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过早死亡且生活质量较低。尽管有很多人被诊断出患有多种疾病，但临床实践往往在不同的个体疾病领域内进行。已经制定了几种措施试图量化此类合并症负担的总体复杂性，但此类指标无法预测多种结果以帮助指导决策。为此，临床预测模型（CPM）是旨在支持的数学工具/算法通过根据一组有关个人的特征（例如年龄、性别、体重等）预测感兴趣的临床事件发生的可能性来做出临床决策。然而，CPM 也适用于个别疾病，其中使用不同的 CPM 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运作方式，并且对于可能对整个医疗保健和护理计划感兴趣的患者没有帮助，而不是对发展个人/特定病症的风险感兴趣。忽视不同情况之间的关系可能会导致风险估计不足，从而对护理计划和治疗决策产生影响。因此，该提案旨在开发一个“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，这通常是通过预测一个 CPM 的诊断 A 的风险、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。该提案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，这意味着通过了解实际概率（来自 CPM 网络）较高，可以对患者进行不同的管理。有一些新兴的建模技术可以用来制定这样的 CPM 网络，但目前方法论上的挑战阻止它们以这样的方式使用。该提案将解决这些挑战，旨在开发放宽先前建模假设的方法，以允许开发反映更现实和更全面的患者健康和护理观点的 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) 研究 CPM 网络的验证（测试），包括扩展目标 1 中的方法以考虑惩罚/收缩以减轻过度拟合的危险。3) 检查将我们的 CPM 网络应用于概念验证临床示例的可行性：冠心病、心房颤动、中风、慢性肾病和 II 型糖尿病，与传统方法相比。4) 探索通过公众和利益相关者参与沟通 CPM 网络风险的策略，并开发软件来传播 CPM 网络方法。由此产生的一系列潜在应用和好处工作，因为解决多重发病率（患有多种疾病的患者）是 NHS 的首要任务。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。该项目直接挑战了历史上的方法，以产生能够更好地告知护理需求、帮助患者了解未来预后、告知医疗保健专业人员并指导服务提供的模型。

项目成果

Clinical prediction models to predict the risk of multiple binary outcomes: a comparison of approaches.

预测多种二元结果风险的临床预测模型：方法比较。

• DOI: http://dx.10.1002/sim.8787
• 发表时间: 2021
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Martin GP

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

使用多项逻辑回归开发多变量预测模型的最小样本量。

• DOI: http://dx.10.1177/09622802231151220
• 发表时间: 2023
• 期刊: Statistical methods in medical research
• 影响因子: 2.3
• 作者: Pate A

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: http://dx.10.1002/sim.9771
• 发表时间: 2023
• 期刊: Statistics in medicine
• 影响因子: 2
• 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.

遵循最小样本量建议时开发临床预测模型：量化引导变异性在调整参数和预测性能方面的重要性。

• DOI: http://dx.10.1177/09622802211046388
• 发表时间: 2021
• 期刊: Statistical methods in medical research
• 影响因子: 2.3
• 作者: Martin GP

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