Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Germany; Max Planck School of Cognition, Leipzig, Germany.
Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Germany.
Biol Psychiatry. 2020 Aug 15;88(4):349-360. doi: 10.1016/j.biopsych.2020.02.009. Epub 2020 Feb 20.
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.
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.
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.
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.
临床高风险（CHR）范式促进了对寻求帮助的处于精神病发病风险中的个体的潜在机制的研究，旨在预测并可能预防其向明显障碍的转变。统计方法，如机器学习和 Cox 回归，通过为构建诊断模型（即区分 CHR 个体与健康个体）和预后模型（即预测未来结果）提供了方法学基础，从而为该研究提供了方法学基础，这些模型基于不同的数据模态，包括临床、神经认知和神经生物学数据。然而，由于 CHR 人群和应用方法的高度异质性，它们在临床实践中的转化仍然受到阻碍。
我们系统地回顾了基于 Cox 回归和机器学习构建的诊断和预后模型的文献。此外，我们还对预测性能进行了荟萃分析，调查了方法学方法和数据模态的异质性。
共纳入 44 篇文章，涵盖了 3707 名用于预后研究的个体和 1052 名用于诊断研究的个体（572 名 CHR 患者和 480 名健康对照者）。CHR 患者可与健康对照者进行分类，敏感性为 78%，特异性为 77%。在预后模型中，敏感性达到 67%，特异性达到 78%。机器学习模型的敏感性比应用 Cox 回归的模型高 10%。预后研究存在发表偏倚，但没有其他调节效应。
我们的结果可能是由当前影响 CHR 领域多个方面的大量临床和方法学异质性所驱动的，这限制了所提出模型的临床可实施性。我们讨论了概念和方法学协调策略，以促进未来临床实践中更可靠和更具普遍性的模型。
