Predicting treatment success in rheumatoid arthritis

Particularly in early disease, prediction can be achieved for people with lower baseline levels of diseases activity

Rheumatoid arthritis is a chronic inflammatory disease that affects a person’s joints, and may cause pain and disability. Rheumatoid arthritis can affect people of all ages, but it most often starts between the ages of 40 and 50 – although this can depend on where you live. Rheumatoid arthritis is more common in women than men. There are many different treatments available for people with rheumatoid arthritis. Predicting the success of specific treatments in individuals is limited. Previous studies have confirmed that people with lower levels of disease activity at baseline are more likely to achieve favourable disease activity states (for example, low disease activity or remission) after treatment. Additionally, higher levels of disease activity at baseline are associated with greater change over time. Many different variables can be monitored to check on a person’s disease over time, and to measure how well treatments are working. These are referred to as the core set variables.

The authors wanted to work out a set of variables that could be used to predict different types of treatment response in people with rheumatoid arthritis.

The study looked at 1,724 people who had taken part in one of three clinical trials called ASPIRE, ATTRACT, and GO-BEFORE. These studies included people with both early and established disease, as well as groups with and without prior treatment experience.

Using data from the three trials, the authors analysed patient and disease characteristics (variables) before treatment to see which ones corresponded to better reaching of treatment goals. Three goals were defined:
1) Whether people achieved a certain response (a percentage improvement).
2) If they achieved a particular disease state (low disease activity or remission).
3) If they achieved a larger numerical improvement on disease activity scales.

The main finding was that the outcomes for different predictors depend on what is considered treatment
success. For example, the best prediction was achieved with a combination of shorter disease duration, male gender, and lower disease activity. This means that male patients with short disease duration and lower levels of disease activity may have the best chance to reach targets of low disease activity or remission.

When predicting whether people would achieve a certain percentage of response in their disease activity
levels, there were no consistently significant predictors – with the exception of a blood test that measures a marker of inflammation called C-reactive protein (often shortened to CRP). Higher CRP levels at baseline were associated with better chance of achieving a response. Based on the analysis, the authors concluded that there is no association between core set variables at baseline and response.

It was already known that people with lower levels of disease activity have a higher chance of reaching a good disease state. This study confirmed that this is true for all disease activity variables, except for markers of inflammation. However, the study also noted that larger changes in disease activity are easier to reach when a person’s disease activity is higher at treatment start – so the predictability depends on how patients and their healthcare provider define treatment success.

One of the main limitations of the analysis is that it is based on trials from a group of drugs called TNF-inhibitors. Because of this, the results may not necessarily be generalisable to different types of treatment. Another limitation is that the analysis includes only people in a trial setting, and therefore it may not apply to people in a real-life population.

The data collected may be helpful when talking to people with rheumatoid arthritis about their chance of treatment success in a given clinical situation. It may also be useful to help optimise clinical trial design and help select the best population with respect to trial endpoints. This would mean fewer people would need to be studied.

This information can be expanded with other treatments and additional predictors to help get closer to a more personalised medicine approach.

If you have rheumatoid arthritis, understanding your treatment goals is important. While large changes are easier to achieve if you have high levels of disease activity before treatment, it will still be important to achieve a good state (remission or low disease activity). The predictors of good response to treatment will help you obtain the best possible care and achieve better outcomes, which ultimately may improve your quality of life.

If you have any concerns about your disease or its treatment, you should talk to your doctor.

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Date prepared: January 2022
Summary based on research article published on: 04 October 2021
From: Capelusnik D, Aletaha D. Baseline predictors of different types of treatment success in rheumatoid arthritis. Ann Rheum Dis 2022;81:153–8. doi:10.1136/annrheumdis-2021-220853

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