The prediction of outcomes in patients at Clinical High Risk for Psychosis (CHR-P) almost exclusively relies on static data obtained at a single snapshot in time (ie, baseline data). Although the CHR-P symptoms are intrinsically evolving over time, available prediction models cannot be dynamically updated to reflect these changes. Hence, the aim of this study was to develop and internally validate a dynamic risk prediction model (joint model) and to implement this model in a user-friendly online risk calculator. Furthermore, we aimed to explore the prognostic performance of extended dynamic risk prediction models and to compare static with dynamic prediction. One hundred ninety-six CHR-P patients were recruited as part of the "Basel Früherkennung von Psychosen" (FePsy) study. Psychopathology and transition to psychosis was assessed at regular intervals for up to 5 years using the Brief Psychiatric Rating Scale-Expanded (BPRS-E). Various specifications of joint models were compared with regard to their cross-validated prognostic performance. We developed and internally validated a joint model that predicts psychosis onset from BPRS-E disorganization and years of education at baseline and BPRS-E positive symptoms during the follow-up with good prognostic performance. The model was implemented as online risk calculator (http://www.fepsy.ch/DPRP/). The use of extended joint models slightly increased the prognostic accuracy compared to basic joint models, and dynamic models showed a higher prognostic accuracy than static models. Our results confirm that extended joint modeling could improve the prediction of psychosis in CHR-P patients. We implemented the first online risk calculator that can dynamically update psychosis risk prediction.

中文翻译：

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动态风险预测模型的开发和验证，以预测临床高风险患者的精神病发作。

临床上患有精神病高风险（CHR-P）的患者对结果的预测几乎完全依赖于在单个快照中及时获得的静态数据（即基线数据）。尽管CHR-P症状在本质上随着时间而发展，但是可用的预测模型无法动态更新以反映这些变化。因此，本研究的目的是开发和内部验证动态风险预测模型（联合模型），并在用户友好的在线风险计算器中实施该模型。此外，我们旨在探索扩展的动态风险预测模型的预后性能，并将静态与动态预测进行比较。作为“ BaselFrüherkennungvon Psychosen”（FePsy）研究的一部分，招募了166名CHR-P患者。使用简短的精神病学分级量表（BPRS-E），定期评估精神病理学和向精神病的过渡情况，长达5年。对关节模型的各种规格进行了交叉验证的预后性能进行了比较。我们开发并内部验证了一种联合模型，该模型可预测因BPRS-E失调和基线受教育年限以及随访期间BPRS-E阳性症状而导致的精神病发作，并具有良好的预后表现。该模型已实现为在线风险计算器（http://www.fepsy.ch/DPRP/）。与基本关节模型相比，扩展关节模型的使用会略微提高预后准确性，而动态模型显示的预后准确性要高于静态模型。我们的结果证实，扩展关节建模可以改善CHR-P患者的精神病预测。我们实施了第一个可以动态更新精神病风险预测的在线风险计算器。