[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-639337-105":59,"doc-detail-639337-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","trajectories-of-remitted-psychotic-depression-identification-of-predictors-of-worsening-by-machine-learning","Trajectories of remitted psychotic depression - identification of predictors of worsening by machine learning","","Remitted psychotic depression (MDDPsy) shows heterogeneity in outcomes. The study identifies subgroups of individuals with distinct trajectories of depression severity during continuation treatment and determines predictors for membership in a worsening trajectory. A 36-week randomized placebo-controlled trial (n=126) used latent class mixed modeling to derive trajectory subgroups and machine learning to predict worsening membership from pre-trajectory characteristics.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/trajectories-of-remitted-psychotic-depression-identification-of-predictors-of-worsening-by-machine-learning/639337/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/trajectories-of-remitted-psychotic-depression-identification-of-predictors-of-worsening-by-machine-learning/639337.png","ImageObject",300,407,{"name":92,"@type":93},"Blue Pony","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-10-22",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What were the study’s aims for remitted psychotic depression?","Question",{"text":112,"@type":113},"To identify subgroups with distinct depression-severity trajectories during continuation treatment, and to detect predictors of membership in the worsening trajectory.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were trajectory subgroups identified in the trial?",{"text":117,"@type":113},"Latent class mixed modeling was used to identify participant subgroups with distinct trajectories of depression severity during the randomized controlled trial.",{"name":119,"@type":110,"acceptedAnswer":120},"Which factors predicted membership in the worsening trajectory?",{"text":121,"@type":113},"Residual depressive symptoms at onset of remission were the strongest predictors, followed by anxiety score at RCT baseline and age of onset of the first lifetime depressive episode.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},639337,1792870217,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},962090760266,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Psychological Medicine  \n[cambridge.org/psm](cambridge.org/psm)  \nOriginal Article  \nCite this article: Banerjee S et al (2024) . Trajectories of remitted psychotic depression:  \nidentification of predictors of worsening by machine learning. Psychological Medicine 54, 1142–1151. [https://doi.org/10.1017/](https://doi.org/10.1017/)[ ](https://doi.org/10.1017/)S0033291723002945  \nReceived: 16 May 2023  \nRevised: 6 September 2023  \nAccepted: 12 September 2023  \nFirst published online: 11 October 2023  \nKeywords:  \nmachine learning; outcome; predictors; psychotic depression; relapse; remission; residual depressive symptoms; trajectories  \nCorresponding author:  \nAlastair J. Flint;  \nEmail: [alastair.flint@uhn.ca](alastair.flint@uhn.ca)  \nTrajectories of remitted psychotic depression: identification of predictors of worsening by machine learning  \nSamprit Banerjee1, Yiyuan Wu1, Kathleen S. Bingham2,3,4, Patricia Marino5, Barnett S. Meyers5, Benoit H. Mulsant2,3, Nicholas H. Neufeld2,3,  \nLindsay D. Oliver3, Jonathan D. Power6, Anthony J. Rothschild7, Jo Anne Sirey5, Aristotle N. Voineskos2,3, Ellen M. Whyte8, George S. Alexopoulos5,  \nAlastair J. Flint2,4  and on behalf of the STOP-PD II Study Group  \n1Department of Population Health Sciences, Weill Cornell Medicine, New York, USA; 2Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; 3Centre for Addiction and Mental Health, Toronto, Canada; 4Centre for Mental Health, University Health Network, Toronto, Canada; 5Department of Psychiatry, Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, New York, USA; 6Department of Psychiatry, Weill Cornell Medicine, New York, USA; 7University of Massachusetts Chan Medical School and UMass Memorial Health Care, Worcester, USA and 8Department of Psychiatry, University of Pittsburgh School of Medicine and UPMC Western Psychiatric Hospital, Pittsburgh, USA  \nAbstract  \nBackground. Remitted psychotic depression (MDDPsy) has heterogeneity of outcome. The study’s aims were to identify subgroups of persons with remitted MDDPsy with distinct trajectories of depression severity during continuation treatment and to detect predictors of membership to the worsening trajectory.  \nMethod. One hundred and twenty-six persons aged 18–85 years participated in a 36-week randomized placebo-controlled trial (RCT) that examined the clinical effects of continuing olanzapine once an episode of MDDPsy had remitted with sertraline plus olanzapine. Latent class mixed modeling was used to identify subgroups of participants with distinct trajectories of depression severity during the RCT. Machine learning was used to predict membership to the trajectories based on participant pre-trajectory characteristics.  \nResults. Seventy-one (56.3%) participants belonged to a subgroup with a stable trajectory of depression scores and 55 (43.7%) belonged to a subgroup with a worsening trajectory. A random forest model with high prediction accuracy (AUC of 0.812) found that the strongest predictors of membership to the worsening subgroup were residual depression symptoms at onset of remission, followed by anxiety score at RCT baseline and age of onset of the first lifetime depressive episode. In a logistic regression model that examined depression score at onset of remission as the only predictor variable, the AUC (0.778) was close to that of the machine learning model.  \nConclusions. Residual depression at onset of remission has high accuracy in predicting membership to worsening outcome of remitted MDDPsy. Research is needed to determine how best to optimize the outcome of psychotic MDDPsy with residual symptoms.  \nIntroduction  \n© The Author(s), 2023 . Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)), which permits unrestricted re-use, d","cbCaivytayZi8GeH","https://ap.wps.com/l/cbCaivytayZi8GeH","pdf",379221,"English","# Abstract\n## Background\n## Method\n## Results\n## Conclusions\n# Introduction\n## Clinical context and heterogeneity of outcome","[{\"question\":\"What were the study’s aims for remitted psychotic depression?\",\"answer\":\"To identify subgroups with distinct depression-severity trajectories during continuation treatment, and to detect predictors of membership in the worsening trajectory.\"},{\"question\":\"How were trajectory subgroups identified in the trial?\",\"answer\":\"Latent class mixed modeling was used to identify participant subgroups with distinct trajectories of depression severity during the randomized controlled trial.\"},{\"question\":\"Which factors predicted membership in the worsening trajectory?\",\"answer\":\"Residual depressive symptoms at onset of remission were the strongest predictors, followed by anxiety score at RCT baseline and age of onset of the first lifetime depressive episode.\"}]","Trajectories of remitted psychotic depression - identification of predictors of worsening by machine learning | PDF",1792703112,25]