Article
Geographical information system and spatial–temporal statistics for monitoring infectious agents in hospital: a model using Klebsiella pneumoniae complex
Registro en:
SILVA, Priscila Pinho da et al. Geographical information system and spatial–temporal statistics for monitoring infectious agents in hospital: a model using Klebsiella pneumoniae complex. Antimicrobial Resistance and Infection Control, v. 10, n. 92, p. 1-12, 2021.
2047-2997
10.1186/s13756-021-00944-5
Autor
Silva, Priscila Pinho da
Silva, Fabiola A. da
Rodrigues, Caio Augusto Santos
Souza, Leonardo Passos
Lima, Elisangela Martins de
Pereira, Maria Helena B
Candella, Claudio Neder
Alves, Marcio Zenaide de Oliveira
Lourenço, Newton D.
Tassinari, Wagner S
Barcellos, Christovam de Castro
Gomes, Marisa Zenaide Ribeiro
Resumen
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, identifed by the use of Vitek-2 system (BioMérieux), were extracted from the hospital’s microbiology labora tory 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 profles 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 signifcant increase in the resistance profle of K. pneumoniae complex was detected during the studied years. We also identifed two space–time clusters afecting adult and paediatric patients harbouring CRKp complex on diferent foors, unnoticed by regular antimicrobial resistance surveillance. Conclusions: In-hospital GIS with space–time statistical analysis can be applied in hospitals. This spatial methodol ogy 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.