Article Abstract

Online ISSN: 1099-176X    Print ISSN: 1091-4358
The Journal of Mental Health Policy and Economics
Volume 27, Issue 1, 2024. Pages: 3-12
Published Online: 1 March 2024

Copyright © 2024 ICMPE.

Effectiveness of Antidepressants in Combination with Psychotherapy

Farrokh Alemi,¹* Tulay G. Soylu,² Mary Cannon,³ Conor McCandless⁴

¹PhD, Department of Health Administration and Policy, George Mason University, Fairfax, VA, USA.
²PhD, MHA, MBA, Department of Health Services Administration and Policy, College of Public Health, Temple University, Philadelphia, PA, USA.
³MD, PhD, Department of Psychiatry, RCSI University of Medicine and Health Sciences. Dublin 2, Ireland.
⁴MB BCh BAO, Department of Psychiatry, St. Patrick’s University Hospital, Dublin 8, Ireland.

This retrospective, observational, pragmatic, cohort study analyzed a large insurance database of 3,678,082 patients to provide an empirically derived guideline for prescribing 14 most common oral antidepressant medications for patients with major depression who are in psychotherapy. The study controlled for selection bias and confounding in observational studies through repeated use of LASSO regressions. We organized the data into 16,770 subgroups of patients with at least 100 cases in each subgroup. Across the population studied, the average remission rate did not differ much. Within subgroups, antidepressants had large, and statistically significant, differences in remission rates. No medication was best for all subgroups. Clinicians can use online tools provided by the study to match patients to one of the subgroups and identify the optimal antidepressant for the patient.

Background: Consensus-guidelines for prescribing antidepressants recommend that clinicians should be vigilant to match antidepressants to patient’s medical history but provide no specific advice on which antidepressant is best for a given medical history.

Aims of the Study: For patients with major depression who are in psychotherapy, this study provides an empirically derived guideline for prescribing antidepressant medications that fit patients’ medical history.

Methods: This retrospective, observational, cohort study analyzed a large insurance database of 3,678,082 patients. Data was obtained from healthcare providers in the U.S. between January 1, 2001, and December 31, 2018. These patients had 10,221,145 episodes of antidepressant treatments. This study reports the remission rates for the 14 most commonly prescribed single antidepressants (amitriptyline, bupropion, citalopram, desvenlafaxine, doxepin, duloxetine, escitalopram, fluoxetine, mirtazapine, nortriptyline, paroxetine, sertraline, trazodone, and venlafaxine) and a category named “Other” (other antidepressants/combination of antidepressants). The study used robust LASSO regressions to identify factors that affected remission rate and clinicians’ selection of antidepressants. The selection bias in observational data was removed through stratification. We organized the data into 16,770 subgroups, of at least 100 cases, using the combination of the largest factors that affected remission and selection bias. This paper reports on 2,467 subgroups of patients who had received psychotherapy.

Results: We found large, and statistically significant, differences in remission rates within subgroups of patients. Remission rates for sertraline ranged from 4.5% to 77.86%, for fluoxetine from 2.86% to 77.78%, for venlafaxine from 5.07% to 76.44%, for bupropion from 0.5% to 64.63%, for desvenlafaxine from 1.59% to 75%, for duloxetine from 3.77% to 75%, for paroxetine from 6.48% to 68.79%, for escitalopram from 1.85% to 65%, and for citalopram from 4.67% to 76.23%. Clearly these medications are ideal for patients in some subgroups but not others. If patients are matched to the subgroups, clinicians can prescribe the medication that works best in the subgroup. Some medications (amitriptyline, doxepin, nortriptyline, and trazodone) always had remission rates below 11% and therefore were not suitable as single antidepressant therapy for any of the subgroups.

Discussions: This study provides an opportunity for clinicians to identify an optimal antidepressant for their patients, before they engage in repeated trials of antidepressants.

Implications for Health Care Provision and Use: To facilitate the matching of patients to the most effective antidepressants, this study provides access to a free, non-commercial, decision aid at http://MeAgainMeds.com.

Implications for Health Policies: Policymakers should evaluate how study findings can be made available through fragmented electronic health records at point-of-care. Alternatively, policymakers can put in place an AI system that recommends antidepressants to patients online, at home, and encourages them to bring the recommendation to their clinicians at their next visit.

Implications for Further Research: Future research could investigate (i) the effectiveness of our recommendations in changing clinical practice, (ii) increasing remission of depression symptoms, and (iii) reducing cost of care. These studies need to be prospective but pragmatic. It is unlikely random clinical trials can address the large number of factors that affect remission.