Combining clinical variables to optimize prediction of antidepressant treatment outcomes

Abstract

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.

Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry

“…Supervised machine learning approaches are often suggested as a powerful tool in medicine. Several methods in machine learning use a multivariate approach to the entire dataset and are able to handle interactions [60,72].…”

Clinical Characteristics and Psychotropic Prescribing Patterns Associated with impaired Concentration in Asians with Depressive Disorders: The REAP-AD Study

“…Similar findings have been reported in 3,637 MDD adults treated with citalopram in Sequenced Treatment Alternatives to Relieve Depression (STAR*D) (Ulher et al 2012). In addition, indecisiveness, guilt, ideas of reference, depressed mood, insomnia and other symptom dimension were proposed as predictors of depression that is resistant to escitalopram and nortriptyline in 793 adults with MDD (Iniesta et al 2016). Hence, we suggest that impaired concentration and its depressive symptom correlates can be regarded as a unitary depressive symptom cluster that is associated with poor treatment response as well as being an intervening variable for poor psychosocial functioning.…”

Machine learning in mental health: A systematic scoping review of methods and applications

“…Conditions that researchers have focused on include schizophrenia [197], Alzheimer's disease [198][199][200], posttraumatic stress disorder [201], depression [202][203][204][205], and psychosis [206][207][208]. For example, ML was demonstrated to identify treatment responders and non-responders to a drug for Parkinson's disease, subsequently leading to improved treatment outcomes [209].…”