Geographical information system and spatial–temporal statistics for monitoring infectious agents in hospital: a model using

Background: The emergence and spread of antimicrobial resistance and infectious agents have challenged hospitals in recent decades. Our aim was to investigate the circulation of target infectious agents using Geographic Information System (GIS) and spatial–temporal statistics to improve surveillance and control of healthcare-associated infection and of antimicrobial resistance (AMR), using Klebsiella pneumoniae complex as a model. Methods: A retrospective study carried out in a 450-bed federal, tertiary hospital, located in Rio de Janeiro. All isolates of K. pneumoniae complex from clinical and surveillance cultures of hospitalized patients between 2014 and 2016, identified by the use of Vitek-2 system (BioMérieux), were extracted from the hospital’s microbiology laboratory database. A basic scaled map of the hospital’s physical structure was created in AutoCAD and converted to QGis software (version 2.18). Thereafter, bacteria according to resistance profiles and patients with carbapenem-resistant K. pneumoniae (CRKp) complex were georeferenced by intensive and nonintensive care wards. Space–time permutation probability scan tests were used for cluster signals detection. Results: Of the total 759 studied isolates, a significant increase in the resistance profile of K. pneumoniae complex was detected during the studied years. We also identified two space–time clusters affecting adult and paediatric patients harbouring CRKp complex on different floors, unnoticed by regular antimicrobial resistance surveillance. Conclusions: In-hospital GIS with space–time statistical analysis can be applied in hospitals. This spatial methodology has the potential to expand and facilitate early detection of hospital outbreaks and may become a new tool in combating AMR or hospital-acquired infection. © The Author(s) 2021. 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The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Open Access *Correspondence: marisargomes@ioc.fiocruz. br; christovam.barcellos@icict.fiocruz. br 1 Laboratório de Genética Molecular de Microrganismos, Instituto Oswaldo Cruz Fundação Oswaldo Cruz, Avenida Brasil, 4365, Pavilhão Leônidas Deane, 6o andar, sala 607, Rio de Janeiro, RJ 21040-900, Brazil 7 Institute of Scientific and Technological Communication and Information in Health, FIOCRUZ, Rio de Janeiro, RJ, Brazil Full list of author information is available at the end of the article Page 2 of 12 da Silva et al. Antimicrob Resist Infect Control (2021) 10:92 Background The emergence and spread of antimicrobial resistance (AMR) in recent decades have challenged hospitals, especially by Gram-negative bacteria with multipledrug resistance (MDR) mechanisms, as the development and access to the therapeutic options for treating infections caused by these agents are still limited. In addition, bacterial cross-transmission between patients and transfer of AMR genes between bacteria are increasingly more well recognised in hospitals [1–3]. Consequently, the incidence of healthcare-acquired MDR Gram-negative bacteria has increased dramatically over the last decades making AMR monitoring and containment critical. Currently, Klebsiella pneumoniae is a good example of a hospital-acquired pathogen that often causes colonisation and infection, with high morbidity and mortality rates and costs [4–7]. The global expansion of hypervirulent and MDR clones of K. pneumoniae has been increasingly reported, especially of carbapenem-resistant strains [5, 8–14]. In the study hospital, we experienced an evolving epidemiology among K. pneumoniae with clonal PDR/XDR KPC-2-producing ST437 strains causing intractable infections between 2014 and 2015 [7]. These events led us to investigate a new approach to monitor AMR, since effective control requires a detailed understanding of how the dynamics of the agent’s circulation in the hospital occur. Geographic information system (GIS) is a well-known system that aims to visualise, explore and model in a fast, direct, simple and interactive way, thus allowing mapping of events and enabling the analysis of spatial information in different degrees [15–17]. Although GIS is widely used in health [15–17], it has not been well explored for surveillance of infectious agents throughout hospitals [18]. Investigating the circulation of MDR bacteria, specifically using members of K. pneumoniae complex as a model, with spatiotemporal epidemiological techniques within a hospital unit, is a method that may identify critical areas and outbreaks, helping to improve surveillance and control of healthcare-associated infection and may prevent dissemination of AMR microorganisms. We hypothesised that this novel methodology would have sufficient accuracy to be performed indoors to facilitate the monitoring of any infectious agents and AMR profiles interactively. Therefore, our main goal was to demonstrate the feasibility and gains of conducting surveillance of a target infectious agent by using routine monitoring data to detect clusters through robust spatial epidemiological tools in a hospital.