CHARACTERISTICS OF ANTIDEPRESSANT CLASSES FOR TREATMENT- RESISTANT DEPRESSION AS DEFINED BY THE NUMBER OF VALID PHARMACOTHERAPIES

Abstract Background Treatment-resistant depression (TRD) is conventionally defined as depression that does not remit with two or more adequate doses of antidepressants of adequate duration (Thase, 1997). Although we have suggested that 3 or more valid pharmacotherapies may include TRD with a high probability, little is known about the characteristics of antidepressants preferred for TRD. Aims & Objectives The purpose of this study was to characterize antidepressant classes for TRD by comparing prescriptions for TRD and non-treatment-resistant depression (non-TRD), classified by the number of valid pharmacotherapies. The study was approved by the Ethics Committee of the Jikei University School of Medicine. Method A longitudinal prescription data set of 494 patients who visited the Department of Psychiatry, The Jikei University Hospital between January 1, 2018 and December 31, 2021 was analyzed. The patients diagnosed with ICD-10 codes F32 (depressive episode) and F33 (recurrent depressive disorder) and treated with antidepressants for 30 days or more were extracted.Patients treated with 3 or more valid pharmacotherapies were included in the TRD group (58 patients) and those treated with 2 or less pharmacotherapies were included in the non-TRD group (436 patients).The presence or absence of antidepressant prescriptions for each group at the last visit during the period was compared using the chi-square test for each class. The significance level was set at 5%. Results The TRD group had significantly higher prescription rates for tricyclic antidepressants and serotonin- noradrenaline reuptake inhibitors (p=3.73×10-5 and 3.22×10-5, respectively), while the non-TRD group had significantly higher prescription rates for selective serotonin reuptake inhibitors (p=1.28×10-2). Prescription rates for noradrenergic and specific serotonergic antidepressants and tetracyclic antidepressants were not significantly different between the two groups (p= 6.14×10-1 and 1.61×10-1, respectively). Discussion & Conclusion It was suggested that in the real-world setting, psychiatrists choose antidepressant classes based on tolerability. References 1. Thase, M. E. and Rush A. J. (1997), “When at first you don't succeed: sequential strategies for antidepressant nonresponders”, J Clin Psychiatry 58: 23–29.

Authors

Institutions

Publication Details

Journal
The International Journal of Neuropsychopharmacology
Published
2025-02-01
DOI
https://doi.org/10.1093/ijnp/pyae059.571
Primary Topic
Treatment of Major Depression
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

CHARACTERISTICS OF ANTIDEPRESSANT CLASSES FOR TREATMENT- RESISTANT DEPRESSION AS DEFINED BY THE NUMBER OF VALID PHARMACOTHERAPIES

Yoshiyuki Sakai, Fumitoshi Kodaka, Tadashi Kobayashi, Masahiro Shigeta et al.
The International Journal of Neuropsychopharmacology
Treatment of Major Depression
article

CHARACTERISTICS OF ANTIDEPRESSANT CLASSES FOR TREATMENT- RESISTANT DEPRESSION AS DEFINED BY THE NUMBER OF VALID PHARMACOTHERAPIES

Yoshiyuki Sakai, Fumitoshi Kodaka, Tadashi Kobayashi, Masahiro Shigeta, Hiroshi Kameyama, Junpei Ishii, Chihiro Saeki, Keisuke Mori
article en

Abstract

Abstract Background Treatment-resistant depression (TRD) is conventionally defined as depression that does not remit with two or more adequate doses of antidepressants of adequate duration (Thase, 1997). Although we have suggested that 3 or more valid pharmacotherapies may include TRD with a high probability, little is known about the characteristics of antidepressants preferred for TRD. Aims & Objectives The purpose of this study was to characterize antidepressant classes for TRD by comparing prescriptions for TRD and non-treatment-resistant depression (non-TRD), classified by the number of valid pharmacotherapies. The study was approved by the Ethics Committee of the Jikei University School of Medicine. Method A longitudinal prescription data set of 494 patients who visited the Department of Psychiatry, The Jikei University Hospital between January 1, 2018 and December 31, 2021 was analyzed. The patients diagnosed with ICD-10 codes F32 (depressive episode) and F33 (recurrent depressive disorder) and treated with antidepressants for 30 days or more were extracted.Patients treated with 3 or more valid pharmacotherapies were included in the TRD group (58 patients) and those treated with 2 or less pharmacotherapies were included in the non-TRD group (436 patients).The presence or absence of antidepressant prescriptions for each group at the last visit during the period was compared using the chi-square test for each class. The significance level was set at 5%. Results The TRD group had significantly higher prescription rates for tricyclic antidepressants and serotonin- noradrenaline reuptake inhibitors (p=3.73×10-5 and 3.22×10-5, respectively), while the non-TRD group had significantly higher prescription rates for selective serotonin reuptake inhibitors (p=1.28×10-2). Prescription rates for noradrenergic and specific serotonergic antidepressants and tetracyclic antidepressants were not significantly different between the two groups (p= 6.14×10-1 and 1.61×10-1, respectively). Discussion & Conclusion It was suggested that in the real-world setting, psychiatrists choose antidepressant classes based on tolerability. References 1. Thase, M. E. and Rush A. J. (1997), “When at first you don't succeed: sequential strategies for antidepressant nonresponders”, J Clin Psychiatry 58: 23–29.

The International Journal of NeuropsychopharmacologyVol. 28(Supplement_1)
Jikei University School of Medicine (JP)
Openalex Percentile: Top 97%
Treatment of Major Depression
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.