Individualized Prediction of Prodromal Symptom Remission for Youth at Clinical High Risk for Psychosis.
The clinical high-risk period before a first episode of psychosis (CHR-P) has been widely studied with the goal of understanding the development of psychosis; however, less attention has been paid to the 75%–80% of CHR-P individuals who do not transition to psychosis. It is an open question whether...
| Publicado en: | Schizophrenia Bulletin Vol. 48; no. 2; pp. 395 - 405 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Mar2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155492718&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155492718 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 05867614 2CZ jtl: Schizophrenia Bulletin issn: 05867614 maglogo: N pubinfo: dt: Mar2022 vid: 48 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 155492718 155492718 155492718 10.1093/schbul/sbab115 155492718 ppf: 395 ppct: 10 formats: tig: atl: Individualized Prediction of Prodromal Symptom Remission for Youth at Clinical High Risk for Psychosis. aug: au: Worthington, Michelle A Addington, Jean Bearden, Carrie E Cadenhead, Kristin S Cornblatt, Barbara A Keshavan, Matcheri Mathalon, Daniel H McGlashan, Thomas H Perkins, Diana O Stone, William S Tsuang, Ming T Walker, Elaine F Woods, Scott W Cannon, Tyrone D affil: Department of Psychology, Yale University , New Haven, CT , USA sug: subj: Psychotic Disorders Risk Factors Disease Remission Human Machine Learning Recovery Algorithms ab: The clinical high-risk period before a first episode of psychosis (CHR-P) has been widely studied with the goal of understanding the development of psychosis; however, less attention has been paid to the 75%–80% of CHR-P individuals who do not transition to psychosis. It is an open question whether multivariable models could be developed to predict remission outcomes at the same level of performance and generalizability as those that predict conversion to psychosis. Participants were drawn from the North American Prodrome Longitudinal Study (NAPLS3). An empirically derived set of clinical and demographic predictor variables were selected with elastic net regularization and were included in a gradient boosting machine algorithm to predict prodromal symptom remission. The predictive model was tested in a comparably sized independent sample (NAPLS2). The classification algorithm developed in NAPLS3 achieved an area under the curve of 0.66 (0.60–0.72) with a sensitivity of 0.68 and specificity of 0.53 when tested in an independent external sample (NAPLS2). Overall, future remitters had lower baseline prodromal symptoms than nonremitters. This study is the first to use a data-driven machine-learning approach to assess clinical and demographic predictors of symptomatic remission in individuals who do not convert to psychosis. The predictive power of the models in this study suggest that remission represents a unique clinical phenomenon. Further study is warranted to best understand factors contributing to resilience and recovery from the CHR-P state. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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