Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta View on the State of the Art
Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta View on the State of the Art
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DOI：
10.1016/j.biopsych.2020.02.009
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发表时间：
2020-08-15
影响因子：
10.6
通讯作者：
Koutsouleris, Nikolaos
中科院分区：
文献类型：
--
作者：
Sanfelici, Rachele;Dwyer, Dominic B.;Koutsouleris, Nikolaos
背景：临床高风险(CHR)范式促进了对处于发展成精神病风险的求助个体的基础的研究，旨在预测并可能预防向显性障碍的转变。机器学习和COX回归等统计学方法为这项研究提供了方法论基础，通过基于不同的数据模式(包括临床、神经认知和神经生物学数据)构建诊断模型(即区分CHR个体与健康个体)和预后模型(即预测未来结果)。方法：我们系统地回顾了建立在Cox回归和机器学习基础上的诊断和预后模型的文献。结果：共纳入44篇文章，其中3707篇用于预后研究，1052例用于诊断研究(572例慢性阻塞性肺病患者和480例健康对照)。CHR患者与健康对照组比较，敏感性为78%，特异性为77%。在预测预后的模型中，灵敏度达到67%，特异度达到78%。机器学习模型的灵敏度比使用COX回归的模型高出10%。我们的结果可能是由于临床和方法学上的异质性，目前影响了慢性阻塞性肺疾病领域的几个方面，并限制了所建议的模型的临床可实施性。我们讨论了概念和方法的协调策略，以促进更可靠和可推广的模型，用于未来的临床实践。
BACKGROUND: The clinical high risk (CHR) paradigm has facilitated research into the underpinnings of help-seeking individuals at risk for developing psychosis, aiming at predicting and possibly preventing transition to the overt disorder. Statistical methods such as machine learning and Cox regression have provided the methodological basis for this research by enabling the construction of diagnostic models (i.e., distinguishing CHR individuals from healthy individuals) and prognostic models (i.e., predicting a future outcome) based on different data modalities, including clinical, neurocognitive, and neurobiological data. However, their translation to clinical practice is still hindered by the high heterogeneity of both CHR populations and methodologies applied.METHODS: We systematically reviewed the literature on diagnostic and prognostic models built on Cox regression and machine learning. Furthermore, we conducted a meta-analysis on prediction performances investigating heterogeneity of methodological approaches and data modality.RESULTS: A total of 44 articles were included, covering 3707 individuals for prognostic studies and 1052 individuals for diagnostic studies (572 CHR patients and 480 healthy control subjects). CHR patients could be classified against healthy control subjects with 78% sensitivity and 77% specificity. Across prognostic models, sensitivity reached 67% and specificity reached 78%. Machine learning models outperformed those applying Cox regression by 10% sensitivity. There was a publication bias for prognostic studies yet no other moderator effects.CONCLUSIONS: Our results may be driven by substantial clinical and methodological heterogeneity currently affecting several aspects of the CHR field and limiting the clinical implementability of the proposed models. We discuss conceptual and methodological harmonization strategies to facilitate more reliable and generalizable models for future clinical practice.