Public Funding for Pharmatics Limited

Hypertrophic cardiomyopathy (HCM) is the most common inherited heart disease (prevalence 1:200 - 1:500), manifested by thickening of cardiac walls, increasing risks of arrhythmia, and sudden cardiac death. HCM affects all ages - it is the leading cause of death among young athletes. Comorbidities due to gene mutations include altered vascular control, and, caused by HCM, ischemia, stroke, dementia, or psychological and social difficulties. Multiple causal mutations and variations in cellular processes lead to highly diverse phenotypes and disease progression. However, HCM is still diagnosed as one single disease, leading to suboptimal care. SMASH-HCM will develop a digital-twin platform to dramatically improve HCM stratification and disease management, both for clinicians and patients. Multilevel and multiorgan dynamic biophysical and data-driven models are integrated in a three-level deep phenotyping approach designed for fast uptake into the clinical workflow. SMASH-HCM unites 8 research partners, 3 hospitals, 3 SMEs, and a global health-technology corporation in collaboration with patients to advance the state of the art in human digital-twins: including in-vitro tools, in-silico from molecular to systemic level models, structured and unstructured data analysis, explainable artificial intelligence - all integrated into a decision support solution for both healthcare professionals and patients. SMASH-HCM delivers new insights into HCM, improved patient care and guidance, validated preclinical tools, and above all, a first HCM stratification and management strategy, validated in a pilot clinical trial, and tested with end users. Thus providing a cost efficient and effective solution for this complex disease. SMASHHCM develops a strategy towards fast regulatory approval. In reaching its goals, SMASH-HCM serves as a basis for future digital-twin platforms for other cardiac diseases integrating models and data from various scales and sources.

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Awaiting Public Project Summary

Long-term conditions account for 70% of total medical spending in the UK, and up to 90% in the US. The costs are set to rise as the population is ageing rapidly. Unless there is a change in how long-term conditions are managed, healthcare will become less accessible and more expensive. A solution to this may be self-management, where patients or their carers recognise symptoms and learn how to act on them promptly. We will develop an innovative approach that uses intelligent algorithms to help patients to self-manage better. Initially, we will focus on Chronic Obstructive Pulmonary Disease (COPD) - a progressive lung disease and the third leading cause of death worldwide. In many countries, COPD has already surpassed chronic heart disease as the main cause of hospital admissions. About 30% of patients discharged after a hospital admission for COPD die within 180 days, and about 50% die within two years. COPD kills more people than the second deadliest cancer, and it kills as many UK women as the deadliest cancer. And while death rates from other major causes are declining, COPD is on the rise. Warning signs and self-management plans that work for one person with COPD may not work for others, and it is now widely recognized that self-management of COPD needs to be an ongoing process individualised to each patient in order to be successful. Unfortunately, primary care providers are often overworked and have insufficient time and resources to provide tailored continual support, which reduces self-management programmes to one-off one-size-fits-all "things" given to patients. Our project will develop artificial intelligence (AI) that will adapt to patients and help them to self-manage better. Patients will be able to receive algorithmic feedback on their self-management techniques 24/7, without having to go to hospital or wait for appointments. Healthcare professionals will be able to make adjustments to self-management plans if they receive warnings from algorithms. A part of the project will help COPD patients to cope with anxiety. Our approach will put citizens in control of the illness, and encourage them to engage in evidence-based risk avoidance strategies approved by clinicians. This may both improve patient outcomes and make healthcare more sustainable. If our approach is successful, it can be extended to many other long-term diseases.

The frequency of long-term illness worldwide is set to double by 2030, putting a strain on healthcare providers. In some settings, telehealth-based chronic disease management technologies were reported to have remarkable success a solution to this problem. However, other major trials suggest that there is no convincing evidence showing that telehealth saves resources or improves quality of life, as the currently used algorithms often cause an increased workload through generation of multiple false alerts. This was shown for Chronic Obstructive Pulmonary Disease (COPD), which is one of the major causes of emergency admissions and mortality in the UK. More effective approaches to treatment and management of chronic conditions are urgently needed. The aim of our project is to investigate feasibility and potential of artificial intelligence (AI) algorithms for improving care, treatment, and management of COPD, using predictions of future exacerbations and hospital admissions to enable pre-emptive actions before a patient has an emergency. Although initially exemplified by COPD, we envisage that our AI-powered intelligent telehealth platform will be able to adapt to other chronic diseases, as well as the needs of patients with multiple diseases and comorbidities.