children with arthritis

Arthritis is not just an ailment of old ag it can affect children too; causing lifelong pain and disability in its most severe forms. Fortunately, some kids grow out of it. Knowing which patients will develop milder forms of disease could spare them unnecessary treatment and potential medication side effects but currently; doctors have no way of predicting disease course or severity.

A computational approach

The researchers describe a computational approach based on machine learning; a form of artificial intelligence in which the computer learns to recognize recurrent patterns from a set of data. The algorithm was able to classify patients into seven distinct groups according to the patterns of swollen or painful joints in the body.  Moreover, it also accurately predicted which children will go into remission; faster and which ones will develop a more severe form of the disease.
As a first step, the researchers set out to subtype the children with arthritis but not been treated with medications yet. They analyzed clinical data from 640 children; collected between 2005 and 2010 as part of the pan-Canadian study Research in Arthritis in Canadian Children, Emphasizing Outcomes (ReACCh-OUT).  All children received detailed physical examinations as part of their care which documenting the location of painful (also known as active) joints in the body.
The data revealed seven major patterns of joint activity: joints in the pelvic area, fingers, wrists, toes, knees, ankles and an indistinct pattern. And while the majority of children fell into a single category; about one-third of patients had active joints that belonged to more than one group. These patients with non-localized joint involvement generally had worse outcomes and took longer to go into remission than the patients whose active joints fall into a single pattern.

Patterns of joint involvement

Although unique patterns of joint involvement are recognized at the bedside; the current patient classification for childhood arthritis only takes into account the overall number of affected joints. It is clear that better descriptions of joint involvement are needed that predict disease course and disease severity.  It was striking from the data that children with non-localized joint involvement are different.

Physicians already observed this before as they were treating these children with strong medications;but still not able to control the disease. “Identifying this group of children early will help us target the right treatments early and prevent unnecessary pain and disability from the ongoing active disease.  Because of the complexity of the disease; with multiple joints affects and in a way that can change over time; as well as a relatively small number of patients available.

Detect patterns of joint pain

The team to look beyond standard statistical methods to detect patterns of joint pain. “They have to use machine learning just to detect these seven patterns of disease in the first place;” says Morris, whose team modified the technique known as multilayer non-negative matrix factorization.
“And then we realized there are some children who do not fall into any of the patterns and they have a very bad version of the disease. Now we understand the disease much better we can group children into these different categories to predict response to treatment; how fast do they go into remission and whether or not they can tell they are in remission and remove therapy.”