000 | 02854 am a22003253u 4500 | ||
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042 | _adc | ||
100 | 1 | 0 |
_aCohen, Zachary D. _eauthor _92496 |
700 | 1 | 0 |
_aDeRubeis, Robert J. _eauthor _92497 |
700 | 1 | 0 |
_aHayes, Rachel _eauthor _92498 |
700 | 1 | 0 |
_aWatkins, Edward R. _eauthor _92499 |
700 | 1 | 0 |
_aLewis, Glyn _eauthor _92500 |
700 | 1 | 0 |
_aByng, Richard _eauthor _92501 |
700 | 1 | 0 |
_aByford, Sarah _eauthor _92502 |
700 | 1 | 0 |
_aCrane, Catherine _eauthor _92503 |
700 | 1 | 0 |
_aKuyken, Willem _eauthor _92504 |
700 | 1 | 0 |
_aDalgleish, Tim _eauthor _91970 |
700 | 1 | 0 |
_aSchweizer, Susanne _eauthor _91969 |
245 | 0 | 0 | _aThe Development and Internal Evaluation of a Predictive Model to Identify for Whom Mindfulness-Based Cognitive Therapy Offers Superior Relapse Prevention for Recurrent Depression Versus Maintenance Antidepressant Medication |
260 |
_bSAGE Publications, _c2022-04-29. |
||
500 | _a/pmc/articles/PMC7614103/ | ||
500 | _a/pubmed/36698442 | ||
520 | _aDepression is highly recurrent, even following successful pharmacological and/or psychological intervention. We aimed to develop clinical prediction models to inform adults with recurrent depression choosing between antidepressant medication (ADM) maintenance or switching to mindfulness-based cognitive therapy (MBCT). Using previously published data (N = 424), we constructed prognostic models using elastic-net regression that combined demographic, clinical, and psychological factors to predict relapse at 24 months under ADM or MBCT. Only the ADM model (discrimination performance: area under the curve [AUC] = .68) predicted relapse better than baseline depression severity (AUC = .54; one-tailed DeLong's test: z = 2.8, p = .003). Individuals with the poorest ADM prognoses who switched to MBCT had better outcomes compared with individuals who maintained ADM (48% vs. 70% relapse, respectively; superior survival times, z = −2.7, p = .008). For individuals with moderate to good ADM prognoses, both treatments resulted in similar likelihood of relapse. If replicated, the results suggest that predictive modeling can inform clinical decision-making around relapse prevention in recurrent depression. | ||
540 | _a© The Author(s) 2022 | ||
540 | _ahttps://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). | ||
546 | _aen | ||
690 |
_aEmpirical Articles _92505 |
||
655 | 7 |
_aText _2local |
|
786 | 0 | _nClin Psychol Sci | |
856 | 4 | 1 |
_uhttp://dx.doi.org/10.1177/21677026221076832 _zConnect to this object online. |
999 |
_c1993 _d1993 |