Improving outcomes in critically ill patients using novel high dimensional multimodal data-driven innovation

My vision is to revolutionise the care of Intensive Care (ICU) patients by harnessing novel approaches in the use of technology and high dimensional data. ICU patient populations are heterogeneous and differ widely in their disease processes and demographics, and multiple interventions in ICU with biological plausibility and promising laboratory data have failed to show any signal for benefit or harm when trialled in large ICU populations. I have shown that myocardial infarction (MI) occurs in 25% of high-risk patients admitted to ICU and is on the causal pathway to mortality, yet is clinically recognised in fewer than 5% of events. I will use continuous cardiac monitoring for the first time in critically unwell patients to systematically diagnose MI. I aim to identify patients at high risk of MI or death based on the interplay between predisposing patient factors such as multimorbidity and functional status, and precipitating factors such as diagnosis and illness severity. Potential individualised interventions to prevent and/or treat MI in critically ill patients will be developed and delivered on a platform that is both time- and resource-efficient, to improve survival and quality of life in critically ill patients.