Five-year resistance trends in pathogens causing healthcare-associated infections at a multi-hospital healthcare system in Saudi Arabia, 2015–2019

Abstract Background Awareness of the resistance patterns of the pathogens within a given health care setting is important to inform the selection of appropriate antimicrobial therapy to reduce the further rise and spread of antimicrobial resistance (AMR), reduce the rate of healthcare-associated infections (HAIs), and multidrug-resistant (MDR) organisms. We aimed to describe resistance patterns to several antimicrobials in pathogens isolated from patients causing HAIs using the data gathered at three private tertiary care hospitals over 5 years, in Saudi Arabia. Patients and methods Data on trends of antimicrobial resistance among bacteria causing HAIs and MDR events in children and adults at three private hospitals were collected retrospectively between 2015 and 2019 using the infection control and prevention surveillance data. Results Over a 5-year period, 29,393 pathogens caused 17,539 HAI events in 15,259 patients. About 57.3% of patients who suffered HAIs were females and the average age was 38.4 ± 16.8 years (81.4% were adults and 18.6% were children). Gram-negative pathogens were 4-times more likely to cause HAIs compared to gram-positive bacteria (79.3% vs. 20.7%). The ranking of causative pathogens in decreasing order was Escherichia coli (42.2%), Klebsiella species (16.8%), and Staphylococcus aureus (13.9%). Acinetobacter species were the only pathogens to decrease significantly (7% reduction, p-value=0.033). The most common resistant pathogens were Extended-spectrum cephalosporin-resistant E. coli (37.1%), Extended-spectrum cephalosporin-resistant Klebsiella (27.8%), Carbapenem-non-susceptible Acinetobacter species (19.5%), Carbapenem-non-susceptible Pseudomonas aeruginosa (19.2%), and Methicillin-resistant Staphylococcus aureus (18.6%). Conclusion National collaboration is required by prompt feedback to the local authorities to tackle the facility and regional differences in antimicrobial resistance rates, which can help plan timely containment interventions to stop and contain microbial threats and to assess their impact swiftly.