In knee osteoarthritis, cartilage in the knee joint gradually wears away, causing pain and stiffness. It affects an estimated 303.1 million people worldwide and can lead to the need for total knee replacement. Being able to better predict how a person’s knee osteoarthritis may worsen in the near future could help inform more timely treatment. Prior research suggests that computational models combining multiple types of data—including a patient’s MRI results, clinical assessments, and blood and urine biochemical tests—could enhance such predictions.

The integration of all three types of information in a single predictive model has not been widely reported. To address that gap, Wang and colleagues utilized data from the Foundation of the National Institutes of Health Osteoarthritis Biomarkers Consortium on 594 people with knee osteoarthritis, including their biochemical test results, clinical data, and a total of 1,753 knee MRIs captured over a 2-year timespan.