Model May Predict Rheumatoid Arthritis

(WebMD)  A new prediction model developed in the Netherlands may make it easier for doctors to identify which arthritis patients need early, aggressive treatment and which do not.

It is now clear that the best strategy for preventing potentially crippling joint damage in patients with rheumatoid arthritis is very early, aggressive treatment with a potentially toxic combination of drugs.

But not all patients with arthritis have the progressive form of the disease.

Studies suggest that pain and stiffness symptoms resolve on their own in time in as many as half of newly diagnosed patients with undifferentiated arthritis. Undifferentiated arthritis is arthritis that doesn't meet criteria for a more specific type.

But about a third of undifferentiated arthritis patients end up with a diagnosis of rheumatoid arthritis, an autoimmune disease that affects joints and other parts of the body.

In an effort to help guide treatment decisions, researchers in the Netherlands have developed a model for predicting a patient's rheumatoid arthritis risk. The research is published in the February issue of Arthritis and Rheumatism.

"This model would be very easy to adapt to clinical practice, because it is based on assessments rheumatologists already make," researcher Annette van der Helm-van Mil, MD, PhD, tells WebMD.

Model Identified Rheumatoid Arthritis Early

The model was developed using data from 570 newly diagnosed patients with undifferentiated arthritis who were followed for a year.

During that time, 177 were diagnosed with rheumatoid arthritis, while the remaining 393 either achieved remission, did not progress, or were diagnosed with other rheumatologic diseases.

Using a combination of questionnaires, physical examinations, and blood samples, van der Helm-van Mil and her colleagues from the Leiden University Medical Center developed their nine-point model.
Rheumatoid Arthritis Danger Signs

Important predictive variables included a patient's age, sex (most rheumatoid arthritis patients are women), number of tender joints and swollen joints, and certain symptoms characteristic of rheumatoid arthritis -- such as morning stiffness and location of affected joints.

Other tests, including blood tests for C-reactive protein level and rheumatoid factor, were also included in the model.

Based on the assessments, the researchers came up with a 14-point predictive score, with 0 being the lowest likelihood of progression to rheumatoid arthritis and 14 representing the highest likelihood.

None of the study's patients with a score of 3 or less ended up with a diagnosis of rheumatoid arthritis; all of those with a score of 11 or greater did.

The likelihood of progression to rheumatoid arthritis increased in tandem with the scores for those between 4 and 10.

Van der Helm-van Mil says the findings must be confirmed in other patient populations. But she says she's confident the model can be useful in hospitals and doctor's offices.

"This model has very good predictive ability," she says. "It is very sensitive."

Clinical Value Uncertain

Dallas rheumatologist Scott J. Zashin, MD, tells WebMD the model may prove to be a useful tool for predicting rheumatoid arthritis.

One major unanswered question, he adds, is whether its use will lead to different treatment decisions. "I'm not sure that it will, but it would be worthwhile to find out."

Zashin is a clinical assistant professor at the University of Texas Southwestern Medical Center in Dallas.

"For years we have been using our own clinical judgments, based on the measurements used in this model, to make decisions about treatment," says Zashin.

"Formalizing these measurements may help us better identify the patients who will benefit from early treatment, but I think that remains to be seen," he says.

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SOURCES: van der Helm-van Mil, A. Arhritis and Rheumatism, February 2007; vol 56: pp 433-440. Annette H.M. van der Helm-van Mil, MD, PhD, Leiden University Medical Center, Leiden, Netherlands. Scott J. Zashin, MD, rheumatologist; clinical assistant professor, University of Texas Southwestern Medical Center, Dallas.

By Salynn Boyles
Reviewed by Louise Chang