Personalized and dynamic antibiograms-an exploration in seven infectious syndromes

Abstract

To prevent antimicrobial resistance and inform better, antibiograms should distinguish different biomedical situations. It is also desirable that new antibiograms provide in vivo, temporal, and patient-specific immunological information. Here, the informative ability of a pattern recognition-based method was explored with data collected from patients that experienced seven infectious syndromes (pneumonia, endocarditis, tuberculosis, syphilis, as well as skin and soft tissue, intra-abdominal, and/or urinary tract infections associated with meningitis). Interactions among seven dimensions (7D) were investigated: (i) space, (ii) time, (iii) temporal data directionality, (iv) immunological multicellularity, (v) antibiotics, (vi) immunomodulation, and (vii) personalized data. Omissions and ambiguity (confounding different biological situations) occurred when static metrics were used in isolation, such as leukocyte percentages. In contrast, hidden information was uncovered when complexity and dynamics were assessed. The 7D approach grouped together observations that displayed similar immune profiles and identified antibiotics that modulated specific leukocytes. For instance, in tuberculosis, blood monocytes were modulated by isoniazid-related antimicrobials. In spite of the diverse syndromes analyzed, this proof-of-concept discriminated. It is suggested that the simultaneously exploration of numerous dimensions associated with complexity may be biologically interpretable, prevent ambiguity, promote research, expand machine learning-oriented methods, and support personalized medicine.