Andrew S Moriarty, Lewis W Paton, Kym I E Snell, Lucinda Archer, Richard D Riley, Joshua E J Buckman, Carolyn A Chew Graham, Simon Gilbody, Shehzad Ali, Stephen Pilling, Nick Meader, Bob Phillips, Peter A Coventry, Jaime Delgadillo, David A Richards, Chris Salisbury, Dean McMillan
{"title":"开发和验证用于预测初级保健中已缓解抑郁症成人患者复发的预后模型：对多项研究中汇集的个体参与者数据进行二次分析。","authors":"Andrew S Moriarty, Lewis W Paton, Kym I E Snell, Lucinda Archer, Richard D Riley, Joshua E J Buckman, Carolyn A Chew Graham, Simon Gilbody, Shehzad Ali, Stephen Pilling, Nick Meader, Bob Phillips, Peter A Coventry, Jaime Delgadillo, David A Richards, Chris Salisbury, Dean McMillan","doi":"10.1136/bmjment-2024-301226","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>The objective was to develop and validate a prognostic model to predict risk of relapse of depression in primary care.</p><p><strong>Methods: </strong>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.</p><p><strong>Findings: </strong>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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Clinical implications: </strong>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.</p><p><strong>Trial registration number: </strong>NCT04666662.</p>","PeriodicalId":72434,"journal":{"name":"BMJ mental health","volume":"27 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529744/pdf/","citationCount":"0","resultStr":"{\"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.\",\"authors\":\"Andrew S Moriarty, Lewis W Paton, Kym I E Snell, Lucinda Archer, Richard D Riley, Joshua E J Buckman, Carolyn A Chew Graham, Simon Gilbody, Shehzad Ali, Stephen Pilling, Nick Meader, Bob Phillips, Peter A Coventry, Jaime Delgadillo, David A Richards, Chris Salisbury, Dean McMillan\",\"doi\":\"10.1136/bmjment-2024-301226\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>The objective was to develop and validate a prognostic model to predict risk of relapse of depression in primary care.</p><p><strong>Methods: </strong>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.</p><p><strong>Findings: </strong>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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Clinical implications: </strong>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.</p><p><strong>Trial registration number: </strong>NCT04666662.</p>\",\"PeriodicalId\":72434,\"journal\":{\"name\":\"BMJ mental health\",\"volume\":\"27 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529744/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMJ mental health\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1136/bmjment-2024-301226\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"0\",\"JCRName\":\"PSYCHIATRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMJ mental health","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1136/bmjment-2024-301226","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"PSYCHIATRY","Score":null,"Total":0}
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 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.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
