Methodology to support the development of prognostic models that incorporate and inform the observation processes within electronic health records

人们非常有兴趣利用患者电子健康记录(EHR)提供的丰富数据来开发临床预测模型(CPM)，根据我们现在对他们的了解来预测一个人是否会在未来的某个时间点发生事件[1，2]。CPMS既可以支持循证决策，也可以支持最近对精准医学的推动，但在EHR中建模时仍然存在挑战。具体地说，EHR通常通过与卫生服务机构的反复接触包含关于患者的纵向信息，但观察时间和测量频率在患者内部和患者之间会有很大差异。例如，假设CPM使用实验室结果和血压来预测患者被转移到重症监护病房(ICU)的实时风险，从而促进在定期医院就诊期间进行早期预警。虽然患者的EHR可能包含定期的血压测量，但他们的实验室结果可能较少被观察到。该项目不是试图在给定的时间点归因于缺失的风险因素(例如，实验室结果)，而是开发允许CPM在它们不存在的情况下进行实时预测的方法(例如，仅使用血压进行预测)，然后潜在地为某些患者‘请求’额外的信息。这种“互动测量”将有助于有针对性的高频数据收集，以帮助那些有不良后果的高危人群(例如，在ICU转院过程中病情恶化)。同样，如果额外的信息(例如实验室结果)是先验的，那么这将反映临床医生对患者的信念，因为观察频率包含关于患者潜在健康状况的信息--所谓的信息性观察[3]。将观察过程和结果过程结合在CPM中很少在CPM中考虑，但可以让他们从临床判断中学习[4，5]。因此，这个项目将：(I)探索当前将观察过程纳入CPM发展的方法，(Ii)开发方法，允许CPM处理不同的风险因素测量频率，同时提供交互测量，以及(Iii)研究交互测量和信息性观察之间的关系。

There is substantial interest in using the abundance of data available through patients' electronic health records (EHRs) to develop clinical prediction models (CPMs) that predict whether a person will have an event at some future time point based on what we know about them now [1,2]. CPMs can support both evidence-based decision-making and the recent drive for precision medicine, but challenges remain when modelling within EHRs. Specifically, EHRs usually contain longitudinal information about a patient, through repeated contact with health services, but the observation times and frequency of measurement will vary considerably within and across patients. For example, suppose a CPM uses lab results and blood pressure to predict the real-time risk of a patient being transferred to an intensive care unit (ICU), thereby facilitating early warning during regular hospital visits. While a patients' EHR might contain regular blood pressure measurements, their lab results might be observed less frequently. Rather than trying to impute missing risk factors (e.g. lab results) at a given time point, this project will develop methods that allow the CPM to make real-time predictions in their absence (e.g. predict using only blood pressure), and then potentially 'request' additional information for certain patients. Such 'interactive measurement' would facilitate targeted high-frequency data collection in those at high-risk of adverse outcome (e.g. deterioration towards ICU transfer). Likewise, if the additional information (e.g. lab results) were available a-priori, then this would reflect the clinician's beliefs about a patient since the observation frequency contains information on the patient's underlying health status - so-called informative observation [3]. Incorporating the observation process and the outcome process within CPMs is rarely considered within CPMs, but could allow them to learn from clinical judgements [4,5]. Therefore, this project will: (i) explore the current approaches of incorporating observation processes within CPM development, (ii) develop methods that allow CPMs to handle heterogeneous risk factor measurement frequency, while concurrently informing interactive measurements, and (iii) study the relationships between interactive measurement and informative observation.For clinical examples, we intend to focus on: 1) discharge versus care escalation in a critical care (hospital) context, and 2) prediction of disease incidence in primary care.