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Outbreaks you didn’t know that you had - the joy of whole genome sequencing
In this episode recorded at the 2023 SHEA Spring Meeting in Seattle, Martin talks to Associate Professor Alex Sundermann about his work on sequencing all isolates from local healthcare settings and the discoveries that this enables. Healthcare infections due to previous admissions, endoscopes, even pseudo-outbreaks are all laid bare by this approach. Some recent papers from the group are listed below:
1. Sundermann AJ, et al. Two artificial tears outbreak-associated cases of XDR Pseudomonas aeruginosa detected through whole genome sequencing-based surveillance. medRxiv. 2023.
https://doi:10.1101/2023.04.11.23288417
2. Sundermann AJ, et al. Sensitivity of National Healthcare Safety Network definitions to capture healthcare-associated transmission identified by whole-genome sequencing surveillance. Infect Control Hosp Epidemiol. 2023:1-3. https://doi:10.1017/ice.2023.52
3. Branch-Elliman W, et al. The future of automated infection detection: Innovation to transform practice (Part III/III). Antimicrob Steward Healthc Epidemiol. 2023;3(1):e26. https://doi:10.1017/ash.2022.333
4. Sundermann AJ, et al. Whole-genome sequencing surveillance and machine learning for healthcare outbreak detection and investigation: A systematic review and summary. Antimicrob Steward Healthc Epidemiol. 2022;2(1):e91. https://doi:10.1017/ash.2021.241
5. Sundermann AJ, et al. Whole-Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection. Clin Infect Dis. 2022;75(3):476-482. https://doi:10.1093/cid/ciab946
6. Sundermann AJ, et al. Outbreak of Pseudomonas aeruginosa Infections from a Contaminated Gastroscope Detected by Whole Genome Sequencing Surveillance. Clin Infect Dis. 2021;73(3):e638-e642. https://doi:10.1093/cid/ciaa1887
7. Kumar P, et al. Method for Economic Evaluation of Bacterial Whole Genome Sequencing Surveillance Compared to Standard of Care in Detecting Hospital Outbreaks. Clin Infect Dis. 2021;73(1):e9-e18. https://doi:10.1093/cid/ciaa512
8. Sundermann AJ, et al. Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks. Infect Control Hosp Epidemiol. 2019:1-6. https://doi:10.1017/ice.2018.343
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By Brett Mitchell
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In this episode recorded at the 2023 SHEA Spring Meeting in Seattle, Martin talks to Associate Professor Alex Sundermann about his work on sequencing all isolates from local healthcare settings and the discoveries that this enables. Healthcare infections due to previous admissions, endoscopes, even pseudo-outbreaks are all laid bare by this approach. Some recent papers from the group are listed below:
1. Sundermann AJ, et al. Two artificial tears outbreak-associated cases of XDR Pseudomonas aeruginosa detected through whole genome sequencing-based surveillance. medRxiv. 2023.
https://doi:10.1101/2023.04.11.23288417
2. Sundermann AJ, et al. Sensitivity of National Healthcare Safety Network definitions to capture healthcare-associated transmission identified by whole-genome sequencing surveillance. Infect Control Hosp Epidemiol. 2023:1-3. https://doi:10.1017/ice.2023.52
3. Branch-Elliman W, et al. The future of automated infection detection: Innovation to transform practice (Part III/III). Antimicrob Steward Healthc Epidemiol. 2023;3(1):e26. https://doi:10.1017/ash.2022.333
4. Sundermann AJ, et al. Whole-genome sequencing surveillance and machine learning for healthcare outbreak detection and investigation: A systematic review and summary. Antimicrob Steward Healthc Epidemiol. 2022;2(1):e91. https://doi:10.1017/ash.2021.241
5. Sundermann AJ, et al. Whole-Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection. Clin Infect Dis. 2022;75(3):476-482. https://doi:10.1093/cid/ciab946
6. Sundermann AJ, et al. Outbreak of Pseudomonas aeruginosa Infections from a Contaminated Gastroscope Detected by Whole Genome Sequencing Surveillance. Clin Infect Dis. 2021;73(3):e638-e642. https://doi:10.1093/cid/ciaa1887
7. Kumar P, et al. Method for Economic Evaluation of Bacterial Whole Genome Sequencing Surveillance Compared to Standard of Care in Detecting Hospital Outbreaks. Clin Infect Dis. 2021;73(1):e9-e18. https://doi:10.1093/cid/ciaa512
8. Sundermann AJ, et al. Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks. Infect Control Hosp Epidemiol. 2019:1-6. https://doi:10.1017/ice.2018.343
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