The Early Psychosis Screener (EPS): Quantitative validation against the SIPS using machine learning
The Early Psychosis Screener (EPS): Quantitative validation against the SIPS using machine learning
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DOI：
10.1016/j.schres.2017.11.030
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发表时间：
2018-07-01
影响因子：
4.5
通讯作者：
Brodey, I. S.
中科院分区：
文献类型：
--
作者：
Brodey, B. B.;Girgis, R. R.;Brodey, I. S.
机器学习技术用于识别信息丰富的早期精神病自我报告项目，并根据精神病风险综合征结构化访谈 (SIPS) 验证早期精神病筛查 (EPS)。在 7 个北美前驱症状纵向研究中心和哥伦比亚大学，对 229 名接受 SIPS 筛查的个体进行了前驱症状问卷简明版 (PQ-B) 和 148 个附加项目。发现 50 人的 SIPS 评分为 0、1 或 2，使他们成为临床低风险 (CLR) 对照； 144 人被归类为临床高危 (CHR) (SIPS 3-5)，35 人被发现患有首发精神病 (FEP) (SIPS 6)。对其中 124 个项目进行谱聚类分析，得出两个有凝聚力的项目组，第一个主要与精神病和躁狂症相关，第二个主要与抑郁、焦虑以及社交和一般工作/学校功能相关。使用最小冗余最大相关性程序，根据区分 CLR 和 CHR 个体的有用性对每组内的项目进行排序。受试者工作特征曲线下面积 (AUC) 分析表明，CLR 和 CHR 参与者的最大分化是通过 26 项解决方案实现的（AUC = 0.899 +/- 0.001）。 EPS-26 优于 PQ-B (AUC = 0.834 +/- 0.001)。出于筛查目的，自我报告 EPS-26 似乎可以区分 CLR 或 CHR 个体以及临床医生施用的 SIPS。 EPS-26 可能被证明可用作自我报告筛选器，并可能导致未经治疗的精神病持续时间缩短。 EPS-26 与实际转换的验证正在进行中。 (C) 2017 Elsevier B.V. 保留所有权利。
Machine learning techniques were used to identify highly informative early psychosis self-report items and to validate an early psychosis screener (EPS) against the Structured Interview for Psychosis-risk Syndromes (SIPS). The Prodromal Questionnaire-Brief Version (PQ-B) and 148 additional items were administered to 229 individuals being screened with the SIPS at 7 North American Prodrome Longitudinal Study sites and at Columbia University. Fifty individuals were found to have SIPS scores of 0, 1, or 2, making them clinically low risk (CLR) controls; 144 were classified as clinically high risk (CHR) (SIPS 3-5) and 35 were found to have first episode psychosis (FEP) (SIPS 6). Spectral clustering analysis, performed on 124 of the items, yielded two cohesive item groups, the first mostly related to psychosis and mania, the second mostly related to depression, anxiety, and social and general work/school functioning. Items within each group were sorted according to their usefulness in distinguishing between CLR and CHR individuals using the Minimum Redundancy Maximum Relevance procedure. A receiver operating characteristic area under the curve (AUC) analysis indicated that maximal differentiation of CLR and CHR participants was achieved with a 26-item solution (AUC = 0.899 +/- 0.001). The EPS-26 outperformed the PQ-B (AUC = 0.834 +/- 0.001). For screening purposes, the self-report EPS-26 appeared to differentiate individuals who are either CLR or CHR approximately as well as the clinician-administered SIPS. The EPS-26 may prove useful as a self-report screener and may lead to a decrease in the duration of untreated psychosis. A validation of the EPS-26 against actual conversion is underway. (C) 2017 Elsevier B.V. All rights reserved.