If it ever comes a day when Artificial Intelligence replaces rheumatologists on diagnostics, we can yet have a second career as clairvoyants.
In RA and many other autoimmune diseases where targeted therapies have transformed our therapeutic landscape, much of the judgment calls we make in daily practice involve prediction and prognostication, just so that we can target the costly and potentially hazardous treatments to those who will need and benefit most from such treatments.
In an earlier post, Ultrasound-detected synovitis in ACPA-positive but asymptomatic patients can predict for eventual development of RA, justifying early institution of immunomodulatory treatment to arrest disease.
ACPA-positivity also predicts for worse RA outcomes (disability, co-morbidity and mortality), justifying early and aggressive therapy. Today, we look at another predictor of bad disease.
Gazing at the Rheuma crystal ball, the following factors predict for worse RA outcomes (rapidly destructive, difficult to treat, unremitting):
1) RF, ACPA & 14-3-3η (especially in high titres);
2) Erosions (early evidence of joint destruction) already present at first diagnosis;
3) Inadequate response to initial appropriately aggressive treatment (steroids or targeted agents) ("failure breeds failure").
As stated in my post on 30 Jan, I do not consider a high ESR/CRP (inflammatory burden) at initial presentation as predictive of a bad outcome. Those who respond well to treatment tend to do well ("success breeds success").