{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T11:01:37Z","timestamp":1784804497502,"version":"3.55.0"},"reference-count":50,"publisher":"Cambridge University Press (CUP)","issue":"8","license":[{"start":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T00:00:00Z","timestamp":1641772800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Psychol. Med."],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec id=\"S0033291721005390_sec_a1\"><jats:title>Background<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec id=\"S0033291721005390_sec_a2\" sec-type=\"methods\"><jats:title>Methods<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec id=\"S0033291721005390_sec_a3\" sec-type=\"results\"><jats:title>Results<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec id=\"S0033291721005390_sec_a4\" sec-type=\"conclusions\"><jats:title>Conclusions<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1017\/s0033291721005390","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T09:06:23Z","timestamp":1641805583000},"page":"3366-3376","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":11,"title":["Predictors of pharmacotherapy outcomes for body dysmorphic disorder: a machine learning approach"],"prefix":"10.1017","volume":"53","author":[{"given":"Joshua E.","family":"Curtiss","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily 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