A new approach in the management of urinary tract infections for kidney transplant patients

31 March 2025

Dr Stephen Kelly from Queen’s University Belfast has received a PhD studentship of £87,000 to look at new ways of identifying urinary tract infections in kidney transplant patients and predicting which antibiotics will be most effective.

Dr Stephen Kelly

“By looking more closely at the urinary tract microbiome, we hope to learn more about the development of UTI, so that we can treat patients more effectively and quickly. This work could have huge benefits not only for kidney transplant patients but for those affected by kidney disease more broadly.” Stephen Kelly.

The problem

Urinary tract infections (UTIs) are the most common infection in the early phase following kidney transplantation. During transplant surgery, devices such as urinary catheters, are placed to help urine flow properly. Although necessary following the procedure, this can provide a surface for the bacteria that are naturally found in the urinary tract to stick to and may lead to infection.

UTIs are usually first noticed when a patient shows symptoms, such as a high temperature or pain when urinating. Dipstick tests may be used, but they can only detect infection when a considerable number of bacteria are present. By this stage, infection is already well-developed and can be more difficult to treat. This is particularly problematic in transplant patients who may have reduced immune function owing to the medications needed to protect their new kidney.

When an infection is suspected, samples are sent to the lab for the bacteria to be grown, identified and tested against different antibiotics to see which works best, which can take time.

The solution

This project aims to support earlier and more precise diagnosis of UTIs by using sensitive techniques which can measure the amount of bacterial DNA in the urine, even at very low levels. It involves analysing samples, already taken from patients for other reasons, to identify and test bacteria against different antibiotics. This will allow treatment for infection to be started earlier, before it has a chance to cause more serious effects.

Stephen and his team will also analyse genetic information from bacteria in urine, before and after transplant surgery to compare these profiles with patients who go on to develop UTIs. This may also allow healthcare professionals to predict who is at higher risk of developing an infection, based on the types and amounts of bacteria in their urinary tract before surgery.

This grant will also support the researchers to use a computer-based technique known as machine learning to predict which antibiotics will be most effective in each UTI. These models have already been developed and have been shown to be very effective against a large range of bacteria but have not yet been tested extensively with bacteria from the urinary tract.

What does this mean for kidney patients?

This work has the potential to enable early, accurate diagnosis of UTIs and support prompt and effective treatment selection. This approach could also reduce the ineffective use of antibiotics, which contributes to the growing problem of antibiotic resistance – this occurs when bacteria evolve certain mechanisms that protect them against certain antibiotics meaning they are no longer effective for the individual with infection.