{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T01:19:44Z","timestamp":1787620784257,"version":"build-2736575974"},"reference-count":44,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":42,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clin Psychology and Psychoth"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Dropout from psychotherapy for major depressive disorder (MDD) undermines treatment effectiveness for both patients and clinicians. Early identification of individuals at risk of discontinuation is therefore essential. Machine learning methods, particularly those incorporating resampling techniques to address imbalanced clinical data, may offer improved predictive capacity. This study evaluates several machine\u2010learning approaches to determine their potential in identifying patients at elevated risk of dropout.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Data were drawn from a randomised controlled trial of outpatients with MDD (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009290) receiving short\u2010term psychodynamic supportive psychotherapy (SPSP) or cognitive behavioural therapy (CBT). Candidate predictors were restricted to variables available in both the development and external validation datasets, resulting in a limited number of overlapping predictors. Multiple machine learning models and resampling strategies were applied to predict dropout, and the best\u2010performing models were subsequently externally validated using an independent clinical dataset (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200996).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Models demonstrated low predictive performance overall. Resampling strategies influenced outcomes, with SMOTE consistently improving F1\u2010scores compared with no resampling. The combination of random forest with SMOTE achieved the highest F1\u2010score (0.303). Five key predictors of dropout emerged: younger age, unemployment, a higher number of comorbid Axis I disorders, absence of a partner, and moderate depression severity. All models showed reduced performance during external validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The models showed very limited predictive performance, which may in part reflect the restricted predictor set resulting from the requirement that variables be assessed in both trials. While clinical application is premature at this stage, the findings provide a valuable proof of concept and highlight key methodological challenges inherent in cross\u2010trial dropout prediction. Despite this, the identified predictors are routinely available in clinical practice, suggesting that future models, if developed with richer predictor sets and validated in larger samples, could ultimately support therapists in the early identification of patients at risk of dropout and in proactively addressing disengagement.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cpp.70320","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T05:13:33Z","timestamp":1786598013000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Dropout in Psychotherapy for Major Depressive Disorder: A Machine Learning Approach to Identifying At\u2010Risk Patients"],"prefix":"10.1002","volume":"33","author":[{"given":"Susanne","family":"Bremer\u2010Hoeve","sequence":"first","affiliation":[{"name":"Dimence Mental Health Group  Deventer the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suzanne C.","family":"van Bronswijk","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Neuropsychology, School for Mental health and Neuroscience, Faculty of Health Medicine and Life Sciences Maastricht University  Maastricht the Netherlands"},{"name":"Department of Psychiatry and Psychology Maastricht University Medical Center  Maastricht the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aartjan T. 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