Initial antidepressant choice by non-psychiatrists: Learning from large-scale electronic health records

Yi han Sheu, Colin Magdamo, Matthew Miller, Jordan W. Smoller, Deborah Blacker

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Objectives: Pharmacological treatment of depression mostly occurs in non-psychiatric settings, but the determinants of initial choice of antidepressant treatment in these settings are unclear. We investigate how non-psychiatrists choose among four antidepressant classes at first prescription (selective serotonin reuptake inhibitors [SSRI], bupropion, mirtazapine, or serotonin-norepinephrine reuptake inhibitors [SNRI]). Method: Using electronic health records (EHRs), we included adult patients at the time of first antidepressant prescription with a co-occurring diagnosis code for a depressive disorder. We selected 64 variables based on a literature search and expert consultation, constructed the variables from either structured codes or through applying natural language processing (NLP), and modeled antidepressant choice using multinomial logistic regression, using SSRI as the reference class. Results: With 47,528 patients, we observed significant associations for 36 of 64 variables. Many of these associations suggested antidepressants' known pharmacological properties/actions guided choice. For example, there was a decreased likelihood of bupropion prescription among patients with epilepsy (adjusted OR 0.49, 95%CI: 0.41–0.57, p < 0.001), and an increased likelihood of mirtazapine prescription among patients with insomnia (adjusted OR 1.59, 95%CI: 1.40–1.80, p < 0.001). Conclusions: Broadly speaking, non-psychiatrists' selection of antidepressant class appears to be at least in part guided by clinically relevant pharmacological considerations.

Original languageEnglish
Pages (from-to)22-31
Number of pages10
JournalGeneral Hospital Psychiatry
Volume81
DOIs
StatePublished - 1 Mar 2023
Externally publishedYes

Keywords

  • Antidepressant
  • Depression
  • Electronic health records
  • Natural language processing

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