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Pregled bibliografske jedinice broj: 887213

Combining clinical variables to optimize prediction of antidepressant treatment outcomes


Iniesta, R.; Malki, K.; Maier, W.; Rietschel, M.; Mors, O.; Hauser, J.; Henigsberg, Neven; Dernovsek, M.Z.; Souery, D.; Stahl, D. et al.
Combining clinical variables to optimize prediction of antidepressant treatment outcomes // Journal of psychiatric research, 78 (2016), 94-102 doi:10.1016/j.jpsychires.2016.03.016 (međunarodna recenzija, članak, znanstveni)


Naslov
Combining clinical variables to optimize prediction of antidepressant treatment outcomes

Autori
Iniesta, R. ; Malki, K. ; Maier, W. ; Rietschel, M. ; Mors, O. ; Hauser, J. ; Henigsberg, Neven ; Dernovsek, M.Z. ; Souery, D. ; Stahl, D. ; Dobson, R. ; Aitchison, K.J. ; Farmer, A. ; Lewis, C.M. ; McGuffin, P. ; Uher, R.

Izvornik
Journal of psychiatric research (0022-3956) 78 (2016); 94-102

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Antidepressant ; Depression ; Machine learning ; Outcome ; Prediction ; Statistical learning

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Kliničke medicinske znanosti



POVEZANOST RADA


Ustanove
Medicinski fakultet, Zagreb

Autor s matičnim brojem:
Neven Henigsberg, (200094)

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • Social Science Citation Index (SSCI)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


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