Carbapenems are last-resort antibiotics, so it is crucial to prevent the spread of carbapenem-resistant Gram-negative bacteria (CRGNB) in hospitals. In 2011, the state of Hesse, Germany, has established a notification system for reporting detected CRGNB cases. To optimally take advantage of these data, we developed a computational tool that predicts outbreak events among the patients reported in a given time window, ranks them according to statistical significance and provides tailored visualizations for individual institutions. It uses a machine learning framework combining supervised and unsupervised techniques with patient-centric and hospital-centric approaches to systematically check for patient clusters with consistent resistance types and possible transmission events in the patient history of hospital stays. Leveraging patient history by custom algorithms is the key novelty of this work, becoming necessary because a colonization with CRGNB may remain undiscovered for a long period. Therefore, place or time of notifications do not hold information on the potential transmission source, and common epidemiological surveillance mechanisms are not applicable. The tool comprehensively recovered human-annotated clusters with different characteristics, such as single or multiple bacterial species, single or double resistances and specific or unspecific resistance types. The top significant additional predictions are investigated via sequencing. Furthermore, cluster recovery dropped drastically when including only the last hospital stay before the patient's CRGNB check, highlighting the importance of reporting earlier hospital stays. Adoption of automated data analysis and transparent presentation of evidence will increase awareness, leading to more complete data, precise predictions and timely countermeasures.

Co-authors: Petra Heinmüller, Philipp Thomas, Lisa Barros de Andrade E Sousa, Theresa Willem, Ivo Foppa, Marie Piraud, Anja M. Hauri