Efficiently identifying individuals at high risk for treatment resistance in major depressive disorder using electronic health records.

ABSTRACT:

Background

With the emergence of evidence-based treatments for treatment-resistant depression, strategies to identify individuals at greater risk for treatment resistance early in the course of illness could have clinical utility. We sought to develop and validate a model to predict treatment resistance in major depressive disorder using coded clinical data from the electronic health record.

Methods

We identified individuals from a large health system with a diagnosis of major depressive disorder receiving an index antidepressant prescription, and used a tree-based machine learning classifier to build a risk stratification model to identify those likely to experience treatment resistance. The resulting model was validated in a second health system.

Results

In the seco

SUBMITTER: Lage I 

PROVIDER: S-EPMC9980713 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature