systematic analysis of their genomes. We sequenced the genomes of 3,004 bacterial isolates from hospitalized patients collected over a 25-month period. We identified bacteria belonging to 97 distinct species, which were distributed among 14 species groups. Within these groups, isolates could be distinguished from one another by both average nucleotide identity (ANI) and principal component analysis of accessory genes (PCA-A). Genetic distances between isolates and rates of evolution varied between different species, which has implications for the selection of distance cut-offs for outbreak analysis. Antimicrobial resistance genes and the sharing of mobile genetic elements between different species were frequently observed. Overall, this study describes the population structure of pathogens circulating in a single healthcare setting, and shows how investigating microbial population dynamics can inform genomic epidemiology studies.

https://www.biorxiv.org/content/biorxiv/early/2021/10/28/2021.10.27.466213.full.pdf

Genomic diversity of hospital-acquired infections revealed through prospective whole genome sequencing-based surveillance

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Outbreak of Pseudomonas aeruginosa Infections from a Contaminated Gastroscope Detected by Whole Genome Sequencing Surveillance.

Whole Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection.

Genomic diversity of hospital-acquired infections revealed through prospective whole genome sequencing-based surveillance