前驱症状
精神病
曲线下面积
内科学
人口
持续时间(音乐)
医学
心理学
精神科
艺术
文学类
环境卫生
作者
Michelle A. Worthington,Meghan A. Collins,Jean Addington,Carrie E. Bearden,Kristin S. Cadenhead,Barbara A. Cornblatt,Matcheri S. Keshavan,Daniel H. Mathalon,Diana O. Perkins,William S. Stone,Elaine F. Walker,Scott W. Woods,Tyrone D. Cannon
标识
DOI:10.1017/s0033291723002301
摘要
Abstract Background Clinical implementation of risk calculator models in the clinical high-risk for psychosis (CHR-P) population has been hindered by heterogeneous risk distributions across study cohorts which could be attributed to pre-ascertainment illness progression. To examine this, we tested whether the duration of attenuated psychotic symptom (APS) worsening prior to baseline moderated performance of the North American prodrome longitudinal study 2 (NAPLS2) risk calculator. We also examined whether rates of cortical thinning, another marker of illness progression, bolstered clinical prediction models. Methods Participants from both the NAPLS2 and NAPLS3 samples were classified as either ‘long’ or ‘short’ symptom duration based on time since APS increase prior to baseline. The NAPLS2 risk calculator model was applied to each of these groups. In a subset of NAPLS3 participants who completed follow-up magnetic resonance imaging scans, change in cortical thickness was combined with the individual risk score to predict conversion to psychosis. Results The risk calculator models achieved similar performance across the combined NAPLS2/NAPLS3 sample [area under the curve (AUC) = 0.69], the long duration group (AUC = 0.71), and the short duration group (AUC = 0.71). The shorter duration group was younger and had higher baseline APS than the longer duration group. The addition of cortical thinning improved the prediction of conversion significantly for the short duration group (AUC = 0.84), with a moderate improvement in prediction for the longer duration group (AUC = 0.78). Conclusions These results suggest that early illness progression differs among CHR-P patients, is detectable with both clinical and neuroimaging measures, and could play an essential role in the prediction of clinical outcomes.
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