The use of clinical prediction models to produce individualized risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implement them in routine clinical care. The current review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number needed to test). We review 4 externally validated clinical prediction models designed to predict psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models and the potential added value of integrating data from evidence syntheses, standardized psychometric assessments, and biological data into EHRs. Clinical prediction models can utilize routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g., meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve the performance of clinical prediction models.

中文翻译：

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使用电子健康记录促进精准精神病学

使用临床预测模型进行个体化风险评估可以促进精准精神病学的实施。作为具有临床代表性的大型患者样本的数据来源，电子健康记录 (EHR) 提供了一个开发和验证临床预测模型的平台，并有可能在常规临床护理中实施它们。当前的评论描述了将精准精神病学应用于 EHR 数据的有前途的用例，并考虑了它们在歧视（区分有结果和没有结果的个体的能力）和校准（预测风险估计与观察结果相对应的程度）方面的表现，以及它们潜在的临床效用（与需要测试的数量的不同假设的不同方法相比，权衡与模型相关的收益和成本）。我们回顾了 4 个经过外部验证的临床预测模型，旨在预测精神病发作、精神病复发、心脏代谢发病率和自杀风险。然后，我们讨论临床实施这些模型的前景，以及将证据合成、标准化心理测量评估和生物数据整合到 EHR 中的数据的潜在附加值。临床预测模型可以以创新的方式利用常规收集的 EHR 数据，这是为现实世界的临床决策提供信息的独特机会。将其他来源的数据（例如荟萃分析）相结合，或将 EHR 数据与研究信息（临床和生物标志物数据）相结合，可以增强我们改善临床预测模型性能的能力。