Misinterpreting this Depression Study May Lead Doctors to Treat the Wrong People

The study claims to predict response to escitalopram treatment — but that is a potentially dangerous interpretation.

It’s a new year, and after a little holiday break I’m back and, frankly, a bit cranky as I peruse the recently-published medical literature, so I’m focusing today on a rather small study, but one that hits a pet peeve of mine and so I’m going to channel my inner Andy Rooney here and gripe for a bit.

Appearing in JAMA Network Open we have this article with the compelling title “Use of Machine Learning for Predicting Escitalopram Treatment Outcome From EEG Recordings in Adults with Depression”.

I like to know what I’m getting into when I read a title. And this title promises quite a bit. To me, it reads like researchers used an EEG and some fancy machine-learning stuff to predict which patients with depression would benefit from escitalopram treatment.

That idea, using a machine learning model to choose the best psychiatric treatment is holy grail-level personalized medicine stuff. See, when confronted with major depressive disorder, docs often try medication after medication to see what sticks — anything to lessen that trial-and-error approach would save tons of time, not to mention lives.

But that is not what this study is about. Walk with me through the methods and you’ll see what I mean.

Researchers from British Columbia analyzed EEG data from 122 adult patients with major depression who were initiated on escitalopram therapy.

As you know, an EEG outputs a ton of data — multiple electrodes, thousands of measurements. This is actually an ideal place to use machine learning tools to squeeze all that data into a single number and the authors do an exemplary job of using a well-established machine learning algorithm called a support vector machine to take those gobs of data and turn it into a prediction.