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Many major health problems, such as heart disease and cancers, are still not effectively prevented and managed. This can be improved by ‘stratifying’ people in ways that better identify their risk, or likely benefit from an intervention or treatment.
Our research develops and translates new stratified approaches into primary care – where over 90% of health care is delivered.
Professor Nadeem Qureshi and Professor Joe Kai
Co-leads of the Primary Care Stratified Medicine Research Group
Medicine and health care often use a ‘one size fits all’ approach to treatment or care. This means many treatments are only effective in 30-60% of people receiving them, while others may not benefit at all.
Better and more equitable healthcare can be achieved by improved understanding of people’s diversity – such as genetic, social or ethnic variation.
Our ‘stratified medicine’ research aims to better identify people, or groups of people at risk of disease, and their response to an intervention or treatment.
We do this by developing and using emerging techniques – such as applying advances in genomics or data science.
We also research how to use these techniques in ‘real life’ health care practice with patients, their GPs and other community healthcare settings. This is ‘translational research'.
We evaluate these approaches in diverse communities to help achieve more equitable (fairer) benefit from advances in care.
Both stratification and translation are required for patients and health services to benefit from better-targeted and effective interventions in prevention and management of common conditions.
This enables selection of the ‘right treatment at the right time’ for individuals.
Having a ‘stratified’ approach can also help earlier diagnosis, screening and treatment, or smarter monitoring, in more cost-effective ways.
Our focus is on major health problems we can address in primary care - such as cardiovascular disease, cancers and common inherited disorders.
Patients identified with raised cholesterol provides an exemplar of our primary care stratified medicine research: interrogating primary care databases has enabled us to stratify patients into those with inherited lipid disorders and multifactorial risk factors for cardiovascular disease, whilst genomic testing enables us to confirm those with severe familial hypercholesterolaemia and milder polygenic conditions.
Also, the database research combined with further sequencing of genome, will stratify patients by response to different lipid lowering therapies. We have also translated these techniques into actual primary care clinical practice using mixed method research.
Pharmacogenomics stratifying drug response to statins using the Clinical Practice Research Datalink (CPRD).
Machine-learning using neural networks, deep learning, ensemble learning, random forests, gradient boosting for improving diagnosis of cardiovascular disease using the Clinical Practice Research Datalink (CPRD).
Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS ONE 2017;12(4):e0174944
Argumentation from patients stratified for familial breast cancer data derived from general practice IT systems and research databases.
Common Inherited Disorders
First study using primary care electronic health records to investigate the association between Sickle Cell Trait and thrombotic risk.
Risk stratification of familial hypercholesterolaemia using the Clinical Practice Research Datalink.
External validation of the familial hypercholesterolaemia case finding stratification tool using UK General Practice databases.
Little I, Vinogradova Y, Orton E, Kai J, Qureshi N. Venous thromboembolism in adults screened for sickle cell trait: a population-based cohort study with nested case–control analysis. BMJ Open 2017;7(3):e012665 doi:10.1136/bmjopen-2016-012665
Weng SF, Kai J, Neil HA, Humphries SE, Qureshi N. Improving identification of familial hypercholesterolaemia in primary care: Derivation and validation of the familial hypercholesterolaemia case ascertainment tool (FAMCAT). Atherosclerosis 2015;238(2):336-43 doi: http://dx.doi.org/10.1016/j.atherosclerosis.2014.12.034
The first randomised control trial (ADDFAM) which showed that simple family history assessment collection by GPs can improve risk stratification of future risk of heart disease in primary care.
Qureshi N, Armstrong S, Dhiman P, Saukko P, Middlemass J, Evans PH, Kai J, for the ADDFAM (Added Value of Family History in CVD RIsk Assessment) Study Group. Effect of adding systematic family history enquiry to cardiovascular disease risk assessment in Primary Care. Annals of Internal Medicine 2012; 156(4):253-262
Related editorial: Berg AO. Family History gets a boost. Annals of Internal Medicine 2012; 156: 315-31
The first mixed methods study (ADDGEN) assessing acceptability of cardiovascular genetic testing in primary care.
Qureshi N, Kai J, Middlemass J, Dhiman P, Cross-Bardell L, Acharya J, Li KW, Humphries SE, Standen PJ. Comparison of coronary heart disease genetic assessment with conventional cardiovascular risk assessment in primary care: reflections on a feasibility study. Primary Health Care Research & Development 2015; 16(6):607-617 doi: 10.1017/S1463423615000122
Middlemass JB, Yazdani MF, Kai J, Standen PJ, Qureshi N. Introducing genetic testing for cardiovascular disease in primary care: a qualitative study. British Journal of General Practice 2014;64(622):e282-e289 doi: 10.3399/bjgp14X679714
The first exploratory trial (FBC) of systematically stratifying risk of familial breast cancer using decision support software in primary care.
Systematic reviews on clinical utility of the cancer family history and accessing cancer genetic services.
Our research impacts health policy internationally, including:
NICE guidelines on familial hypercholesterolaemia, breast cancer and lipid modification.
Antenatal and newborn genetic screening programmes.
Related training in the UK NHS.
Use of family history in health care (NIH consensus statement in US).
Research on diversity and genetics is helping reduce disparities in the Genome England 100,000 project.
Our research has been published in leading peer-reviewed journals. See our individual staff profiles to find out more. See a list of our recent publications.
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