Eleven Years of Meropenem Resistance: Insights from a Northern Italy Health Unit

Abstract Background In the document “WHO Bacterial Priority Pathogens List”, three pathogens are cited as of critical or high priority for public health due to their resistance to carbapenems, which are drugs of choice for hard-to-treat infections. Our analysis has assessed the dimension of carbapenem resistance and associated factors for those three bacterial species, evaluating laboratory data from 2013 to 2024 in a local health unit in Northern Italy. Methods Data of Acinetobacter Baumannii (AB), Klebsiella Pneumoniae (KP) e Pseudomonas Aeruginosa (PA) have been evaluated for resistance patterns to meropenem, patient sex, patient age, year of detection, level of care (outpatient care, inpatient care, home care), and type of sample. A subanalysis of the hospitalized patients was performed including the number of beds of the facility. Chi-squared test and logistic regressions were performed with R. Results A number of 352 for AB, 8977 for KP and 4094 for PA records were found in the period. Percentage of resistance varied from 69% for AB, 16% for KP and 16% for PA. For all the three species a difference between resistance profile to meropenem and level of assistance was assessed (chi-squared test, p < 0.01). In multivariable regression, the association with resistance profile were: • for AB, year of detection (OR:1.1;p<0.05) and inpatient care (OR:7.3;p<0.01); • for KP, male sex (OR:2.3;p<0.01), year of detection (OR:0.9;p<0.01), age of the patient (OR:1.02;p<0.01) and inpatient care (OR:3.8;p<0.01); • for PA, male sex (OR:1.5;p<0.01), inpatient care (OR:1.7;p<0.01) and home care (OR 1.6; p < 0.01). Stratifying by hospitalized patients, resistance patterns of association remained similar; the number of beds in the facilities was negatively associated with resistance for PA (OR:0.9;p<0.01) positively for KP (OR:1.02;p<0.01). Conclusions Laboratory data allow an overview of resistance patterns. Different pathogens species show different associated factors and resistance control trends. Key messages • Laboratory data analysis allow overview of antimicrobial resistance patterns on a large scale and comparison between different pathogen species. • Demographic and care-related determinants of antimicrobial resistance display general trends that require integration with species- and context-specific data.