Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice.
Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice.
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DOI：
10.1093/schbul/sbaa120
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发表时间：
2021-03-16
影响因子：
6.6
通讯作者：
Fusar-Poli P
中科院分区：
文献类型：
--
作者：
Salazar de Pablo G;Studerus E;Vaquerizo-Serrano J;Irving J;Catalan A;Oliver D;Baldwin H;Danese A;Fazel S;Steyerberg EW;Stahl D;Fusar-Poli P
精确精神病学对临床实践的影响还没有得到系统的评估。本研究旨在对已验证的预测模型进行全面综述，以估计精神障碍患者受到某种疾病(诊断)、发展结果(预后)或对治疗(预测性)反应的个体风险。符合Prisma/Right/Charms标准的科学网、Cochrane中央综述和Ovid/JuncINFO数据库从成立到2019年7月21日的系统审查(Propero CRD42019155713)，以确定报告精神病学个性化估计并经内部或外部验证或实施的诊断/预后/预测研究。随机效应元回归分析处理了几个因素对预测模型准确性的影响。文献检索共收集到584篇预测建模研究，其中包括。10.4%的研究包括内部验证的预测模型(n=61)，4.6%的外部验证的模型(n=27)，以及考虑实施的0.2%的模型(n=1)。在已验证的预测模型研究中(n=88)，18.2%为诊断性，68.2%为预后，13.6%为预测性。最常调查的疾病是精神病(36.4%)，最常使用的预测指标是临床(69.5%)。与多模式模型相比，单模式模型(β=.29，P=.03)以及与预后模型(β=.84，P<；.0001)和预测性模型(β=.87，P=.002)相比的诊断性模型与更高的准确性相关。到目前为止，已有几个有效的预测模型可用于支持精神疾病的诊断和预后，特别是精神病，或预测治疗反应。在现实世界的临床实践中，缺乏实施性研究限制了知识的进步。需要新一代实施研究来解决这一翻译差距。
The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders. PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models. Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (β = .29, P = .03) and diagnostic compared to prognostic (β = .84, p < .0001) and predictive (β = .87, P = .002) models were associated with increased accuracy. To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.
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