Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai200030, PR China.
Department of Psychology, Florida A&M University, Tallahassee, Florida32307, USA.
Psychol Med. 2020 Jul;50(9):1578-1584. doi: 10.1017/S0033291719002174. Epub 2019 Aug 27.
Few of the previous studies of clinical high risk of psychosis (CHR) have explored whether outcomes other than conversion, such as poor functioning or treatment responses, are better predicted when using risk calculators. To answer this question, we compared the predictive accuracy between the outcome of conversion and poor functioning by using the NAPLS-2 risk calculator.
Three hundred CHR individuals were identified using the Chinese version of the Structured Interview for Prodromal Symptoms. Of these, 228 (76.0%) completed neurocognitive assessments at baseline and 199 (66.3%) had at least a 1-year follow-up assessment. The latter group was used in the NAPLS-2 risk calculator.
We divided the sample into two broad categories based on different outcome definitions, conversion (n = 46) v. non-conversion (n = 153) or recovery (n = 138) v. poor functioning (n = 61). Interestingly, the NAPLS-2 risk calculator showed moderate discrimination of subsequent conversion to psychosis in this sample with an area under the receiver operating characteristic curve (AUC) of 0.631 (p = 0.007). However, for discriminating poor functioning, the AUC of the model increased to 0.754 (p < 0.001).
Our results suggest that the current risk calculator was a better fit for predicting a poor functional outcome and treatment response than it was in the prediction of conversion to psychosis.
之前很少有关于临床精神病高危(CHR)的研究探索在使用风险计算器时,除了转化率之外,其他结果(如功能不良或治疗反应)是否可以更好地预测。为了回答这个问题,我们比较了使用 NAPLS-2 风险计算器预测转化率和功能不良结果的预测准确性。
使用中文版前驱症状结构化访谈,确定了 300 名 CHR 个体。其中,228 名(76.0%)在基线时完成了神经认知评估,199 名(66.3%)有至少 1 年的随访评估。后者用于 NAPLS-2 风险计算器。
我们根据不同的结果定义将样本分为两类,转化率(n=46)与非转化率(n=153)或恢复(n=138)与功能不良(n=61)。有趣的是,NAPLS-2 风险计算器在该样本中显示出对随后精神病转化率的中等区分能力,其受试者工作特征曲线下面积(AUC)为 0.631(p=0.007)。然而,对于区分功能不良,该模型的 AUC 增加到 0.754(p<0.001)。
我们的结果表明,当前的风险计算器更适合预测功能不良结果和治疗反应,而不是预测精神病转化率。