Title:Choice of Treatment with Antidepressants: Influencing Factors
VOLUME: 18 ISSUE: 36
Author(s):Hubertus Himmerich and Dominika W. Wranik
Affiliation:Department of Psychiatry and Psychotherapy, University of Leipzig, Leipzig, Germany.
Keywords:Antidepressants, decision making, pharmacoeconomics, treatment algorithm, serotonin reuptake inhibitors, tricyclic antidepressants, care, clinicians, depression, drug
Abstract:Depressive disorders place a large burden on patients and on society. Although efficacious treatment options for unipolar depressive
disorders exist, substantial gaps in care remain. In part, the challenge lies in the matching of individual patients with appropriate
care. This is complicated by the steady increases in the variety of antidepressants available in the market. The goal of this study is to
highlight the decision processes in the selection of antidepressants by clinicians, given that most treatments have similar clinical effectiveness
profiles.
We conducted a systematic literature review of studies that referred to the decisions surrounding treatment with antidepressants for the
treatment of non-psychotic unipolar depression. Our analysis of the literature reveals that the choice of treatment is based on a variety of
factors, of which clinical evidence is only one. These factors can be categorized into clinical factors such as illness and treatment characteristics,
individual factors such as patient and physician characteristics, and contextual factors such as setting characteristics, decision
supports and pharmacoeconomic aspects.
Illness characteristics are defined by the type and severity of depression. Treatment characteristics include drug properties, efficacy, effectiveness
and favorable as well as unintended adverse effects of the drug. Examples for patient characteristics are co-morbidities and
individual preferences, and physician characteristics include knowledge, experience, values and beliefs, and the relationship with the patient.
Treatment guidelines, algorithms, and most recently, computational supports and biological markers serve as decision supports.
