Why is preventing antibiotic resistance so hard? Analysis of failed resistance management

Abstract

We describe the case of a patient with pancreatitis followed by intra-abdominal infection in which source control was not achieved. Antimicrobial therapy led to the emergence of resistance in multiple organisms through multiple population dynamics processes. While the initial insult was not due to infection, subsequent infections with resistant organisms contributed to a poor outcome for the patient. Though resistance evolution was a known risk, it was difficult to predict the next organism that would arise in the setting of antibiotic pressure and its resistance profile. This case illustrates the clinical challenge of antibiotic resistance that current approaches cannot readily prevent. LAY SUMMARY Why is antibiotic resistance management so complex? Distinct evolutionary processes unfold when antibiotic treatment is initiated that lead, separately and together, to the undesired outcome of antibiotic resistance. This clinical case exemplifies some of those processes and highlights the dire need for evolutionary risk assessments to be incorporated into clinical decision making.

Keywords: antibiotic resistance; antibiotic stewardship; clinical decision making; translational evolutionary medicine.

Figure 1.

Evolutionary processes driving antibiotic resistance. The use of antibiotics can have undesired effects in off-target locations within a patient, with important consequences in terms of resistance evolution. We identify at least three potential population dynamics within these sites that can drive resistance evolution. The presence of antibiotics at these off-target sites selects for the emergence of resistance variants within a host via (i) de novo evolution of resistance through mutation or horizontal gene transfer (purple); (ii) pre-existing resistant sub-populations, whereby low-frequency resistant variants benefit from the clearance of competing sensitive cells or (iii) transmission of resistant variants from different hosts in a process known as colonization resistance

Figure 2.

Patient’s course of infection (top panel) and antimicrobial treatment (lower panel) is shown in (A). Grey shaded areas highlight fungal infections and antifungal treatments. Vertical dotted lines correspond to days of surgical intervention. Antimicrobial treatment was given either orally (red) or intravenously (IV, in orange). Infections were identified from urine (light green), intra-abdominal fluid (blue) or blood (dark blue) cultures. Bubble stamps in the top panel emphasize infections where evidence suggests that antimicrobial resistance emerged through distinct evolutionary process. The ‘stamps’ are as depicted in Fig. 1. (B) Resistance profiles of the various infectious agents over time as determined by the Clinical and Laboratory Standards Institute (CLSI) breakpoints with MIC measured with Sensititre: Resistant (red), intermediate (yellow), sensitive (light blue) and non-interpretable MICs (grey; embedded x indicates the measured MIC in µg/ml)