UHR 1000+ 队列中超高风险人群向精神病过渡的临床预测模型的开发和时间验证

IF 73.3 1区 医学 Q1 Medicine World Psychiatry Pub Date : 2024-09-16 DOI:10.1002/wps.21240
Simon Hartmann, Dominic Dwyer, Blake Cavve, Enda M. Byrne, Isabelle Scott, Caroline Gao, Cassandra Wannan, Hok Pan Yuen, Jessica Hartmann, Ashleigh Lin, Stephen J. Wood, Johanna T.W. Wigman, Christel M. Middeldorp, Andrew Thompson, Paul Amminger, Monika Schlögelhofer, Anita Riecher-Rössler, Eric Y.H. Chen, Ian B. Hickie, Lisa J. Phillips, Miriam R. Schäfer, Nilufar Mossaheb, Stefan Smesny, Gregor Berger, Lieuwe de Haan, Merete Nordentoft, Swapna Verma, Dorien H. Nieman, Patrick D. McGorry, Alison R. Yung, Scott R. Clark, Barnaby Nelson
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引用次数: 0

摘要

几十年来,精神病超高风险(UHR)的概念一直处于精神病学研究的前沿,其最终目标是预防高风险人群中精神病性障碍的发生。Orygen 公司(澳大利亚墨尔本)在这一临床人群中开展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 患者组成,随访时间从 1 年到 16.7 年不等。本文介绍了该队列,提出了该队列中向精神病过渡的临床预测模型,并研究了预测性能如何受到 UHR 样本随时间推移而发生的变化的影响。我们使用 Cox 比例危险模型分析了向精神病过渡的情况。我们使用多重估算和鲁宾法则调查了整个队列中精神病转归的临床预测因素。为评估随时间推移的性能漂移,1995-2016年的数据被用于初始模型拟合,随后模型在2017-2020年的数据中得到验证。在随访期间,220 例(17.7%)患者出现了精神障碍。汇总的危险比(HR)估计值显示,高危精神状态综合评估(CAARMS)言语紊乱子量表严重程度评分(HR=1.12,95% CI:1.02-1.24,P=0.024)、CAARMS异常思维内容子量表严重程度评分(HR=1.13,95% CI:1.03-1.24,P=0.009)、阴性症状评估量表(SANS)总评分(HR=1.02,95% CI:1.00-1.03,p=0.022)、社会与职业功能评估量表(SOFAS)得分(HR=0.98,95% CI:0.97-1.00,p=0.036)以及症状出现与进入 UHR 服务之间的时间(对数转换)(HR=1.10,95% CI:1.02-1.19,p=0.013)均可预测向精神病的转变。符合短暂局限性间歇性精神病性症状(BLIPS)标准的 UHR 患者比符合减轻精神病性症状(APS)标准的患者(HR=0.48,95% CI:0.32-0.73,p=0.001)和符合特质风险标准(直系亲属患有精神病性障碍或分裂型人格障碍,且前一年的功能显著下降)者(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性,但在对2017-2020年的数据进行验证时,校准性出现了20.2%-35.4%的漂移。要开发准确的精神病预测模型,需要大规模的纵向数据,如来自 UHR 1000+ 队列的数据。评估现有和未来风险计算器的时间漂移至关重要,因为随着时间的推移,时间漂移可能会降低其在临床实践中的效用。
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Development and temporal validation of a clinical prediction model of transition to psychosis in individuals at ultra-high risk in the UHR 1000+ cohort
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.
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来源期刊
World Psychiatry
World Psychiatry Nursing-Psychiatric Mental Health
CiteScore
64.10
自引率
7.40%
发文量
124
期刊介绍: World Psychiatry is the official journal of the World Psychiatric Association. It aims to disseminate information on significant clinical, service, and research developments in the mental health field. World Psychiatry is published three times per year and is sent free of charge to psychiatrists.The recipient psychiatrists' names and addresses are provided by WPA member societies and sections.The language used in the journal is designed to be understandable by the majority of mental health professionals worldwide.
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