AI model predicts patients at most risk of complication during treatment for advanced kidney failure

Artificial intelligence experts and healthcare professionals in Portsmouth have come together to help prevent a common and painful complication in advanced kidney failure treatment.

A study led by the University of Portsmouth and Portsmouth Hospitals University NHS Trust (PHUT) has developed an AI model to predict which patients are most at risk of their blood pressure dropping during dialysis; a condition known as intradialytic hypotension (IDH).

3 million people have Chronic Kidney Disease in the UK and 31,000 of these are on haemodialysis (Kidney Care UK), where their blood is circulated through a machine to clean it of toxins.

One of the most common complications for patients undergoing this treatment at home or in centres is IDH, which occurs when their blood pressure drops suddenly. It is associated with increased mortality and hospitalisations, and until now there has been no reliable way to predict if it will occur.

Pre-dialysis and real-time data were collected from 10 treatment centres over two decades (2000-2020), involving 3,944 patients. The team used data comprising a total of 73,323 sessions with 36,662 IDH events.

Using this information, they identified 33 variables to determine the most at-risk individuals. These were all observations that are routinely collected during clinical care, such as weight, temperature, age, blood pressure, medication and treatment details.

Project lead, Dr Shamsul Masum from the University’s School of Electrical and Mechanical Engineering, said: “This research highlights the value of using machine learning in healthcare, particularly in complex situations like haemodialysis. Predicting hypotension not only helps clinicians intervene early but also opens the door to personalised care.

"As we continue to develop and refine these models, the goal is to create a practical decision-support system that could enhance dialysis management, patient safety and quality of care.”

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Source: University of Portsmouth, 23 October 2024