{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T17:26:16Z","timestamp":1788715576388,"version":"build-2803163510"},"reference-count":53,"publisher":"Cambridge University Press (CUP)","issue":"16","license":[{"start":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T00:00:00Z","timestamp":1734307200000},"content-version":"unspecified","delay-in-days":15,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Psychol. Med."],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec id=\"S0033291724001582_sec_a1\"><jats:title>Background<\/jats:title><jats:p>England's primary care service for psychological therapy (Improving Access to Psychological Therapies [IAPT]) treats anxiety and depression, with a target recovery rate of 50%. Identifying the characteristics of patients who achieve recovery may assist in optimizing future treatment. This naturalistic cohort study investigated pre-therapy characteristics as predictors of recovery and improvement after IAPT therapy.<\/jats:p><\/jats:sec><jats:sec id=\"S0033291724001582_sec_a2\" sec-type=\"methods\"><jats:title>Methods<\/jats:title><jats:p>In a cohort of patients attending an IAPT service in South London, we recruited 263 participants and conducted a baseline interview to gather extensive pre-therapy characteristics. Bayesian prediction models and variable selection were used to identify baseline variables prognostic of good clinical outcomes. Recovery (primary outcome) was defined using (IAPT) service-defined score thresholds for both depression (Patient Health Questionnaire [PHQ-9]) and anxiety (Generalized Anxiety Disorder [GAD-7]). Depression and anxiety outcomes were also evaluated as standalone (PHQ-9\/GAD-7) scores after therapy. Prediction model performance metrics were estimated using cross-validation.<\/jats:p><\/jats:sec><jats:sec id=\"S0033291724001582_sec_a3\" sec-type=\"results\"><jats:title>Results<\/jats:title><jats:p>Predictor variables explained 26% (recovery), 37% (depression), and 31% (anxiety) of the variance in outcomes, respectively. Variables prognostic of recovery were lower pre-treatment depression severity and not meeting criteria for obsessive compulsive disorder. Post-therapy depression and anxiety severity scores were predicted by lower symptom severity and higher ratings of health-related quality of life (EuroQol questionnaire [EQ5D]) at baseline.<\/jats:p><\/jats:sec><jats:sec id=\"S0033291724001582_sec_a4\" sec-type=\"conclusions\"><jats:title>Conclusion<\/jats:title><jats:p>Almost a third of the variance in clinical outcomes was explained by pre-treatment symptom severity scores. These constructs benefit from being rapidly accessible in healthcare services. If replicated in external samples, the early identification of patients who are less likely to recover may facilitate earlier triage to alternative interventions.<\/jats:p><\/jats:sec>","DOI":"10.1017\/s0033291724001582","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T12:47:21Z","timestamp":1734353241000},"page":"4503-4517","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":3,"title":["Predictors of outcome following psychological therapy for depression and anxiety in an urban primary care service: a naturalistic Bayesian prediction modeling 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