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. Antimicrobial stewardship is a beacon of hope with regard to measures to combat resistance, but 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: First, 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. Second, we investigate the association of heterogeneity in the consumption of antibiotics with the rate of resistant pathogens. The data basis for this 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, including differential entropy. Results: For some pathogen-antibiotic pairs, the consumption-dependent dynamic model for the growth in the proportion of antimicrobial resistance provides surprisingly good predictions, while for others, the model is less suitable. Cross-resistance and other 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. It turns out that the three time courses of Shannon entropy, the so-called 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: We provide evidence that the amount of consumption of certain antibiotics drives the corresponding proportions of pathogens’ resistances to these antibiotics, however, the model predictions of the mono-causal models used are generally not sufficiently good, pointing to a more complex interaction dynamics. Therefore, we switch to the level of structural features and are thus able to show that the degree of constantly mixing the shares of antibiotic consumption has a control function with regard to the incidence of resistance. Controlling differential consumption heterogeneity therefore appears to be a feasible operational basis for antibiotic stewardship. Our results speak in favor of an experimental study or at least inter-clinical comparative studies, as this is the only way to identify functional dependencies. In summary, the integration of clinical expertise with model-based prediction appears to be a feasible antibiotic stewardship strategy.