Iniesta, R;
Malki, K;
Maier, W;
Rietschel, M;
Mors, O;
Hauser, J;
Henigsberg, N;
... Uher, R; + view all
(2016)
Combining clinical variables to optimize prediction of antidepressant treatment outcomes.
Journal of Psychiatric Research
, 78
pp. 94-102.
10.1016/j.jpsychires.2016.03.016.
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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.
| Type: | Article |
|---|---|
| Title: | Combining clinical variables to optimize prediction of antidepressant treatment outcomes |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.jpsychires.2016.03.016 |
| Publisher version: | http://doi.org/10.1016/j.jpsychires.2016.03.016 |
| Language: | English |
| Additional information: | © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the Creative Commons Attribution Non-commercial Non-derivative 4.0 International license (CC BY-NC-ND 4.0). This license allows you to share, copy, distribute and transmit the work for personal and non-commercial use providing author and publisher attribution is clearly stated. Further details about CC BY licenses are available at http://creativecommons.org/ licenses/by/4.0. |
| Keywords: | Antidepressant, Depression, Machine learning, Outcome, Prediction, Statistical learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1503912 |
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