An excessive reduction of the high blood glucose levels (defined hyperglycaemia) with medications in people with diabetes can result in very low blood glucose levels (defined hypoglycaemia). Hypoglycaemia significantly impairs the quality of life and could potentially increase the risk of future cardiovascular complications (for example, heart attack or stroke).
In this study, we aim to identify in patients with diabetes and the characteristics (for example, age or ethnicity) which are related to a higher risk of complications after an episode of hypoglycaemia. This is relevant because the risk of future cardiovascular complications and death can be very different in patients with diabetes; it could be very high is some patients and low in others. To date, however, the characteristics associated with an increased risk are not known.
In this view, we will first identify the characteristics associated with a higher risk in patients who have experienced hypoglycaemia; then, we will develop a model which can be used by health care professionals to estimate the risk of death or cardiovascular complications
This study covers the research area related to the risk stratification of long-term complications associated with severe hypoglycaemia in patients with diabetes.
Firstly, using data collected in the Clinical Practice Research Datalink (CPRD) Gold database with linkage to Hospital Episode Statistics (HES) and Office for National Statistics (ONS), we will investigate the association between severe hypoglycaemia and cardiovascular and all-cause mortality, and how other risk factors, such as co-morbidities and medications use, act as effect modifiers of their association. Therefore, a cohort of diabetic patients will be identified within CPRD and their exposure status (hypoglycaemia, exposed; non-hypoglycaemia, unexposed) will be defined with linkage to HES. The outcomes all-cause and cause-specific deaths will be identified via linkage to ONS. Time-to-event analysis will be used to estimate the hazard associated with specific risk factors, their interactions, and their relationship with survival probabilities (i.e., absolute risk).
Secondly, the same cohort will be used to develop and validate risk prediction models for all-cause, cardiovascular, and non-cardiovascular mortality. Model's performance will be assessed using well-established statistical methods, including discrimination, calibration, and reclassification indices. The discrimination indices will quantify, in particular, the added value of including information on severe hypoglycaemia to estimate the risk of outcomes. Moreover, model's prediction ability will be evaluated across characteristics of patients.
All-cause mortality
- Cardiovascular mortality
Francesco Zaccardi - Chief Investigator - University of Leicester
Francesco Zaccardi - Corresponding Applicant - University of Leicester
Claire Lawson - Collaborator - University of Leicester
Kamlesh Khunti - Collaborator - University of Leicester
Melanie Davies - Collaborator - University of Leicester
Suping Ling - Collaborator - University of Leicester
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Townsend Score