Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies
Background Relapse of depression is common and contributes to the overall associated morbidity and burden. We lack evidence-based tools to estimate an individual’s risk of relapse after treatment in primary care, which may help us more effectively target relapse prevention.Objective The objective wa...
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| Format: | Article |
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BMJ Publishing Group
2024-06-01
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| Series: | BMJ Mental Health |
| Online Access: | https://mentalhealth.bmj.com/content/27/1/e301226.full |
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| author | Simon Gilbody Dean McMillan Bob Phillips Kym I E Snell Richard D Riley David A Richards Jaime Delgadillo Shehzad Ali Chris Salisbury Lewis W Paton Lucinda Archer Peter A Coventry Stephen Pilling Andrew S Moriarty Joshua E J Buckman Nick Meader Carolyn A Chew Graham |
| author_facet | Simon Gilbody Dean McMillan Bob Phillips Kym I E Snell Richard D Riley David A Richards Jaime Delgadillo Shehzad Ali Chris Salisbury Lewis W Paton Lucinda Archer Peter A Coventry Stephen Pilling Andrew S Moriarty Joshua E J Buckman Nick Meader Carolyn A Chew Graham |
| author_sort | Simon Gilbody |
| collection | DOAJ |
| description | Background Relapse of depression is common and contributes to the overall associated morbidity and burden. We lack evidence-based tools to estimate an individual’s risk of relapse after treatment in primary care, which may help us more effectively target relapse prevention.Objective The objective was to develop and validate a prognostic model to predict risk of relapse of depression in primary care.Methods Multilevel logistic regression models were developed, using individual participant data from seven primary care-based studies (n=1244), to predict relapse of depression. The model was internally validated using bootstrapping, and generalisability was explored using internal–external cross-validation.Findings Residual depressive symptoms (OR: 1.13 (95% CI: 1.07 to 1.20), p<0.001) and baseline depression severity (OR: 1.07 (1.04 to 1.11), p<0.001) were associated with relapse. The validated model had low discrimination (C-statistic 0.60 (0.55–0.65)) and miscalibration concerns (calibration slope 0.81 (0.31–1.31)). On secondary analysis, being in a relationship was associated with reduced risk of relapse (OR: 0.43 (0.28–0.67), p<0.001); this remained statistically significant after correction for multiple significance testing.Conclusions We could not predict risk of depression relapse with sufficient accuracy in primary care data, using routinely recorded measures. Relationship status warrants further research to explore its role as a prognostic factor for relapse.Clinical implications Until we can accurately stratify patients according to risk of relapse, a universal approach to relapse prevention may be most beneficial, either during acute-phase treatment or post remission. Where possible, this could be guided by the presence or absence of known prognostic factors (eg, residual depressive symptoms) and targeted towards these.Trial registration number NCT04666662. |
| format | Article |
| id | doaj-art-c4859bb2eb6f4002ae87fcb089d753f9 |
| institution | DOAJ |
| issn | 2755-9734 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Mental Health |
| spelling | doaj-art-c4859bb2eb6f4002ae87fcb089d753f92025-08-20T03:09:23ZengBMJ Publishing GroupBMJ Mental Health2755-97342024-06-0127110.1136/bmjment-2024-301226Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studiesSimon Gilbody0Dean McMillan1Bob Phillips2Kym I E Snell3Richard D Riley4David A Richards5Jaime Delgadillo6Shehzad Ali7Chris Salisbury8Lewis W Paton9Lucinda Archer10Peter A Coventry11Stephen Pilling12Andrew S Moriarty13Joshua E J Buckman14Nick Meader15Carolyn A Chew Graham16Hull York Medical School and Department of Health Sciences, University of York, York, UKHull York Medical School, Hull, UK1 Centre for Reviews and Dissemination, University of York Alcuin College, York, UKInstitute of Applied Health Research, University of Birmingham, Birmingham, UKCentre for Prognosis Research, Research Institute for Primary Care and Health Sciences, Keele University, Keele, UKFaculty of Health and Life Sciences, University of Exeter, Exeter, UKDepartment of Psychology, The University of Sheffield, Sheffield, UKEpidemiology and Biostatistics, University of Western Ontario, London, Ontario, CanadaCentre for Academic Primary Care, University of Bristol, Bristol, UKHull York Medical School and Department of Health Sciences, University of York, York, Yorkshire, UKassistant professorDepartment of Health Sciences, University of York, York, UKCentre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational and Health Psychology, University College London, London, UKHull York Medical School and Department of Health Sciences, University of York, York, Yorkshire, UKResearch Department of Clinical, Educational and Health Psychology, University College London, London, UKPopulation Health Sciences Institute, University of Newcastle upon Tyne, Newcastle upon Tyne, UKSchool of Medicine, Keele University, Keele, Staffordshire, UKBackground Relapse of depression is common and contributes to the overall associated morbidity and burden. We lack evidence-based tools to estimate an individual’s risk of relapse after treatment in primary care, which may help us more effectively target relapse prevention.Objective The objective was to develop and validate a prognostic model to predict risk of relapse of depression in primary care.Methods Multilevel logistic regression models were developed, using individual participant data from seven primary care-based studies (n=1244), to predict relapse of depression. The model was internally validated using bootstrapping, and generalisability was explored using internal–external cross-validation.Findings Residual depressive symptoms (OR: 1.13 (95% CI: 1.07 to 1.20), p<0.001) and baseline depression severity (OR: 1.07 (1.04 to 1.11), p<0.001) were associated with relapse. The validated model had low discrimination (C-statistic 0.60 (0.55–0.65)) and miscalibration concerns (calibration slope 0.81 (0.31–1.31)). On secondary analysis, being in a relationship was associated with reduced risk of relapse (OR: 0.43 (0.28–0.67), p<0.001); this remained statistically significant after correction for multiple significance testing.Conclusions We could not predict risk of depression relapse with sufficient accuracy in primary care data, using routinely recorded measures. Relationship status warrants further research to explore its role as a prognostic factor for relapse.Clinical implications Until we can accurately stratify patients according to risk of relapse, a universal approach to relapse prevention may be most beneficial, either during acute-phase treatment or post remission. Where possible, this could be guided by the presence or absence of known prognostic factors (eg, residual depressive symptoms) and targeted towards these.Trial registration number NCT04666662.https://mentalhealth.bmj.com/content/27/1/e301226.full |
| spellingShingle | Simon Gilbody Dean McMillan Bob Phillips Kym I E Snell Richard D Riley David A Richards Jaime Delgadillo Shehzad Ali Chris Salisbury Lewis W Paton Lucinda Archer Peter A Coventry Stephen Pilling Andrew S Moriarty Joshua E J Buckman Nick Meader Carolyn A Chew Graham Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies BMJ Mental Health |
| title | Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies |
| title_full | Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies |
| title_fullStr | Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies |
| title_full_unstemmed | Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies |
| title_short | Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies |
| title_sort | development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care secondary analysis of pooled individual participant data from multiple studies |
| url | https://mentalhealth.bmj.com/content/27/1/e301226.full |
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