Early Psychosis: Interventions and Clinical-detection Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, London, UK.
Institute of Psychiatry and Mental Health, Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón School of Medicine, Universidad Complutense, Instituto de Investigación Sanitaria Gregorio Marañón, CIBERSAM, Madrid, Spain.
Schizophr Bull. 2021 Mar 16;47(2):284-297. doi: 10.1093/schbul/sbaa120.
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.
精准精神病学对临床实践的影响尚未得到系统评估。本研究旨在全面综述已验证的预测模型，以评估个体患有精神障碍的风险（诊断）、发展结局（预后）或对治疗的反应（预测）。
符合 PRISMA/RIGHT/CHARMS 标准的系统综述，检索了 Web of Science、Cochrane 中央评价数据库和 Ovid/PsycINFO 数据库，检索时间从建库至 2019 年 7 月 21 日（PROSPERO CRD42019155713），以确定在精神病学中报告个体化估计值并进行内部或外部验证或实施的诊断/预后/预测预测研究。随机效应荟萃回归分析探讨了多个因素对预测模型准确性的影响。
文献检索共确定了 584 项预测建模研究，其中 89 项被纳入。总研究中，10.4%（n=61）的研究包括内部验证的预测模型，4.6%（n=27）的研究包括外部验证的预测模型，0.2%（n=1）的研究考虑实施。在已验证的预测建模研究（n=88）中，18.2%为诊断性，68.2%为预后性，13.6%为预测性。研究最多的疾病是精神病（36.4%），最常使用的预测指标是临床指标（69.5%）。与单模态模型相比，多模态模型（β=0.29，P=0.03），与预后模型相比，诊断模型（β=0.84，P<0.0001）和预测模型（β=0.87，P=0.002）准确性更高。
目前，有几个已验证的预测模型可用于支持精神障碍的诊断和预后，特别是精神病，或预测治疗反应。知识的进步受到现实临床实践中缺乏实施研究的限制。需要开展新一代的实施研究来解决这一转化差距。
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