AI tool could help heart attack survivors receive tailored care

An AI tool has identified three health paths followed by heart attack survivors, which researchers say could help clinicians tailor care earlier.

The study analysed health records from 12,701 UK Biobank participants who had experienced a heart attack and tracked the sequence and timing of new diagnoses over the next five years.

Machine learning was used to group patients whose health developed in similar ways after the event.

Researchers from the University of Surrey identified three distinct trajectories.

The largest group, representing 63 per cent of patients, developed cardiometabolic conditions including high blood pressure, type 2 diabetes and dyslipidaemia, alongside episodic heart and respiratory complications.

A second group, accounting for 23 per cent and thought to be smokers, experienced deterioration affecting the lungs, musculoskeletal system and other organs.

This group had a mortality rate of 44 per cent, more than three times that recorded in the largest group.

Around 14 per cent of patients developed structural heart diseases, arrhythmias and kidney problems.

The researchers found that information available when a heart attack occurred, including patients’ existing diagnoses and demographic data, could be used to predict which trajectory they were likely to follow.

Dr Anthony Onoja, lead author and research fellow at the University of Surrey, said: “We found that we could predict the health trajectory a patient would follow after a heart attack, at the point of the event itself, using their pre-existing diagnoses and demographic data. Our AI tool was incredibly effective at finding and predicting the highest-risk group, where respiratory conditions, older age, and higher deprivation scores were key predictors.

“Our approach is exciting, but we are still early in this journey, and we believe that in the future this could help hospitals identify people who follow these trajectories early and develop tailored care for them.”

The team also examined whether the different trajectories reflected distinct biological processes.

Genetic analysis confirmed that each group mapped to different molecular pathways, including immune activation and tissue remodelling in the largest group, insulin signalling and lipid transport in the arrhythmia group, and chronic inflammation and degeneration in the smoking-related group.

Professor Nophar Geifman, senior author of the study from the University of Surrey, said: “Clinicians typically use risk assessments, such as the SMART score, to help them understand how likely a patient is to have another heart event. We found that these tools are still the strongest single predictor of mortality in our study, but the trajectories added detail that a stand-alone score cannot provide. The patterns we have identified show that we can capture more than just a patient’s risk but, crucially, why, and where intervention could be needed.