PRedicting cardiOvascular risk and its Management In SEvere mental illness (PROMISE)

Context This project extends a programme of work from stage one of my Future Leaders Fellowship (FLF1), designed to exploit electronic health records (EHRs) to improve the health of people with severe mental illness (SMI). People with SMI (schizophrenia, bipolar disorder, psychotic illness and major depression) have a life expectancy shortened by 10-20 years compared to the general population. The majority of this reduced life expectancy is due to increased risk of cardiovascular disease (CVD) related mortality. Evidence suggests that people with SMI are less likely to have their CVD risk managed, less likely to receive a timely CVD diagnosis and less likely to receive intensive interventions compared to the general population. These issues were also raised by my lived experience advisors. Improved CVD screening and risk prediction could mitigate some of these inequities. An NHS Long-Term Plan commitment and part of Core20PLUS5 is that people living with SMI will receive an annual physical health check. However, these need to be fit for purpose, calculate risk accurately and be acted upon if we are to move the needle on this major health inequality. Challenge this project addresses A large number of CVD risk prediction tools exist for use in the general population. There are SMI-specific risk calculators, including QRISK3 and PRIMROSE. However, the Framingham risk score and QRISK2, which were developed in non-SMI populations, remain the most commonly applied to this population in clinical practice. Current risk calculators, including those specially designed and re-weighted for SMI may still over- or under-estimate risk. The National Institute for Health and Care Excellence recommend a 10% ten-year risk threshold for initiating preventative statin therapy, however it is not clear whether this is the optimal threshold for modifying risk in SMI. These calculators are not routinely used in primary care or secondary mental healthcare settings in the United Kingdom (UK) and are potentially outdated. Aim To assess the performance of CVD risk calculators at scale in SMI populations, modify them to optimise CVD risk prediction, and reduce CVD by implementing the new model. Objectives Test the accuracy of existing CVD risk calculators Determine if preventative medication is prescribed in line with risk calculator results in SMI populations Determine if recalibration, machine learning models, changes in predictor variables or modified thresholds for intervention improve CVD risk prediction in SMI Pilot the implementation of the new model in mental health services Investigate usability, facilitators and barriers to uptake Potential applications and benefits We will confirm the most accurate risk tool for use in SMI populations in the UK. We will determine how this tool can be used in clinical practice, with potential integration into EHR systems. This tool will feed in to health checks that form part of the NHS Long-Term Plan via my links with Office for Health Improvement and Disparities. Increased appropriate prescribing of statin's and other preventative medications for cardiometabolic health will reduce morbidity and mortality in SMI populations.