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

HOD2：采用整体方法进行多种发病率的临床预测：相互关联的风险模型的动态协同作用

• 批准号: MR/T025085/1
• 负责人: Glen Martin
• 金额: $ 61.83万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FT025085%2F1
• 关键词: HOD2; Toward; Holistic; Approaches; Clinical

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.

患有多种疾病的人更有可能更早死亡，生活质量更低。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别情况下进行。已经开发了几种方法来试图量化这种共病负担的总体复杂性，但这种方法不能预测多个结果来帮助指导决策。为此，临床预测模型(CPM)是旨在通过在给定一组关于个人的特征(例如，他们的年龄、性别、体重等)的情况下预测感兴趣的临床事件将发生的可能性来支持临床决策的数学工具/算法。然而，CPM也在个别疾病的口袋中运作，在那里，不同的CPM被用来预测单一不良临床结果的可能性。然而，这不尊重医疗实践的工作方式，对于那些可能对整个医疗保健和护理计划感兴趣的患者没有帮助，而不是对个人/特定疾病的风险感兴趣。忽视不同病情之间的关系可能会导致低估风险，这可能会对护理计划和治疗决策产生影响。因此，这项建议旨在开发一个“CPM-网络”环境，其中将开发模型来预测患者发生不同(但可能相关)事件的可能性。例如，这通常是通过预测来自一个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的。这一建议的关键点是，这些不是独立的事件，我们的CPM网络将适当地捕捉到这一点。在临床上，这意味着患者将得到不同的管理，因为他们知道实际的概率(来自CPM-Network)更高。有一些新出现的建模技术可以用来制定这样一个CPM网络，但目前方法学方面的挑战阻碍了它们在这方面的使用。在这个项目中，我们有以下目标：1)开发同时适合多个CPM的方法，以允许CPM以计算可行的方式预测跨越不同疾病区域的多个事件的风险。2)调查CPM网络的验证(测试)，包括将目标1的方法扩展到考虑惩罚/收缩，以减轻过度配备的危险。3)与传统方法相比，检查将我们的CPM网络应用于概念验证临床实例的可行性：冠心病、房颤、中风、慢性肾脏疾病和II型糖尿病。4)探索通过公众和利益相关者参与来传播CPM网络风险的策略，并开发软件来传播CPM网络方法。这项工作有一系列潜在的应用和好处，因为解决多种发病率(患有多种疾病的患者)是NHS的高度优先事项。例如，通过CPM网络准确地预测多种疾病的风险，可以通过适当的多种疾病规划来辅助临床决策。该项目直接挑战了实现这一目标的历史性方法，以产生能够更好地告知护理需求、帮助患者了解未来预后、告知医疗专业人员并指导服务提供的模型。

项目成果

Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small.

• DOI: 10.1016/j.jclinepi.2020.12.005
• 发表时间: 2021-04
• 期刊: Journal of clinical epidemiology
• 影响因子: 7.2
• 作者: Riley RD;Snell KIE;Martin GP;Whittle R;Archer L;Sperrin M;Collins GS
• 通讯作者: Collins GS

Developing prediction models to estimate the risk of two survival outcomes both occurring: A comparison of techniques.

开发预测模型来估计两种生存结果同时发生的风险：技术比较。

• DOI: 10.1002/sim.9771
• 发表时间: 2023
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Pate A

Calibration plots for multistate risk predictions models: an overview and simulation comparing novel approaches

多状态风险预测模型的校准图：比较新方法的概述和模拟

• DOI: 10.48550/arxiv.2308.13394
• 发表时间: 2023
• 作者: Pate A

Developing clinical prediction models when adhering to minimum sample size recommendations: The importance of quantifying bootstrap variability in tuning parameters and predictive performance.

• DOI: 10.1177/09622802211046388
• 发表时间: 2021-12
• 期刊: Statistical methods in medical research
• 影响因子: 2.3
• 作者: Martin GP;Riley RD;Collins GS;Sperrin M
• 通讯作者: Sperrin M

Minimum sample size for developing a multivariable prediction model using multinomial logistic regression.

• DOI: 10.1177/09622802231151220
• 发表时间: 2023-03
• 期刊: STATISTICAL METHODS IN MEDICAL RESEARCH
• 影响因子: 2.3
• 作者: Pate, Alexander;Riley, Richard D.;Collins, Gary S.;van Smeden, Maarten;Van Calster, Ben;Ensor, Joie;Martin, Glen P.
• 通讯作者: Martin, Glen P.