J E J Buckman1,2, Z D Cohen3, C O'Driscoll1, E I Fried4, R Saunders1, G Ambler5, R J DeRubeis6, S Gilbody7, S D Hollon8, T Kendrick9, E Watkins10, T C Eley11, A J Peel11, C Rayner11, D Kessler12, N Wiles13, G Lewis14, S Pilling1,15. 1. Research Department of Clinical, Educational & Health Psychology, Centre for Outcomes Research and Effectiveness (CORE), University College London, 1-19 Torrington Place, London, UK. 2. iCope - Camden & Islington Psychological Therapies Services - Camden & Islington NHS Foundation Trust, St Pancras Hospital, London, UK. 3. Department of Psychiatry, University of California, Los Angeles, Los Angeles, CA, USA. 4. Department of Clinical Psychology, Leiden University, Leiden, The Netherlands. 5. Statistical Science, University College London, 1-19 Torrington Place, London, UK. 6. Department of Psychology, School of Arts and Sciences, 425 S. University Avenue, PhiladelphiaPA, USA. 7. Department of Health Sciences, University of York, Seebohm Rowntree Building, Heslington, York, UK. 8. Department of Psychology, Vanderbilt University, Nashville, TN, USA. 9. Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Aldermoor Health Centre, Southampton, UK. 10. Department of Psychology, University of Exeter, Sir Henry Wellcome Building for Mood Disorders Research, Perry Road, Exeter, UK. 11. Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK. 12. Centre for Academic Primary Care, Population Health Sciences, Bristol Medical School, University of Bristol, Canynge Hall, Bristol, UK. 13. Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Bristol, UK. 14. Division of Psychiatry, University College London, Maple House, London, UK. 15. Camden & Islington NHS Foundation Trust, St Pancras Hospital, London, UK.
Abstract
BACKGROUND: This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data. METHODS: Individual patient data from all six eligible randomised controlled trials were used to develop (k = 3, n = 1722) and test (k = 3, n = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1-3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3-4 months. RESULTS: Models 1-7 all outperformed the null model and model 8. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum scores had little impact. CONCLUSIONS: Any of the modelling techniques (models 1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.
BACKGROUND: This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data. METHODS: Individual patient data from all six eligible randomised controlled trials were used to develop (k = 3, n = 1722) and test (k = 3, n = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1-3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3-4 months. RESULTS: Models 1-7 all outperformed the null model and model 8. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum scores had little impact. CONCLUSIONS: Any of the modelling techniques (models 1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.
Entities:
Keywords:
Depressive symptoms; major depression; network analysis; prediction modelling; prognosis
Authors: Michael W Otto; Jeffrey L Birk; Hayley E Fitzgerald; Gregory V Chauvin; Alexandra K Gold; Jenna R Carl Journal: Clin Psychol Rev Date: 2022-05-23
Authors: Carlijn J M Wibbelink; Arnoud Arntz; Raoul P P P Grasman; Roland Sinnaeve; Michiel Boog; Odile M C Bremer; Eliane C P Dek; Sevinç Göral Alkan; Chrissy James; Annemieke M Koppeschaar; Linda Kramer; Maria Ploegmakers; Arita Schaling; Faye I Smits; Jan H Kamphuis Journal: BMC Psychiatry Date: 2022-02-05 Impact factor: 3.630
Authors: Joshua E J Buckman; Rob Saunders; Joshua Stott; Zachary D Cohen; Laura-Louise Arundell; Thalia C Eley; Steven D Hollon; Tony Kendrick; Gareth Ambler; Edward Watkins; Simon Gilbody; David Kessler; Nicola Wiles; David Richards; Sally Brabyn; Elizabeth Littlewood; Robert J DeRubeis; Glyn Lewis; Stephen Pilling Journal: JAMA Psychiatry Date: 2022-05-01 Impact factor: 21.596