Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
OASIS Service, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom.
JAMA Psychiatry. 2018 Dec 1;75(12):1289-1297. doi: 10.1001/jamapsychiatry.2018.2530.
Prognosis is a venerable component of medical knowledge introduced by Hippocrates (460-377 BC). This educational review presents a contemporary evidence-based approach for how to incorporate clinical risk prediction models in modern psychiatry. The article is organized around key methodological themes most relevant for the science of prognosis in psychiatry. Within each theme, the article highlights key challenges and makes pragmatic recommendations to improve scientific understanding of prognosis in psychiatry.
The initial step to building clinical risk prediction models that can affect psychiatric care involves designing the model: preparation of the protocol and definition of the outcomes and of the statistical methods (theme 1). Further initial steps involve carefully selecting the predictors, preparing the data, and developing the model in these data. A subsequent step is the validation of the model to accurately test its generalizability (theme 2). The next consideration is that the accuracy of the clinical prediction model is affected by the incidence of the psychiatric condition under investigation (theme 3). Eventually, clinical prediction models need to be implemented in real-world clinical routine, and this is usually the most challenging step (theme 4). Advanced methods such as machine learning approaches can overcome some problems that undermine the previous steps (theme 5). The relevance of each of these themes to current clinical risk prediction modeling in psychiatry is discussed and recommendations are given.
Together, these perspectives intend to contribute to an integrative, evidence-based science of prognosis in psychiatry. By focusing on the outcome of the individuals, rather than on the disease, clinical risk prediction modeling can become the cornerstone for a scientific and personalized psychiatry.
预后是希波克拉底（公元前 460-377 年）引入的医学知识中一个令人尊敬的组成部分。这篇教育综述介绍了一种当代基于证据的方法，用于将临床风险预测模型纳入现代精神病学。本文围绕与精神病学预后科学最相关的关键方法学主题进行组织。在每个主题中，本文强调了关键挑战，并提出了切实可行的建议，以提高对精神病学预后的科学理解。
构建能够影响精神科护理的临床风险预测模型的初始步骤涉及模型设计：制定方案，定义结局和统计方法（主题 1）。进一步的初始步骤包括仔细选择预测因子、准备数据以及在这些数据中开发模型。接下来的步骤是验证模型，以准确测试其通用性（主题 2）。下一个需要考虑的是，临床预测模型的准确性受到所研究的精神状况的发生率的影响（主题 3）。最终，临床预测模型需要在现实临床常规中实施，这通常是最具挑战性的步骤（主题 4）。机器学习方法等先进方法可以克服破坏前几个步骤的一些问题（主题 5）。本文讨论了这些主题对当前精神病学中临床风险预测建模的相关性，并提出了建议。
总之，这些观点旨在为精神病学的预后综合、基于证据的科学做出贡献。通过关注个体的结局而不是疾病，临床风险预测模型可以成为科学和个性化精神病学的基石。
