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.

中文翻译：

---

精神病风险综合征患者的个性化诊断和预后模型：对最新技术的元分析观点。

背景 临床高风险 (CHR) 范式促进了对有患精神病风险的寻求帮助的个体的基础的研究，旨在预测并可能防止向明显障碍的转变。机器学习和 Cox 回归等统计方法通过构建基于不同数据模式的诊断模型（即区分 CHR 个体与健康个体）和预后模型（即预测未来结果），为这项研究提供了方法论基础，包括临床、神经认知和神经生物学数据。然而，它们向临床实践的转化仍然受到 CHR 人群和所应用方法的高度异质性的阻碍。方法 我们系统地回顾了基于 Cox 回归和机器学习的诊断和预后模型的文献。此外，我们对预测性能进行了荟萃分析，调查了方法论方法和数据模式的异质性。结果共纳入44篇文章，涵盖3707人进行预后研究和1052人进行诊断研究（572名CHR患者和480名健康对照受试者）。CHR 患者可以与健康对照受试者进行分类，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%。机器学习模型的灵敏度比应用 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。结论 我们的结果可能是由目前影响 CHR 领域几个方面的大量临床和方法学异质性驱动的，并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上的协调策略，以促进未来临床实践中更可靠和可推广的模型。