Combining clinical variables to optimize prediction of antidepressant treatment outcomes

Scientific paper - Original scientific paper

• Author: Raquel Iniesta
• Author: Karim Malki
• Author: Wolfgang Maier
• Author: Marcella Rietschel
• Author: Ole Mors
• Author: Joanna Hauser
• Author: Neven Henigsberg
• Author: Mojca Zvezdana Dernovsek
• Author: Daniel Souery
• Author: Daniel Stahl
• Author: Richard Dobson
• Author: Katherine J. Aitchison
• Author: Anne Farmer
• Author: Cathryn M. Lewis
• Author: Peter McGuffin
• Author: Rudolf Uher
• Author's institution: University of Zagreb; School of Medicine
• Scientific / art field, discipline and subdiscipline: BIOMEDICINE AND HEALTHCARE; Basic Medical Sciences

Abstract (english)

The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug.

• Keywords (english): Age Factors; Antidepressive Agents/therapeutic use; Area Under Curve; Body Mass Index; Citalopram/therapeutic use; Depressive Disorder, Major/diagnosis; Depressive Disorder, Major/drug therapy; Depressive Disorder, Major/epidemiology; Depressive Disorder, Major/physiopathology; Female; Humans; Machine Learning; Male; Nortriptyline/therapeutic use; Personality; Precision Medicine; Prognosis; Psychiatric Status Rating Scales; Regression Analysis; Treatment Outcome
• Language: english
• Publication type: Scientific paper - Original scientific paper
• Publication status: Published
• Peer review: Peer review
• Publication version: Published version
• Journal title: Journal of Psychiatric Research
• Numbering: vol. 78, pp. 94-102
• p-ISSN: 0022-3956
• DOI: https://doi.org/10.1016/j.jpsychires.2016.03.016
• URN:NBN: urn:nbn:hr:105:431320
• Publication: 2016-07
• Document URL: https://doi.org/10.1016/j.jpsychires.2016.03.016
• Type of resource: Text
• Access conditions: Open access
• Created on: 2024-03-28 08:48:12