RETRACTED: Dynamic Prediction of Outcomes for Youth at Clinical High Risk for Psychosis: A Joint Modeling Approach

IMPORTANCE: Leveraging the dynamic nature of clinical variables in the clinical high risk for psychosis (CHR-P) population has the potential to significantly improve the performance of outcome prediction models. OBJECTIVE: To improve performance of prediction models and elucidate dynamic clinical pr...

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Veröffentlicht in:JAMA psychiatry (Chicago, Ill.) Ill.), 2023-10, Vol.80 (10), p.1017-1025
Hauptverfasser: Worthington, Michelle A, Addington, Jean, Bearden, Carrie E, Cadenhead, Kristin S, Cornblatt, Barbara A, Keshavan, Matcheri, Lympus, Cole A, Mathalon, Daniel H, Perkins, Diana O, Stone, William S, Walker, Elaine F, Woods, Scott W, Zhao, Yize, Cannon, Tyrone D
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container_end_page 1025
container_issue 10
container_start_page 1017
container_title JAMA psychiatry (Chicago, Ill.)
container_volume 80
creator Worthington, Michelle A
Addington, Jean
Bearden, Carrie E
Cadenhead, Kristin S
Cornblatt, Barbara A
Keshavan, Matcheri
Lympus, Cole A
Mathalon, Daniel H
Perkins, Diana O
Stone, William S
Walker, Elaine F
Woods, Scott W
Zhao, Yize
Cannon, Tyrone D
description IMPORTANCE: Leveraging the dynamic nature of clinical variables in the clinical high risk for psychosis (CHR-P) population has the potential to significantly improve the performance of outcome prediction models. OBJECTIVE: To improve performance of prediction models and elucidate dynamic clinical profiles using joint modeling to predict conversion to psychosis and symptom remission. DESIGN, SETTING, AND PARTICIPANTS: Data were collected as part of the third wave of the North American Prodrome Longitudinal Study (NAPLS 3), which is a 9-site prospective longitudinal study. Participants were individuals aged 12 to 30 years who met criteria for a psychosis-risk syndrome. Clinical, neurocognitive, and demographic variables were collected at baseline and at multiple follow-up visits, beginning at 2 months and up to 24 months. An initial feature selection process identified longitudinal clinical variables that showed differential change for each outcome group across 2 months. With these variables, a joint modeling framework was used to estimate the likelihood of eventual outcomes. Models were developed and tested in a 10-fold cross-validation framework. Clinical data were collected between February 2015 and November 2018, and data were analyzed from February 2022 to December 2023. MAIN OUTCOMES AND MEASURES: Prediction models were built to predict conversion to psychosis and symptom remission. Participants met criteria for conversion if their positive symptoms reached the fully psychotic range and for symptom remission if they were subprodromal on the Scale of Psychosis-Risk Symptoms for a duration of 6 months or more. RESULTS: Of 488 included NAPLS 3 participants, 232 (47.5%) were female, and the mean (SD) age was 18.2 (3.4) years. Joint models achieved a high level of accuracy in predicting conversion (balanced accuracy [BAC], 0.91) and remission (BAC, 0.99) compared with baseline models (conversion: BAC, 0.65; remission: BAC, 0.60). Clinical variables that showed differential change between outcome groups across a 2-month span, including measures of symptom severity and aspects of functioning, were also identified. Further, intra-individual risks for each outcome were more negatively correlated when using joint models (r = −0.92; P 
doi_str_mv 10.1001/jamapsychiatry.2023.2378
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OBJECTIVE: To improve performance of prediction models and elucidate dynamic clinical profiles using joint modeling to predict conversion to psychosis and symptom remission. DESIGN, SETTING, AND PARTICIPANTS: Data were collected as part of the third wave of the North American Prodrome Longitudinal Study (NAPLS 3), which is a 9-site prospective longitudinal study. Participants were individuals aged 12 to 30 years who met criteria for a psychosis-risk syndrome. Clinical, neurocognitive, and demographic variables were collected at baseline and at multiple follow-up visits, beginning at 2 months and up to 24 months. An initial feature selection process identified longitudinal clinical variables that showed differential change for each outcome group across 2 months. With these variables, a joint modeling framework was used to estimate the likelihood of eventual outcomes. Models were developed and tested in a 10-fold cross-validation framework. Clinical data were collected between February 2015 and November 2018, and data were analyzed from February 2022 to December 2023. MAIN OUTCOMES AND MEASURES: Prediction models were built to predict conversion to psychosis and symptom remission. Participants met criteria for conversion if their positive symptoms reached the fully psychotic range and for symptom remission if they were subprodromal on the Scale of Psychosis-Risk Symptoms for a duration of 6 months or more. RESULTS: Of 488 included NAPLS 3 participants, 232 (47.5%) were female, and the mean (SD) age was 18.2 (3.4) years. Joint models achieved a high level of accuracy in predicting conversion (balanced accuracy [BAC], 0.91) and remission (BAC, 0.99) compared with baseline models (conversion: BAC, 0.65; remission: BAC, 0.60). Clinical variables that showed differential change between outcome groups across a 2-month span, including measures of symptom severity and aspects of functioning, were also identified. Further, intra-individual risks for each outcome were more negatively correlated when using joint models (r = −0.92; P &lt; .001) compared with baseline models (r = −0.50; P &lt; .001). CONCLUSIONS AND RELEVANCE: In this study, joint models significantly outperformed baseline models in predicting both conversion and remission, demonstrating that monitoring short-term clinical change may help to parse heterogeneous dynamic clinical trajectories in a CHR-P population. These findings could inform additional study of targeted treatment selection and could move the field closer to clinical implementation of prediction models.</description><identifier>ISSN: 2168-622X</identifier><identifier>EISSN: 2168-6238</identifier><identifier>DOI: 10.1001/jamapsychiatry.2023.2378</identifier><language>eng</language><publisher>American Medical Association</publisher><ispartof>JAMA psychiatry (Chicago, Ill.), 2023-10, Vol.80 (10), p.1017-1025</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-a688-b003647894ea580526b7544fa8291409d52028560a07e12a387eb2bd006ec803</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://jamanetwork.com/journals/jamapsychiatry/articlepdf/10.1001/jamapsychiatry.2023.2378$$EPDF$$P50$$Gama$$H</linktopdf><linktohtml>$$Uhttps://jamanetwork.com/journals/jamapsychiatry/fullarticle/10.1001/jamapsychiatry.2023.2378$$EHTML$$P50$$Gama$$H</linktohtml><link.rule.ids>64,314,780,784,3340,27924,27925,76489,76492</link.rule.ids></links><search><creatorcontrib>Worthington, Michelle A</creatorcontrib><creatorcontrib>Addington, Jean</creatorcontrib><creatorcontrib>Bearden, Carrie E</creatorcontrib><creatorcontrib>Cadenhead, Kristin S</creatorcontrib><creatorcontrib>Cornblatt, Barbara A</creatorcontrib><creatorcontrib>Keshavan, Matcheri</creatorcontrib><creatorcontrib>Lympus, Cole A</creatorcontrib><creatorcontrib>Mathalon, Daniel H</creatorcontrib><creatorcontrib>Perkins, Diana O</creatorcontrib><creatorcontrib>Stone, William S</creatorcontrib><creatorcontrib>Walker, Elaine F</creatorcontrib><creatorcontrib>Woods, Scott W</creatorcontrib><creatorcontrib>Zhao, Yize</creatorcontrib><creatorcontrib>Cannon, Tyrone D</creatorcontrib><title>RETRACTED: Dynamic Prediction of Outcomes for Youth at Clinical High Risk for Psychosis: A Joint Modeling Approach</title><title>JAMA psychiatry (Chicago, Ill.)</title><description>IMPORTANCE: Leveraging the dynamic nature of clinical variables in the clinical high risk for psychosis (CHR-P) population has the potential to significantly improve the performance of outcome prediction models. OBJECTIVE: To improve performance of prediction models and elucidate dynamic clinical profiles using joint modeling to predict conversion to psychosis and symptom remission. DESIGN, SETTING, AND PARTICIPANTS: Data were collected as part of the third wave of the North American Prodrome Longitudinal Study (NAPLS 3), which is a 9-site prospective longitudinal study. Participants were individuals aged 12 to 30 years who met criteria for a psychosis-risk syndrome. Clinical, neurocognitive, and demographic variables were collected at baseline and at multiple follow-up visits, beginning at 2 months and up to 24 months. An initial feature selection process identified longitudinal clinical variables that showed differential change for each outcome group across 2 months. With these variables, a joint modeling framework was used to estimate the likelihood of eventual outcomes. Models were developed and tested in a 10-fold cross-validation framework. Clinical data were collected between February 2015 and November 2018, and data were analyzed from February 2022 to December 2023. MAIN OUTCOMES AND MEASURES: Prediction models were built to predict conversion to psychosis and symptom remission. Participants met criteria for conversion if their positive symptoms reached the fully psychotic range and for symptom remission if they were subprodromal on the Scale of Psychosis-Risk Symptoms for a duration of 6 months or more. RESULTS: Of 488 included NAPLS 3 participants, 232 (47.5%) were female, and the mean (SD) age was 18.2 (3.4) years. Joint models achieved a high level of accuracy in predicting conversion (balanced accuracy [BAC], 0.91) and remission (BAC, 0.99) compared with baseline models (conversion: BAC, 0.65; remission: BAC, 0.60). Clinical variables that showed differential change between outcome groups across a 2-month span, including measures of symptom severity and aspects of functioning, were also identified. Further, intra-individual risks for each outcome were more negatively correlated when using joint models (r = −0.92; P &lt; .001) compared with baseline models (r = −0.50; P &lt; .001). CONCLUSIONS AND RELEVANCE: In this study, joint models significantly outperformed baseline models in predicting both conversion and remission, demonstrating that monitoring short-term clinical change may help to parse heterogeneous dynamic clinical trajectories in a CHR-P population. These findings could inform additional study of targeted treatment selection and could move the field closer to clinical implementation of prediction models.</description><issn>2168-622X</issn><issn>2168-6238</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNpVkE1OwzAQhS0EElXpBVj5AiljOz9ud1FaKKioVekCVpHjOI1LE0d2usjtSSiqxGxmpHlv9OZDCBOYEgDydBSVaFwnSy1a200pUDalLOI3aERJyL2QMn57nennPZo4d4S-OIDP-AjZ3XK_i5P9cjHHi64WlZZ4a1WuZatNjU2BN-dWmko5XBiLv8y5LbFocXLStZbihFf6UOKddt-_--0Qxjjt5jjGb0bXLX43uerFBxw3jTVClg_orhAnpyZ_fYw-npf7ZOWtNy-vSbz2RMi5lwGw0I_4zFci4BDQMIsC3y8EpzPiwywP-m95EIKASBEqGI9URrMcIFSSAxsjfrkqrXHOqiJtrK6E7VIC6QAv_Q8vHeClA7ze-nix9oKri3KIIsLZD60QboA</recordid><startdate>20231001</startdate><enddate>20231001</enddate><creator>Worthington, Michelle A</creator><creator>Addington, Jean</creator><creator>Bearden, Carrie E</creator><creator>Cadenhead, Kristin S</creator><creator>Cornblatt, Barbara A</creator><creator>Keshavan, Matcheri</creator><creator>Lympus, Cole A</creator><creator>Mathalon, Daniel H</creator><creator>Perkins, Diana O</creator><creator>Stone, William S</creator><creator>Walker, Elaine F</creator><creator>Woods, Scott W</creator><creator>Zhao, Yize</creator><creator>Cannon, Tyrone D</creator><general>American Medical Association</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20231001</creationdate><title>RETRACTED: Dynamic Prediction of Outcomes for Youth at Clinical High Risk for Psychosis: A Joint Modeling Approach</title><author>Worthington, Michelle A ; Addington, Jean ; Bearden, Carrie E ; Cadenhead, Kristin S ; Cornblatt, Barbara A ; Keshavan, Matcheri ; Lympus, Cole A ; Mathalon, Daniel H ; Perkins, Diana O ; Stone, William S ; Walker, Elaine F ; Woods, Scott W ; Zhao, Yize ; Cannon, Tyrone D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a688-b003647894ea580526b7544fa8291409d52028560a07e12a387eb2bd006ec803</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Worthington, Michelle A</creatorcontrib><creatorcontrib>Addington, Jean</creatorcontrib><creatorcontrib>Bearden, Carrie E</creatorcontrib><creatorcontrib>Cadenhead, Kristin S</creatorcontrib><creatorcontrib>Cornblatt, Barbara A</creatorcontrib><creatorcontrib>Keshavan, Matcheri</creatorcontrib><creatorcontrib>Lympus, Cole A</creatorcontrib><creatorcontrib>Mathalon, Daniel H</creatorcontrib><creatorcontrib>Perkins, Diana O</creatorcontrib><creatorcontrib>Stone, William S</creatorcontrib><creatorcontrib>Walker, Elaine F</creatorcontrib><creatorcontrib>Woods, Scott W</creatorcontrib><creatorcontrib>Zhao, Yize</creatorcontrib><creatorcontrib>Cannon, Tyrone D</creatorcontrib><collection>CrossRef</collection><jtitle>JAMA psychiatry (Chicago, Ill.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Worthington, Michelle A</au><au>Addington, Jean</au><au>Bearden, Carrie E</au><au>Cadenhead, Kristin S</au><au>Cornblatt, Barbara A</au><au>Keshavan, Matcheri</au><au>Lympus, Cole A</au><au>Mathalon, Daniel H</au><au>Perkins, Diana O</au><au>Stone, William S</au><au>Walker, Elaine F</au><au>Woods, Scott W</au><au>Zhao, Yize</au><au>Cannon, Tyrone D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>RETRACTED: Dynamic Prediction of Outcomes for Youth at Clinical High Risk for Psychosis: A Joint Modeling Approach</atitle><jtitle>JAMA psychiatry (Chicago, Ill.)</jtitle><date>2023-10-01</date><risdate>2023</risdate><volume>80</volume><issue>10</issue><spage>1017</spage><epage>1025</epage><pages>1017-1025</pages><issn>2168-622X</issn><eissn>2168-6238</eissn><abstract>IMPORTANCE: Leveraging the dynamic nature of clinical variables in the clinical high risk for psychosis (CHR-P) population has the potential to significantly improve the performance of outcome prediction models. OBJECTIVE: To improve performance of prediction models and elucidate dynamic clinical profiles using joint modeling to predict conversion to psychosis and symptom remission. DESIGN, SETTING, AND PARTICIPANTS: Data were collected as part of the third wave of the North American Prodrome Longitudinal Study (NAPLS 3), which is a 9-site prospective longitudinal study. Participants were individuals aged 12 to 30 years who met criteria for a psychosis-risk syndrome. Clinical, neurocognitive, and demographic variables were collected at baseline and at multiple follow-up visits, beginning at 2 months and up to 24 months. An initial feature selection process identified longitudinal clinical variables that showed differential change for each outcome group across 2 months. With these variables, a joint modeling framework was used to estimate the likelihood of eventual outcomes. Models were developed and tested in a 10-fold cross-validation framework. Clinical data were collected between February 2015 and November 2018, and data were analyzed from February 2022 to December 2023. MAIN OUTCOMES AND MEASURES: Prediction models were built to predict conversion to psychosis and symptom remission. Participants met criteria for conversion if their positive symptoms reached the fully psychotic range and for symptom remission if they were subprodromal on the Scale of Psychosis-Risk Symptoms for a duration of 6 months or more. RESULTS: Of 488 included NAPLS 3 participants, 232 (47.5%) were female, and the mean (SD) age was 18.2 (3.4) years. Joint models achieved a high level of accuracy in predicting conversion (balanced accuracy [BAC], 0.91) and remission (BAC, 0.99) compared with baseline models (conversion: BAC, 0.65; remission: BAC, 0.60). Clinical variables that showed differential change between outcome groups across a 2-month span, including measures of symptom severity and aspects of functioning, were also identified. Further, intra-individual risks for each outcome were more negatively correlated when using joint models (r = −0.92; P &lt; .001) compared with baseline models (r = −0.50; P &lt; .001). CONCLUSIONS AND RELEVANCE: In this study, joint models significantly outperformed baseline models in predicting both conversion and remission, demonstrating that monitoring short-term clinical change may help to parse heterogeneous dynamic clinical trajectories in a CHR-P population. These findings could inform additional study of targeted treatment selection and could move the field closer to clinical implementation of prediction models.</abstract><pub>American Medical Association</pub><doi>10.1001/jamapsychiatry.2023.2378</doi><tpages>9</tpages></addata></record>
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title RETRACTED: Dynamic Prediction of Outcomes for Youth at Clinical High Risk for Psychosis: A Joint Modeling Approach
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