Despite the availability of electronic health records, general practice records are mainly based on a simple definition of the disease as whether is present or absent but do not grade the disease progression (also known as disease severity) and how it changes over time. Identifying disease severity is important and could be used to better understand and address patient needs.
Using data from the Clinical Practice Research Datalink and hospitalisation records between 2006 and 2016, we will determine the disease severity of two health conditions: type 2 diabetes (a chronic condition defined by abnormally-high blood sugar (glucose) due to defects in insulin secretion and/or action); and coronary heart disease (a heart condition causing chest pain and heart attack). For both conditions, we will use statistical methods to identify: i) The severity grades; ii) The time it takes to progress from one severity grade to the next using detailed data on patients' diagnoses and prescribed treatments. Also, we will apply our methods to 'forecast' needs for years 2020 and 2030 and consider the implications for future health-care planning for people with these two conditions. We expect the tools and findings produced to be useful for both direct patient care and policy-making.
Background and Objective: Despite that there are numerous grades of clinical severity for most long-term conditions, analyses of routinely-collected electronic health records still tend to characterise these conditions in a simplistic binary (presence/absence) approach. We aim to longitudinally characterise two exemplar conditions, type 2 diabetes (T2DM) and coronary heart disease (CHD), to develop clinical decision algorithms that will grade the severity and respective health care needs of patients with T2DM or CHD using linked hospitalisation and mortality data.
Methods and Data Analysis: In this cohort study, data will be organised and analysed in annual data bins (2006-2016). Over time and by region, age-group, gender and socio-economic deprivation, we will: i) Generate algorithms to estimate severity grades for T2DM and CHD, using within-condition diagnoses (such as complications), comorbidities, treatments and referrals and validate using survival analysis models; ii) Assess the association between the severity scores and the outcomes: cardiovascular disease, hospitalisation and death (including cardiovascular mortality) iii) Describe the disease progression journey, time needed to progress through severity grades; iv) Using regression modelling techniques, estimate stratified prevalence rates for T2DM and CHD, for 2020 and 2030. We expect the developed algorithms and findings to inform both direct patient care and policy-making.
Develop cardiovascular disease (CVD)
- Hospitalisation
- All-cause death
- Cardiovascular and diabetes-related deaths
- Hospitalisation due to hypoglycaemia
Evangelos Kontopantelis - Chief Investigator - University of Manchester
Salwa Zghebi - Corresponding Applicant - University of Manchester
Darren Ashcroft - Collaborator - University of Manchester
David Reeves - Collaborator - University of Manchester
Harm Van Marwijk - Collaborator - Brighton and Sussex Medical School
Mamas Mamas - Collaborator - Keele University
Martin Rutter - Collaborator - University of Manchester
HES Accident and Emergency;HES Admitted Patient Care;HES Outpatient;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation