Urinary tract infection (UTI) at the point of care

Urinary tract infections are accountable for millions of medical consultations and put significant burden on healthcare systems and societies [1]. Since proper UTI diagnostics is time consuming, clinicians often face an information gap where diagnostic information is not available, but treatment options are requested. Often, this results in the empiric prescription of antibiotics, even though up to 80% of suspected UTI cases turn out to be negative [2]. Here, innovative technology can help to close this information gap and to improve clinical decision support in the doctor’s office and in the hospital.

A look into the future of addressing UTIs and antibiotic use

A patient searching for relief from UTI-related malaise, added to the time-consuming diagnostics for appropriate clinical decision making, creates a dilemma in clinical practice, leading to antibiotic treatment of individuals without proper diagnostics [4]. A recent study revealed that patients prefer convenience and confidence of diagnostic approaches instead of speed in context of AMR [6].

To shape the advancement of healthcare we are aiming to build a bridge between the laboratory and clinicians through diagnostic technology. Diagnostic information must be accessible and understandable at all levels along the clinical decision pathway to allow quick and confident decision support for the treatment of UTI. This will improve the clinical dilemma as well as the patient experience and address the growing impact of AMR through targeted antimicrobial treatments.

One example is antibiotic susceptibility testing (AST), which is performed to identify which antimicrobial regimen is specifically effective for individual infections. There are newer technologies under development to improve and speed up the AST process. One way this is happening is by measuring the growth rate of bacterial cells in real time through microfluidics and image analysis methods. A sample is loaded onto a microfluidic chip and any bacteria present are caught in bacteria-sized traps. The trapped bacteria are monitored, whereby the loading time gives an estimate of the density of bacteria in the sample. Bacterial growth is monitored in each trap, some of which contain a candidate antibiotic. The average growth rates are calculated in real time and the bacteria are considered susceptible if its growth is duly inhibited.