Predictors of pharmacotherapy outcomes for body dysmorphic disorder: a machine learning approach.

Autor:	Curtiss JE; Massachusetts General Hospital, Boston, MA, USA.; Harvard Medical School, Boston, MA, USA., Bernstein EE; Massachusetts General Hospital, Boston, MA, USA.; Harvard Medical School, Boston, MA, USA., Wilhelm S; Massachusetts General Hospital, Boston, MA, USA.; Harvard Medical School, Boston, MA, USA., Phillips KA; Rhode Island Hospital, Butler Hospital, and Alpert Medical School of Brown University, Providence, RI, USA.; New York-Presbyterian Hospital and Weill Cornell Medical College, New York, NY, USA.
Jazyk:	angličtina
Zdroj:	Psychological medicine [Psychol Med] 2023 Jun; Vol. 53 (8), pp. 3366-3376. Date of Electronic Publication: 2022 Jan 10.
DOI:	10.1017/S0033291721005390
Abstrakt:	Background: Serotonin-reuptake inhibitors (SRIs) are first-line pharmacotherapy for the treatment of body dysmorphic disorder (BDD), a common and severe disorder. However, prior research has not focused on or identified definitive predictors of SRI treatment outcomes. Leveraging precision medicine techniques such as machine learning can facilitate the prediction of treatment outcomes. Methods: The study used 10-fold cross-validation support vector machine (SVM) learning models to predict three treatment outcomes (i.e. response, partial remission, and full remission) for 97 patients with BDD receiving up to 14-weeks of open-label treatment with the SRI escitalopram. SVM models used baseline clinical and demographic variables as predictors. Feature importance analyses complemented traditional SVM modeling to identify which variables most successfully predicted treatment response. Results: SVM models indicated acceptable classification performance for predicting treatment response with an area under the curve (AUC) of 0.77 (sensitivity = 0.77 and specificity = 0.63), partial remission with an AUC of 0.75 (sensitivity = 0.67 and specificity = 0.73), and full remission with an AUC of 0.79 (sensitivity = 0.70 and specificity = 0.79). Feature importance analyses supported constructs such as better quality of life and less severe depression, general psychopathology symptoms, and hopelessness as more predictive of better treatment outcome; demographic variables were least predictive. Conclusions: The current study is the first to demonstrate that machine learning algorithms can successfully predict treatment outcomes for pharmacotherapy for BDD. Consistent with precision medicine initiatives in psychiatry, the current study provides a foundation for personalized pharmacotherapy strategies for patients with BDD.
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu