Advanced machine learning guiding cardiovascular therapy decisions in type 2 diabetes patients.

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Recent studies show that 70% percent of all deaths among type 2 diabetes (T2DM)patients are due to cardiovascular disease (CVD) involving coronary heart disease,stroke, heart failure and peripheral arterial disease. CVD treatment in subjects withoutT2DM is guided by the Qrisk3 prognostic rule, however while multiple CVD prognosticrules are available, it is unclear which rule is most appropriate for T2DM patients.Furthermore, it is unclear whether it is possible to develop more advanced rules, jointlypredicting individual CVD elements (e.g., CHD and stroke), while allowing for competingrisk and disease trajectories.As a first step in introducing risk-stratified CVD management in T2DM care I will validateexisting CVD prognostic algorithms on a British sample of 200,000+ T2DM CPRDsubjects. The scope of the project includes exploring average out-of-sampleperformance, as well as presenting stratified results for geographical location, diagnosissetting, disease duration, year of diagnosis and patient type.The next phase of the project focuses on developing novel CVD prediction rules indiabetes patients using advanced competing-risk and multi-stage methodologies.Moreover, we want to leverage genomics data to improve prognostic performance. Thescope of the project also includes exploring the utility of EHR data and real-time analyticsto update CVD rules in diabetes population, designing methods for predicting repeatedepisodes and predicting disease trajectory.