Pilot study of a combined genomic and epidemiologic surveillance program for hospital-acquired multidrug-resistant pathogens across multiple hospital networks in Australia.

Objectives: To conduct a pilot study implementing combined genomic and epidemiologic surveillance for hospital-acquired multidrug-resistant organisms (MDROs) to predict transmission between patients and to estimate the local burden of MDRO transmission. Design(s): Pilot prospective multicenter surveillance study. Setting(s): The study was conducted in 8 university hospitals (2,800 beds total) in Melbourne, Australia (population 4.8 million), including 4 acute-care, 1 specialist cancer care, and 3 subacute-care hospitals. Method(s): All clinical and screening isolates from hospital inpatients (April 24 to June 18, 2017) were collected for 6 MDROs: VanA VRE, MRSA, ESBL Escherichia coli (ESBL-Ec) and Klebsiella pneumoniae (ESBL-Kp), and carbapenem-resistant Pseudomonas aeruginosa (CRPa) and Acinetobacter baumannii (CRAb). Isolates were analyzed and reported as routine by hospital laboratories, underwent whole-genome sequencing at the central laboratory, and were analyzed using open-source bioinformatic tools. MDRO burden and transmission were assessed using combined genomic and epidemiologic data. Result(s): In total, 408 isolates were collected from 358 patients; 47.5% were screening isolates. ESBL-Ec was most common (52.5%), then MRSA (21.6%), vanA VRE (15.7%), and ESBL-Kp (7.6%). Most MDROs (88.3%) were isolated from patients with recent healthcare exposure. Combining genomics and epidemiology identified that at least 27.1% of MDROs were likely acquired in a hospital; most of these transmission events would not have been detected without genomics. The highest proportion of transmission occurred with vanA VRE (88.4% of patients). Conclusion(s): Genomic and epidemiologic data from multiple institutions can feasibly be combined prospectively, providing substantial insights into the burden and distribution of MDROs, including in-hospital transmission. This analysis enables infection control teams to target interventions more effectively. Copyright © 2020 by The Society