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

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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.