Prediction of premature cardiovascular disease (Professor Nathalie Conrad)
PROJECT TITLE
Prediction of premature cardiovascular disease
SUPERVISORS
DESCRIPTION OF PROJECT
Premature cardiovascular disease remains an important cause of morbidity and mortality, yet prediction of premature events (ie. events occurring before the age of 55 years in men or 65 years in women) is suboptimal. Existing risk prediction tools are largely derived from older cohorts and tend to underestimate early risk. Established risk scores such as SCORE2 and PREVENT perform suboptimally in younger populations, and how to meaningfully improve prediction of premature CVD remains unclear.
This project will evaluate the performance of established risk scores (SCORE2 and PREVENT) for predicting premature CVD and assess how recalibration affects hazard estimates and risk stratification. It will investigate whether adding polygenic risk scores, family history, and selected biomarkers (eg. Lp(a) or inflammatory markers), as well as medical history (prior CVD, women-specific factors such as adverse pregnancy outcomes) improves predictive performance.
This project is expected to clarify the performance and limitations of existing risk prediction tools for premature CVD and identify strategies to improve prediction accuracy in younger individuals. Ultimately, the findings may inform more precise, age-appropriate risk prediction approaches, with potential to guide earlier and more targeted prevention.
The analysis will use large-scale linked electronic health record data from sources such as CPRD, alongside cohort data from UK and international biobanks with genetic and biomarker information. The project will require skills in epidemiology and biostatistics, including survival analysis, risk prediction modelling, and model validation, as well as experience working with large EHR and genetic datasets.
TRAINING OPPORTUNITIES
The DPhil student will receive advanced training in epidemiological methods, data processing and data analysis for large-scale biomedical datasets, and biostatistics.
Further to that, the DPhil student will receive training in systematic literature reviews, scientific writing, presentation skills, and will work with a strong inter-disciplinary team of epidemiologists, cardiologists, and statisticians to conduct high-quality research.
Funding Information
The position is not currently funded, however, we will assist the DPhil candidate in securing funding.
HOW TO APPLY
To apply for this research degree, please click here.