- A new AI-powered tool accurately predicts when a patient with motor neurone disease (MND) will need a feeding tube
- Correctly timing the procedure is critical; this tool helps avoid placing the tube too early - lowering quality of life, or too late - increasing health risks and causing detrimental weight loss
- The model can predict the time of need with an error of just three months in all patients and less than month in the largest group of patients
- The prediction tool uses patient data routinely collected at diagnosis and has shown stable, reliable performance across diverse patient populations in the US, Europe, and Sweden
A new AI tool that accurately predicts the need for a feeding tube could transform patient care and improve quality of life for people living with Motor Neurone Disease (MND).
The new tool, developed by a team at the University of Sheffield, will improve patient care by providing doctors and patients with the crucial information to plan the life-extending intervention at the ideal time.
MND - also known as Amyotrophic Lateral Sclerosis (ALS) - is a devastating, progressive and fatal condition that attacks the nerve cells controlling muscles. As the disease advances, many patients struggle to swallow, leading to dangerous weight loss and malnutrition. A gastrostomy is a procedure to place a feeding tube directly into the stomach, which is vital for maintaining nutrition, quality of life, and even survival.
However, timing is critical. If the procedure is carried out too early, it can have an adverse effect on quality of life. If done too late, it carries greater risks and can be less effective because patients can enter a 'refractive' stage of being malnourished. The procedure may even become impossible due to weakened breathing muscles.
Researchers from across Europe, led by Professor Johnathan Cooper-Knock at the University of Sheffield's Institute for Translational Neuroscience (SITraN), created a sophisticated machine learning model (AI) to tackle the challenge of MND's unpredictable progression. The model uses routine measurements collected at the time of diagnosis to estimate how quickly the disease will progress in each individual patient, thereby allowing clinicians to pinpoint the optimal time for the critical intervention.
"One of the hardest aspects of living with MND is the uncertainty, it is a cruel and devastating disease." said Professor Johnathan Cooper-Knock from the University of Sheffield.
"Until now it has been impossible for clinicians to predict when someone living with MND may need a feeding tube - it could be anything from eight months after diagnosis to 20 years.
"By pinpointing the optimal window for a gastrostomy to within three months, doctors and patients can better plan for the surgery and we can help ensure the best possible quality of life and potentially extend survival."
Researchers used data from more than 20,000 MND patients to develop the AI model to predict the time when significant weight loss will have occurred - this is a key indicator that a feeding tube is needed. The new tool was able to predict the optimal window within a median error of just 3.7 months at the time of diagnosis. For patients who were re-evaluated six months after diagnosis, the model's accuracy improved further, with a median error of just 2.6 months.
Professor Johnathan Cooper-Knock added: "This is not just about a surgical procedure; it's about preserving a patient's dignity and ability to maintain nutrition safely. For a clinician, knowing this critical window allows us to move from reacting to the disease's progression to proactively managing it, providing optimal care and avoiding the distressing complications of rushing a patient to surgery when they are already too frail.
"Ultimately, this tool ensures patients get the right care at the right time, maximizing the quality of every single day."
The promising results of the study, published in the journal eBioMedicine, mean researchers are now planning a prospective clinical trial to formally validate the tool before it can become a standard part of MND care.
