Association Between Differential Heterogeneity of Antibiotics Consumption and Share of Resistant Pathogens and Its Implication for Antibiotic Stewardship in a German Hospital Intensive Care Unit

Background: The rapid rise in antimicrobial resistance has become one of the 10 most pressing health problems worldwide in recent years. Antibiotic stewardship offers hope in the fight against antibiotic resistance, but it is currently still falling short of expectations. A better understanding of the dynamics of the interaction between antibiotic consumption and the emergence and spread of resistance is urgently needed. Methods: We discuss a simple dynamic model based on a differential equation to describe the increase in the proportion of a pathogen’s antimicrobial resistance to an antibiotic as a function of the time-dependent consumption of that antibiotic. Furthermore, we investigate the association of heterogeneity in the consumption of antibiotics with the rate of resistant pathogens. Data basis is the hospital information system and the patient data-management system of a German hospital, restricted to the intensive care unit. To quantify heterogeneity, we discuss and compare different entropy measures. Results: For some pathogen–antibiotic pairs, the consumption-dependent dynamic model for the growth in the proportion of antimicrobial resistance provides acceptable predictions, while for others, the model is less suitable. Cross-resistance and complex interactions with other pathogens and antibiotics may be responsible for this, suggesting that the observed dynamic behavior should be complementary, described using heterogeneity models. Time courses of Shannon entropy, the Antibiotic Heterogeneity Index, and the negative Gini Index correlate positively with the time series of the resistance rate. Thus, an increase in heterogeneity correlates with a decreasing resistance rate. However, a time-delayed cross-correlation of a differential entropy measure with resistance share suggests a functional dependence that can be utilized for antibiotic stewardship. Conclusions: Evidence is provided that the amount of consumption of certain antibiotics drives the corresponding proportions of pathogens’ resistance to these antibiotics; however, the model predictions of these univariable models are generally not sufficiently good, pointing to a more complex interaction dynamics. Therefore, we switch to the level of structural features and show that the degree of constantly mixing of the shares of antibiotic consumption has a control function regarding the incidence of resistance. Controlling differential consumption heterogeneity, therefore, appears to be a feasible operational basis for antibiotic stewardship. Experimental studies are demanded to identify functional dependencies; however, the integration of clinical expertise with model-based prediction appears to be a feasible antibiotic stewardship strategy.