Extending outbreak investigation with machine learning and graph theory: Benefits of new tools with application to a nosocomial outbreak of a multidrug-resistant organism.

Atkinson, Andrew; Ellenberger, Benjamin; Piezzi, Vanja; Kaspar, Tanja; Salazar-Vizcaya, Luisa; Endrich, Olga; Leichtle, Alexander B; Marschall, Jonas (2023). Extending outbreak investigation with machine learning and graph theory: Benefits of new tools with application to a nosocomial outbreak of a multidrug-resistant organism. Infection control and hospital epidemiology, 44(2), pp. 246-252. Cambridge University Press 10.1017/ice.2022.66

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OBJECTIVE

From January 1, 2018, until July 31, 2020, our hospital network experienced an outbreak of vancomycin-resistant enterococci (VRE). The goal of our study was to improve existing processes by applying machine-learning and graph-theoretical methods to a nosocomial outbreak investigation.

METHODS

We assembled medical records generated during the first 2 years of the outbreak period (January 2018 through December 2019). We identified risk factors for VRE colonization using standard statistical methods, and we extended these with a decision-tree machine-learning approach. We then elicited possible transmission pathways by detecting commonalities between VRE cases using a graph theoretical network analysis approach.

RESULTS

We compared 560 VRE patients to 86,684 controls. Logistic models revealed predictors of VRE colonization as age (aOR, 1.4 (per 10 years), with 95% confidence interval [CI], 1.3-1.5; P < .001), ICU admission during stay (aOR, 1.5; 95% CI, 1.2-1.9; P < .001), Charlson comorbidity score (aOR, 1.1; 95% CI, 1.1-1.2; P < .001), the number of different prescribed antibiotics (aOR, 1.6; 95% CI, 1.5-1.7; P < .001), and the number of rooms the patient stayed in during their hospitalization(s) (aOR, 1.1; 95% CI, 1.1-1.2; P < .001). The decision-tree machine-learning method confirmed these findings. Graph network analysis established 3 main pathways by which the VRE cases were connected: healthcare personnel, medical devices, and patient rooms.

CONCLUSIONS

We identified risk factors for being a VRE carrier, along with 3 important links with VRE (healthcare personnel, medical devices, patient rooms). Data science is likely to provide a better understanding of outbreaks, but interpretations require data maturity, and potential confounding factors must be considered.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Institute of Clinical Chemistry
04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Clinic of Infectiology

UniBE Contributor:

Atkinson, Andrew David, Ellenberger, Benjamin Till, Piezzi, Vanja, Kaspar, Tanja, Salazar Vizcaya, Luisa Paola, Endrich, Olga, Leichtle, Alexander Benedikt (A), Marschall, Jonas

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0899-823X

Publisher:

Cambridge University Press

Language:

English

Submitter:

Pubmed Import

Date Deposited:

20 Sep 2022 12:59

Last Modified:

02 Mar 2023 23:36

Publisher DOI:

10.1017/ice.2022.66

PubMed ID:

36111457

BORIS DOI:

10.48350/173042

URI:

https://boris.unibe.ch/id/eprint/173042

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