AI Tool Enhances Patient Care by Predicting Feeding Tube Timing for MND

A new AI tool developed by the University of Sheffield predicts the optimal timing for feeding tube placement in Motor Neurone Disease patients, improving care and quality of life.

A newly developed AI tool could significantly enhance patient care for those with Motor Neurone Disease (MND) by accurately predicting when a feeding tube is needed. Created by researchers at the University of Sheffield, this tool aims to provide essential information to doctors and patients, enabling timely planning for life-extending interventions.

MND, also known as Amyotrophic Lateral Sclerosis (ALS), is a severe condition that progressively damages nerve cells controlling muscles. As the disease progresses, swallowing becomes difficult, leading to malnutrition and weight loss. A gastrostomy, which involves placing a feeding tube directly into the stomach, is crucial for maintaining nutrition and quality of life. However, timing is critical; performing the procedure too early or too late can have adverse effects.

AI Model for Predicting Disease Progression

Researchers from Europe, led by Professor Johnathan Cooper-Knock at the University of Sheffield's Institute for Translational Neuroscience (SITraN), have developed an advanced machine learning model to address MND's unpredictable progression. This model uses routine diagnostic measurements to estimate disease progression speed in individual patients, helping clinicians determine the best time for intervention.

Professor Johnathan Cooper-Knock stated, "One of the hardest aspects of living with MND is the uncertainty; it is a cruel and devastating disease." Until now, predicting when a patient might need a feeding tube was challenging, with timelines ranging from eight months to 20 years post-diagnosis.

Improving Patient Outcomes with AI

The AI model was trained using data from over 20,000 MND patients to predict when significant weight loss would occur—a key indicator for needing a feeding tube. At diagnosis, the tool predicted the optimal window with a median error of just 3.7 months. For patients reassessed six months later, accuracy improved to a median error of 2.6 months.

Professor 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." Knowing this critical window allows clinicians to shift from reacting to proactively managing disease progression, providing optimal care and avoiding complications from delayed surgery.

Future Prospects and Clinical Trials

The study's promising results were published in eBioMedicine. Researchers are now planning a prospective clinical trial to validate the tool before integrating it into standard MND care. This tool ensures patients receive timely care, maximising their quality of life and potentially extending survival.

By identifying the optimal time for gastrostomy within three months, doctors and patients can better plan surgeries. This proactive approach helps ensure the best possible quality of life and may extend survival for those living with MND.

With inputs from PTI