Developing a prediction model for LVSD / Heart Failure

United Kingdom
Sep 22, 2016
Dec 21, 2016
Organization Type
University and College
Full Time

Heart failure is a clinical syndrome of fatigue, oedema and breathlessness in the presence of cardiac dysfunction. Many patients with heart failure are undiagnosed. Some of these patients have heart failure with reduced Left Ventricular Systolic Dysfunction (LVSD). Others have heart failure with preserved ejection fraction. LVSD is the commonest cause of heart failure. Symptomatic LVSD has a poor prognosis and high healthcare utilisation costs but appropriate treatment improves outcomes.

Early treatment of heart failure with LVSD or even LVSD before it has progressed to heart failure is beneficial. There is therefore a case for identifying patients with LVSD and with symptomatic heart failure. Many UK patients with heart failure remain undiagnosed.

Difficulties in identifying heart failure are well recognised and in the UK are complicated by restricted access to echocardiography facilities.

One strategy to improve identification of patients with LVSD and with symptomatic heart failure is to undertake selective screening or case finding.

The aim of this study is to derive a prediction model for heart failure using analysis of data routinely recorded in primary care. This prediction model would be incorporated into existing electronic patient records software to identify patients whose characteristics indicate they are have a high probability of having LVSD or symptomatic heart failure and to assist in the earlier diagnosis of heart failure.

A retrospective cohort study of adults aged ≥30 years in a large anonymised database of electronic primary care records.

This doctoral thesis will develop and make use of skills in epidemiology and statistical analysis and in the handling of large datasets of electronic primary care records.

Applicants should hold or realistically expect to obtain at least an Upper Second Class Honours Degree in a relevant health or social science subject and have skills in data analysis.

Alternatively applicants may hold or realistically expect to obtain at least an Upper Second Class Honours Degree in a relevant subject that includes substantial analytic skills such as statistics, mathematics or computing science, but have a demonstrable interest in health and health care.

Funding Notes

Self-funded students only


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