HOD2: Toward Holistic Approaches to Clinical Prediction of Multi-Morbidity: A Dynamic Synergy of Inter-Connected Risk Models

患有多种疾病的人更有可能过早死亡并且生活质量较低。尽管被诊断患有多种疾病的人数很多，但临床实践往往在不同的个人条件领域内运作。已经开发了几种措施，试图量化这种合并症负担的总体复杂性，但这些指标无法预测多种结果，以帮助指导决策。临床预测模型（CPM）是一种数学工具/算法，旨在通过预测在给定一组关于个体的特征的情况下感兴趣的临床事件发生的可能性来支持临床决策（例如，他们的年龄、性别、体重等）。然而，CPM也适用于个别疾病，其中不同的CPM用于预测单一不良临床结果的可能性。然而，这未能尊重医疗实践的工作方式，并且对可能对他们的整个医疗保健和护理计划感兴趣的患者没有帮助，而不是发展个人/特定条件的风险。忽视不同疾病之间的关系可能导致对风险的低估，这可能对护理计划和治疗决策产生影响。因此，本提案旨在开发“CPM网络”环境，在该环境中将开发模型以预测患者发生不同（但可能相关）事件的可能性。例如，这通常通过预测来自一个CPM的诊断A的风险、来自另一个CPM的诊断B的风险，然后通过假设诊断彼此不相关（独立）来组合这些风险来实现。这个建议的关键点是，这些不是独立的事件，我们的CPM网络将适当地捕捉这一点。在临床上，这意味着通过了解实际概率（来自CPM网络）更高，将以不同的方式管理患者。有一些新的建模技术可用于建立这样一个国家预防机制网络，但目前在方法上的挑战使它们无法用于这一能力。该提案将应对这些挑战，旨在开发放松先前建模假设的方法，以允许开发反映患者健康和护理的更现实和全面观点的CPM。在该项目中，我们有以下目标：1）开发同时适合多个CPM的方法，以允许CPM以计算上可行的方式预测不同疾病领域的多个事件的风险。2）调查CPM网络的验证（测试），包括从目标1扩展方法，考虑惩罚/收缩，以减轻过度拟合的危险。3）检查将我们的CPM网络应用于以下概念验证临床实例的可行性：冠心病、房颤、中风、慢性肾病和II型糖尿病。4）通过公众和利益相关者的参与，探索CPM网络的风险沟通策略，这项工作有一系列潜在的应用和好处，因为解决多种疾病（患有多种疾病的病人）是国民保健服务的一个高度优先事项。例如，通过CPM网络准确预测多发病风险可以通过适当的多发病规划来帮助临床决策。该项目直接挑战历史方法，以产生能够更好地告知护理需求的模型，帮助患者了解未来预后，告知医疗保健专业人员并指导服务提供。

Individuals with multiple medical conditions are more likely to die earlier and have lower quality of life. Despite the high number of people who are diagnosed with multiple conditions, clinical practice tends to operate within distinct areas of individual conditions. Several measures have been developed that attempt to quantify the overall complexity of such comorbidity burden, but such metrics cannot predict multiple outcomes to help guide decision-making.To this end, clinical prediction models (CPMs) are mathematical tools/algorithms that aim to support clinical decision-making by predicting the likelihood that a clinical event of interest will occur given a set of characteristics about the individual (e.g. their age, gender, weight, etc.). However, CPMs also operate in pockets of individual diseases, where different CPMs are made to predict the likelihood of a single adverse clinical outcome. However, this fails to respect the way medical practice works and is unhelpful for the patient who is likely interested in their whole healthcare and care planning, rather than risks of developing individual/specific conditions. Ignoring the relationships between different conditions can lead to an under-estimation of risk, which can have consequences for care-planning and treatment decision-making. Therefore, this proposal will aim to develop a "CPM-Network" environment, where models will be developed to predict the likelihood of a patient developing different (but potentially related) events. For example, this is classically achieved by predicting the risk of diagnosis A from one CPM, the risk of diagnosis B from another CPM, and then combining these risks by assuming the diagnoses are not related to each other (independent). The key point of this proposal is that these are not independent events, and our CPM-Network will capture this appropriately. Clinically, this means that patients will be managed differently by knowing that the actual probabilities (from the CPM-Network) are higher. There are emerging modelling techniques that can be used to formulate such a CPM-Network, but methodological challenges currently prohibit them being used in such a capacity. This proposal will address these challenges and aim to develop methods that relax previous modelling assumptions, to allow development of CPMs that reflect a more realistic and holistic view of a patient's health and care.In this project, we have the following objectives:1) To develop methods that fit multiple CPMs simultaneously to allow CPMs to predict risks of multiple events across different disease areas in a computationally feasible manner.2) Investigate validation (testing) of CPM-Networks, including extending methods from Objective 1 to consider penalisation/shrinkage to mitigate the dangers of overfitting.3) To examine the feasibility of applying our CPM-Network to proof-of-concept clinical examples of: coronary heart disease, atrial fibrillation, stroke, chronic kidney disease and type-II diabetes mellitus, compared to conventional approaches.4) Explore strategies for communicating risks from a CPM-Network through public and stakeholder engagement, and develop software to disseminate the CPM-Network approach.There are a range of potential applications and benefits arising from this work, since tackling multi-morbidity (patients with multiple medical conditions) is a high priority for the NHS. For example, accurately predicting multi-morbid risk through a CPM-Network can aid clinical decision-making through appropriate multi-morbidity planning. This project directly challenges historic approaches to doing this, to produce models that can better inform care needs, aid patients understand future prognosis, inform healthcare professionals, and guide service provision.