Brain predictors of fatigue in rheumatoid arthritis: A machine learning study, 2022, Goni et al

Abstract
Background
Fatigue is a common and burdensome symptom in Rheumatoid Arthritis (RA), yet is poorly understood. Currently, clinicians rely solely on fatigue questionnaires, which are inherently subjective measures. For the effective development of future therapies and stratification, it is of vital importance to identify biomarkers of fatigue. In this study, we identify brain differences between RA patients who improved and did not improve their levels of fatigue based on Chalder Fatigue Scale variation (ΔCFS≥ 2), and we compared the performance of different classifiers to distinguish between these samples at baseline.

Methods
Fifty-four fatigued RA patients underwent a magnetic resonance (MR) scan at baseline and 6 months later. At 6 months we identified those whose fatigue levels improved and those for whom it did not. More than 900 brain features across three data sets were assessed as potential predictors of fatigue improvement. These data sets included clinical, structural MRI (sMRI) and diffusion tensor imaging (DTI) data. A genetic algorithm was used for feature selection. Three classifiers were employed in the discrimination of improvers and non-improvers of fatigue: a Least Square Linear Discriminant (LSLD), a linear Support Vector Machine (SVM) and a SVM with Radial Basis Function kernel.

Results
The highest accuracy (67.9%) was achieved with the sMRI set, followed by the DTI set (63.8%), whereas classification performance using clinical features was at the chance level. The mean curvature of the left superior temporal sulcus was most strongly selected during the feature selection step, followed by the surface are of the right frontal pole and the surface area of the left banks of the superior temporal sulcus.

Conclusions
The results presented evidence a superiority of brain metrics over clinical metrics in predicting fatigue changes. Further exploration of these methods may support clinicians to triage patients towards the most appropriate fatigue alleviating therapies.

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0269952

 

It detect changes!

This is the Scientology e-meter, which "detects" Thetans, ghosts that inhabit the bodies of people and cause illness. Using the CFQ here is just as much of a joke as this machine, or polygraphs, or Tarot cards, or anything really.

 

The great CFS once more!

 

what are these mysterious fatigue relieving therapies.

 

It isn't clear from the abstract whether the initial MRI findings predicted fatigue improvement or that the change in MRI over 6 months correlated with fatigue improvement. I cannot be bothered to try to find out because the whole thing looks like gibberish.

Surely asking the patient how they feel (a clinical measure) is more use, as a way of finding out how they feel, than any of this?

 

It sounds a bit exaggerated, hyperbolic, when I say that most of how biopsychosocial happens in evidence-based medicine is a popularity contest, an issue of fashion, rather than reliability. Nope, it literally is. It's bad but fashionable so it's used. Same as all the stupid labels invented for us over the years. We didn't invent them, they did. And they continue to be used because they are fashionable.

It's like those psychological experiments they do with a fake group, where only one person is the actual subject and everyone else is part of the experiment, and they're asked simple things like which line is shorter and everyone part of the experiment gives the wrong answer because the goal is to check how people react to situations like this.

Everyone says the longer line is shorter? Well I don't want to rock the boat here so it must be. This questionnaire is obviously bad but it's very fashionable? Let's go right ahead and use it. That's how you build a career.