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Prediction of psychosis: Model development and internal validation of a personalized risk calculatorHighly Cited Paper

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dc.contributor.authorLee, Tae Young-
dc.contributor.authorWu Jeong Hwang-
dc.contributor.authorNahrie S. Kim-
dc.contributor.authorInkyung Park-
dc.contributor.authorLho, Silvia Kyungjin-
dc.contributor.authorMoon, Sun-Young-
dc.contributor.authorOh, Sanghoon-
dc.contributor.authorLee, Junhee-
dc.contributor.authorKim, Minah-
dc.contributor.authorChoong-Wan Woo-
dc.contributor.authorJun Soo Kwon-
dc.date.accessioned2023-01-27T00:40:12Z-
dc.date.available2023-01-27T00:40:12Z-
dc.date.created2021-01-26-
dc.date.issued2022-10-
dc.identifier.issn0033-2917-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/12843-
dc.description.abstractCopyright © The Author(s), 2020. Published by Cambridge University Press.Background Over the past two decades, early detection and early intervention in psychosis have become essential goals of psychiatry. However, clinical impressions are insufficient for predicting psychosis outcomes in clinical high-risk (CHR) individuals; a more rigorous and objective model is needed. This study aims to develop and internally validate a model for predicting the transition to psychosis within 10 years. Methods Two hundred and eight help-seeking individuals who fulfilled the CHR criteria were enrolled from the prospective, naturalistic cohort program for CHR at the Seoul Youth Clinic (SYC). The least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was used to develop a predictive model for a psychotic transition. We performed k-means clustering and survival analysis to stratify the risk of psychosis. Results The predictive model, which includes clinical and cognitive variables, identified the following six baseline variables as important predictors: 1-year percentage decrease in the Global Assessment of Functioning score, IQ, California Verbal Learning Test score, Strange Stories test score, and scores in two domains of the Social Functioning Scale. The predictive model showed a cross-validated Harrell's C-index of 0.78 and identified three subclusters with significantly different risk levels. Conclusions Overall, our predictive model showed a predictive ability and could facilitate a personalized therapeutic approach to different risks in high-risk individuals.-
dc.language영어-
dc.publisherCambridge University Press-
dc.titlePrediction of psychosis: Model development and internal validation of a personalized risk calculator-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000880310400027-
dc.identifier.scopusid2-s2.0-85097839604-
dc.identifier.rimsid74373-
dc.contributor.affiliatedAuthorWu Jeong Hwang-
dc.contributor.affiliatedAuthorNahrie S. Kim-
dc.contributor.affiliatedAuthorInkyung Park-
dc.contributor.affiliatedAuthorChoong-Wan Woo-
dc.contributor.affiliatedAuthorJun Soo Kwon-
dc.identifier.doi10.1017/S0033291720004675-
dc.identifier.bibliographicCitationPsychological Medicine, v.52, no.13, pp.2632 - 2640-
dc.relation.isPartOfPsychological Medicine-
dc.citation.titlePsychological Medicine-
dc.citation.volume52-
dc.citation.number13-
dc.citation.startPage2632-
dc.citation.endPage2640-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassssci-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaPsychology-
dc.relation.journalResearchAreaPsychiatry-
dc.relation.journalWebOfScienceCategoryPsychology, Clinical-
dc.relation.journalWebOfScienceCategoryPsychiatry-
dc.relation.journalWebOfScienceCategoryPsychology-
dc.subject.keywordPlusCLINICAL HIGH-RISK-
dc.subject.keywordPlusULTRA-HIGH-RISK-
dc.subject.keywordPlusEARLY INTERVENTION-
dc.subject.keywordPlusAT-RISK-
dc.subject.keywordPlusFOLLOW-UP-
dc.subject.keywordPlusMENTAL STATE-
dc.subject.keywordPlusTRANSITION-
dc.subject.keywordPlusINDIVIDUALS-
dc.subject.keywordPlusSCHIZOPHRENIA-
dc.subject.keywordPlusPREVENTION-
dc.subject.keywordAuthorpsychosis-
dc.subject.keywordAuthortransition-
dc.subject.keywordAuthorClinical high-risk (CHR)-
dc.subject.keywordAuthorLASSO-
dc.subject.keywordAuthorpersonalized medicine-
dc.subject.keywordAuthorprediction-
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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