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A clinical–epidemiological score to predict CR-GNB sepsis to guide empirical antimicrobial therapy (EAT), using local data, persists as an unmet need. On the basis of a case–case–control design in a prospective cohort study, the predictive factors for CR-GNB sepsis were previously determined as prior infection, use of mechanical ventilation and carbapenem, and length of hospital stay. In this study, each factor was scored according to the logistic regression coefficients, and the ROC curve analysis determined its accuracy in predicting CR-GNB sepsis in the entire cohort. Among the total of 629 admissions followed by 7797 patient-days, 329 single or recurrent episodes of SIRS/sepsis were enrolled, from August 2015 to March 2017. At least one species of CR-GNB was identified as the etiology in 108 (33%) episodes, and 221 were classified as the control group. The cutoff point of ≥3 (maximum of 4) had the best sensitivity/specificity, while ≤1 showed excellent sensitivity to exclude CR-GNB sepsis. The area under the curve was 0.80 (95% CI: 0.76–0.85) and the number needed to treat was 2.0. The score may improve CR-GNB coverage and spare polymyxins with 22% (95% CI: 17–28%) adequacy rate change. The score has a good ability to predict CR-GNB sepsis and to guide EAT in the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]