Beyond Cipriani and Monoamines: Are We Improving Depression Treatment Outcomes?
Presenting Author(s): Dr. Sidney H. Kennedy, MD, FRCPC, FCAHS
Date and time: 21 Mar 2019 from 18:30 to 20:00
Location: Wildrose Salon C
Objectives
- Be familiar with key findings from meta-analyses on antidepressant efficacy and acceptability;
- Recognize limitations of current diagnostic classification and alternative approaches;
- Appreciate progress with novel pharmacological and device-related therapies for depression.
Literature References
- Cipriani A, Furukawa TA, Salanti G et al. (2018) Comparative efficacy and acceptability of 21 antidepressant drugs for the acute
treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet. 391(10128): 1357-1366. - Kennedy SH, Lam RW, Rotzinger S, et al. on behalf of the CAN-BIND Investigator Team. (2019) Symptomatic and Functional
Outcomes and Early Prediction of Response to Escitalopram Monotherapy and Sequential Adjunctive Aripiprazole Therapy in Patients
with Major Depressive Disorder: A CAN-BIND-1 Report. J Clin Psychiatry. - Henter ID, de Souza RT, Zarate CA Jr. (2018) Glutamatergic modulators in depression. Harv Rev Psychiatry. 26(6): 307-319.
Abstract
In 2018, Cipriani and colleagues published the largest ever multiple treatments meta-analysis of antidepressant drugs, confirming that
all 21 evaluated antidepressants were superior in efficacy to placebo. This study also provides valuable information about the balance
between efficacy and acceptability among individual antidepressants. However, this review is limited by the short-term nature of the
trials, lack of data on functional outcome and the inability to address heterogeneity among individual depressed patients. A
complementary approach to improve outcomes is being pursued by a number of networks, involving a more granular approach to data
collection with molecular, physiological and neuroimaging data as well as clinical domains. This presentation will provide an update on
both approaches, with a focus on the Canadian Biomarker Integration Network in Depression (CAN-BIND), and consider how large
meta-analytic and biomarker datasets can be integrated.