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Accurately identifying patients who are excellent candidates or unsuitable for a medication: a novel approach

Authors South C, Rush AJ, Carmody TJ, Jha MK, Trivedi MH

Received 13 April 2017

Accepted for publication 31 August 2017

Published 15 December 2017 Volume 2017:13 Pages 3001—3010


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Roger Pinder

Charles South,1–3 A John Rush,4,* Thomas J Carmody,1–3 Manish K Jha,1,2 Madhukar H Trivedi1,2,*

1Center for Depression Research and Clinical Care, 2Department of Psychiatry, 3Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; 4Department of Psychiatry and Behavioral Sciences, Duke-National University of Singapore, Singapore; Duke Medical School, Durham, NC, USA

*These authors contributed equally to this work

Objective: The objective of the study was to determine whether a unique analytic approach – as a proof of concept – could identify individual depressed outpatients (using 30 baseline clinical and demographic variables) who are very likely (75% certain) to not benefit (NB) or to remit (R), accepting that without sufficient certainty, no prediction (NP) would be made.
Methods: Patients from the Combining Medications to Enhance Depression Outcomes trial treated with escitalopram (S-CIT) + placebo (n=212) or S-CIT + bupropion-SR (n=206) were analyzed separately to assess replicability. For each treatment, the elastic net was used to identify subsets of predictive baseline measures for R and NB, separately. Two different equations that estimate the likelihood of remission and no benefit were developed for each patient. The ratio of these two numbers characterized likely outcomes for each patient.
Results: The two treatment cells had comparable rates of remission (40%) and no benefit (22%). In S-CIT + bupropion-SR, 11 were predicted NB of which 82% were correct; 26 were predicted R – 85% correct (169 had NP). For S-CIT + placebo, 13 were predicted NB – 69% correct; 44 were predicted R – 75% correct (155 were NP). Overall, 94/418 (22%) patients were identified with a meaningful degree of certainty (69%–85% correct). Different variable sets with some overlap were predictive of remission and no benefit within and across treatments, despite comparable outcomes.
Conclusion: In two separate analyses with two different treatments, this analytic approach – which is also applicable to pretreatment laboratory tests – identified a meaningful proportion (over 20%) of depressed patients for whom a treatment outcome was predicted with sufficient certainty that the clinician can elect to strongly recommend for or choose to avoid a particular treatment. Different persons seem to be remitting or not benefiting with these two different treatments.

Keywords: patient selection, depression, elastic net, analytic approach, treatment response

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