The concept of ultra-high risk for psychosis (UHR) has been at the forefront of psychiatric research for several decades, with the ultimate goal of preventing the onset of psychotic disorder in high-risk individuals. Orygen (Melbourne, Australia) has led a range of observational and intervention studies in this clinical population. These datasets have now been integrated into the UHR 1000+ cohort, consisting of a sample of 1,245 UHR individuals with a follow-up period ranging from 1 to 16.7 years. This paper describes the cohort, presents a clinical prediction model of transition to psychosis in this cohort, and examines how predictive performance is affected by changes in UHR samples over time. We analyzed transition to psychosis using a Cox proportional hazards model. Clinical predictors for transition to psychosis were investigated in the entire cohort using multiple imputation and Rubin's rule. To assess performance drift over time, data from 1995-2016 were used for initial model fitting, and models were subsequently validated on data from 2017-2020. Over the follow-up period, 220 cases (17.7%) developed a psychotic disorder. Pooled hazard ratio (HR) estimates showed that the Comprehensive Assessment of At-Risk Mental States (CAARMS) Disorganized Speech subscale severity score (HR=1.12, 95% CI: 1.02-1.24, p=0.024), the CAARMS Unusual Thought Content subscale severity score (HR=1.13, 95% CI: 1.03-1.24, p=0.009), the Scale for the Assessment of Negative Symptoms (SANS) total score (HR=1.02, 95% CI: 1.00-1.03, p=0.022), the Social and Occupational Functioning Assessment Scale (SOFAS) score (HR=0.98, 95% CI: 0.97-1.00, p=0.036), and time between onset of symptoms and entry to UHR service (log transformed) (HR=1.10, 95% CI: 1.02-1.19, p=0.013) were predictive of transition to psychosis. UHR individuals who met the brief limited intermittent psychotic symptoms (BLIPS) criteria had a higher probability of transitioning to psychosis than those who met the attenuated psychotic symptoms (APS) criteria (HR=0.48, 95% CI: 0.32-0.73, p=0.001) and those who met the Trait risk criteria (a first-degree relative with a psychotic disorder or a schizotypal personality disorder plus a significant decrease in functioning during the previous year) (HR=0.43, 95% CI: 0.22-0.83, p=0.013). Models based on data from 1995-2016 displayed good calibration at initial model fitting, but showed a drift of 20.2-35.4% in calibration when validated on data from 2017-2020. Large-scale longitudinal data such as those from the UHR 1000+ cohort are required to develop accurate psychosis prediction models. It is critical to assess existing and future risk calculators for temporal drift, that may reduce their utility in clinical practice over time.

中文翻译：

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UHR 1000+队列中超高风险个体向精神病转变的临床预测模型的开发和时间验证


精神病超高风险（UHR）的概念几十年来一直处于精神病学研究的前沿，其最终目标是预防高危人群发生精神病。 Orygen（澳大利亚墨尔本）在这一临床人群中领导了一系列观察和干预研究。这些数据集现已整合到 UHR 1000+ 队列中，该队列由 1,245 名 UHR 个体组成，随访期为 1 至 16.7 年。本文描述了该队列，提出了该队列中向精神病转变的临床预测模型，并研究了 UHR 样本随时间变化如何影响预测性能。我们使用 Cox 比例风险模型分析了向精神病的转变。使用多重插补和鲁宾规则在整个队列中研究了向精神病转变的临床预测因素。为了评估随时间推移的性能漂移，使用 1995-2016 年的数据进行初始模型拟合，随后根据 2017-2020 年的数据验证模型。在随访期间，220 例（17.7%）出现了精神障碍。汇总风险比 (HR) 估计显示，风险心理状态综合评估 (CAARMS) 混乱言语分量表严重程度评分（HR=1.12，95% CI：1.02-1.24，p=0.024）、CAARMS 异常思想内容分量表严重程度评分（HR=1.13，95% CI：1.03-1.24，p=0.009），阴性症状评估量表（SANS）总分（HR=1.02，95% CI：1.00-1.03，p=0.022） 、社会和职业功能评估量表 (SOFAS) 评分（HR=0.98，95% CI：0.97-1.00，p=0.036），以及症状出现和进入 UHR 服务之间的时间（对数转换）（HR=1.10， 95% CI：1.02-1.19，p=0。013）是向精神病转变的预测。符合短暂有限间歇性精神病症状 (BLIPS) 标准的 UHR 个体比符合减轻精神病症状 (APS) 标准的 UHR 个体转变为精神病的可能性更高 (HR=0.48, 95% CI: 0.32-0.73, p=0.001 ）以及符合特质风险标准的人（患有精神障碍或精神分裂型人格障碍的一级亲属，加上前一年功能显着下降）（HR=0.43，95% CI：0.22-0.83，p= 0.013）。基于 1995-2016 年数据的模型在初始模型拟合时显示出良好的校准，但在对 2017-2020 年数据进行验证时显示校准漂移为 20.2-35.4%。开发准确的精神病预测模型需要大规模纵向数据，例如来自 UHR 1000+ 队列的数据。评估现有和未来的时间漂移​​风险计算器至关重要，这可能会随着时间的推移降低其在临床实践中的实用性。