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

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;&nbs...

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Autores principales:	South C, Rush AJ, Carmody TJ, Jha MK, Trivedi MH
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2017
Materias:	Patient selection depression elastic net analytic approach treatment response Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/3db3cee9409f49bbb0858d50a42f598a
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spelling	oai:doaj.org-article:3db3cee9409f49bbb0858d50a42f598a2021-12-02T00:36:59ZAccurately identifying patients who are excellent candidates or unsuitable for a medication: a novel approach1178-2021https://doaj.org/article/3db3cee9409f49bbb0858d50a42f598a2017-12-01T00:00:00Zhttps://www.dovepress.com/accurately-identifying-patients-who-are-excellent-candidates-or-unsuit-peer-reviewed-article-NDThttps://doaj.org/toc/1178-2021Charles 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, USAThese 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 responseSouth CRush AJCarmody TJJha MKTrivedi MHDove Medical PressarticlePatient selectiondepressionelastic netanalytic approachtreatment responseNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENNeuropsychiatric Disease and Treatment, Vol Volume 13, Pp 3001-3010 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Patient selection depression elastic net analytic approach treatment response Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
spellingShingle	Patient selection depression elastic net analytic approach treatment response Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429 South C Rush AJ Carmody TJ Jha MK Trivedi MH Accurately identifying patients who are excellent candidates or unsuitable for a medication: a novel approach
description	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, USAThese 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
format	article
author	South C Rush AJ Carmody TJ Jha MK Trivedi MH
author_facet	South C Rush AJ Carmody TJ Jha MK Trivedi MH
author_sort	South C
title	Accurately identifying patients who are excellent candidates or unsuitable for a medication: a novel approach
title_short	Accurately identifying patients who are excellent candidates or unsuitable for a medication: a novel approach
title_full	Accurately identifying patients who are excellent candidates or unsuitable for a medication: a novel approach
title_fullStr	Accurately identifying patients who are excellent candidates or unsuitable for a medication: a novel approach
title_full_unstemmed	Accurately identifying patients who are excellent candidates or unsuitable for a medication: a novel approach
title_sort	accurately identifying patients who are excellent candidates or unsuitable for a medication: a novel approach
publisher	Dove Medical Press
publishDate	2017
url	https://doaj.org/article/3db3cee9409f49bbb0858d50a42f598a
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Accurately identifying patients who are excellent candidates or unsuitable for a medication: a novel approach

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