Zhang TianHong, Tang XiaoChen, Zhang Yue, Xu LiHua, Wei YanYan, Hu YeGang, Cui HuiRu, Tang YingYing, Liu HaiChun, Chen Tao, Li ChunBo, Wang JiJun
Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China.
Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, PR China.
Asian J Psychiatr. 2023 Mar;81:103468. doi: 10.1016/j.ajp.2023.103468. Epub 2023 Jan 18.
This study attempted to construct and validate dynamic prediction via multivariate joint models and compare the prognostic performance of these models to both static and univariate joint models. Individuals with clinical high risk(CHR)(n = 289) were recruited and re-assessed for positive symptoms, general functions, and conversion to psychosis at 2-months, 1-year, and 2-years to develop the dynamic models. A multivariate joint model of positive psychotic symptoms was assessed using the Structured Interview for Prodromal Symptoms(SIPSp) and general function assessed by global assessment of functioning scores(GAFs) with time-to-conversion to psychosis. The area under the receiver operating characteristic(ROC) curve(AUC) was used to test the accuracy of the models. Among 298 CHR individuals, 68 converted to psychosis within 2 years after the initial assessments. Multivariate joint models showed that declining GAFs and increasing SIPSp corresponded to significant and trending to significantly increased risk of psychosis onset and had much higher prognostic accuracy (cross-validated AUC=0.9) compared to the static model(AUC=0.6) and univariate joint models(cross-validated AUC=0.6-0.8). Our results showed that multivariate joint models could be highly efficient in forecasting psychosis onset for CHR individuals. Longitudinal assessments for psychopathology and general functions can be useful for dynamically predicting the prognosis of the pre-morbid phase of psychosis.
本研究试图通过多变量联合模型构建并验证动态预测，并将这些模型的预后性能与静态和单变量联合模型进行比较。招募了临床高危（CHR）个体（n = 289），并在2个月、1年和2年时对其阳性症状、一般功能以及向精神病的转变进行重新评估，以建立动态模型。使用前驱症状结构化访谈（SIPSp）评估阳性精神病性症状的多变量联合模型，并通过功能总体评定量表（GAFs）评估一般功能，并记录向精神病转变的时间。采用受试者操作特征（ROC）曲线下面积（AUC）来检验模型的准确性。在298名CHR个体中，68人在初次评估后的2年内转变为精神病。多变量联合模型显示，GAFs下降和SIPSp增加对应着精神病发作风险显著增加且呈显著上升趋势，与静态模型（AUC = 0.6）和单变量联合模型（交叉验证AUC = 0.6 - 0.8）相比，其预后准确性更高（交叉验证AUC = 0.9）。我们的结果表明，多变量联合模型在预测CHR个体的精神病发作方面可能非常有效。对精神病理学和一般功能的纵向评估有助于动态预测精神病发病前期的预后。
