
==== Front
medRxiv
MEDRXIV
medRxiv
Cold Spring Harbor Laboratory

10.1101/2024.09.11.24313478
preprint
1
Article
Cross-EHR validation of antidepressant response algorithm and links with genetics of psychiatric traits
Sealock Julia M.
Tubbs Justin D. http://orcid.org/0000-0002-6671-6816

Lake Allison M.
Straub Peter
Smoller Jordan W.
Davis Lea K.
12 9 2024
2024.09.11.24313478https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
http://medrxiv.org/lookup/doi/10.1101/2024.09.11.24313478
nihpp-2024.09.11.24313478.pdf
Abstract

Objective

Antidepressants are commonly prescribed medications in the United States, however, factors underlying response are poorly understood. Electronic health records (EHRs) provide a cost-effective way to create and test response algorithms on large, longitudinal cohorts. We describe a new antidepressant response algorithm, validation in two independent EHR databases, and genetic associations with antidepressant response.

Method

We deployed the algorithm in EHRs at Vanderbilt University Medical Center (VUMC), the All of Us Research Program, and the Mass General Brigham Healthcare System (MGB) and validated response outcomes with patient health questionnaire (PHQ) scores. In a meta-analysis across all sites, worse antidepressant response associated with higher PHQ-8 scores (beta = 0.20, p-value = 1.09 x 10 −18 ).

Results

We used polygenic scores to investigate the relationship between genetic liability of psychiatric disorders and response to first antidepressant trial across VUMC and MGB. After controlling for depression diagnosis, higher polygenic scores for depression, schizophrenia, bipolar, and cross-disorders associated with poorer response to the first antidepressant trial (depression: p-value = 2.84 x 10 −8 , OR = 1.07; schizophrenia: p-value = 5.93 x 10 −4 , OR = 1.05; bipolar: p-value = 1.99 x 10 −3 , OR = 1.04; cross-disorders: p-value = 1.03 x 10 −3 , OR = 1.05).

Conclusions

Overall, we demonstrate our antidepressant response algorithm can be deployed across multiple EHR systems to increase sample size of genetic and epidemiologic studies of antidepressant response.
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