期刊
PSYCHOLOGICAL MEDICINE
卷 51, 期 4, 页码 653-660出版社
CAMBRIDGE UNIV PRESS
DOI: 10.1017/S003329171900360X
关键词
Outcome; prediction; prodromal psychosis; transition; ultra high risk
资金
- Ministry of Science and Technology of China
- National Key R&D Program of China [2016YFC1306800]
- National Natural Science Foundation of China [81671329, 81671332, 81361120403]
- Shanghai Jiaotong University Foundation [ZH2018ZDB03, ZH2018QNB19, YG2016QN42]
- Shanghai Key Laboratory of Psychotic Disorders [13dz2260500]
- Science and Technology Commission of Shanghai Municipality [19441907800, 17411953100, 16JC1420200]
- Clinical Research Center at Shanghai Mental Health Center [CRC2018ZD01, CRC2018ZD04, CRC2018YB01]
- Shanghai Mental Health Center Foundation [2017-TSXK-03]
- R21 Fogarty/NIMH [1R21 MH093294-01A1]
- NIMH [1R21MH113674]
- US-China Program for Biomedical Collaborative Research [1R01 MH 101052-01]
- United States NIMH [K23 MH102358]
The study developed a mobile app-based psychosis risk calculator (RC) that accurately predicts whether CHR individuals will transition to psychosis and provides personalized risk assessment. This tool can play an important role in monitoring and early intervention.
Background Only 30% or fewer of individuals at clinical high risk (CHR) convert to full psychosis within 2 years. Efforts are thus underway to refine risk identification strategies to increase their predictive power. Our objective was to develop and validate the predictive accuracy and individualized risk components of a mobile app-based psychosis risk calculator (RC) in a CHR sample from the SHARP (ShangHai At Risk for Psychosis) program. Method In total, 400 CHR individuals were identified by the Chinese version of the Structured Interview for Prodromal Syndromes. In the first phase of 300 CHR individuals, 196 subjects (65.3%) who completed neurocognitive assessments and had at least a 2-year follow-up assessment were included in the construction of an RC for psychosis. In the second phase of the SHARP sample of 100 subjects, 93 with data integrity were included to validate the performance of the SHARP-RC. Results The SHARP-RC showed good discrimination of subsequent transition to psychosis with an AUC of 0.78 (p < 0.001). The individualized risk generated by the SHARP-RC provided a solid estimation of conversion in the independent validation sample, with an AUC of 0.80 (p = 0.003). A risk estimate of 20% or higher had excellent sensitivity (84%) and moderate specificity (63%) for the prediction of psychosis. The relative contribution of individual risk components can be simultaneously generated. The mobile app-based SHARP-RC was developed as a convenient tool for individualized psychosis risk appraisal. Conclusions The SHARP-RC provides a practical tool not only for assessing the probability that an individual at CHR will develop full psychosis, but also personal risk components that might be targeted in early intervention.
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