Chronic kidney disease (CKD) is a common illness where the kidneys do not work as well as they should and is a major health problem worldwide. It is primarily diagnosed using estimated glomerular filtration rate (eGFR) which is a marker of how well your kidneys are working by measuring for a waste product in your blood. A low eGFR indicates poor kidney function. CKD can lead to other complications, such as infections and heart problems, and is often developed in patients with diabetes. CKD develops into end stage renal disease (ESRD) if left untreated, where the kidneys can no longer perform properly due to damage.
The findings of this study will help to see what increases the chances of getting ESRD, infections or heart problems in patients with CKD depending on their diabetes status. We will calculate their eGFR and see who is more likely to develop ESRD, infection and heart problems out of the three groups: type 1 diabetics, type 2 diabetics or no diabetes. We will describe the patterns in these groups and see if there is early detection of kidney problems by looking at their eGFR at diagnosis.
This study aims to predict outcomes such as end stage renal disease (ESRD), infection and major adverse cardiac events (MACE) in patients with chronic kidney disease (CKD) in three different cohorts; non-diabetes, diabetes type 1 and diabetes type 2. The objectives are to identify a population of patients with CKD using Read codes and ICD-10 codes and describe their progression to one or more of the three endpoints: MACE, infection or ESRD.
Adjusted incidence rates and 95% confidence intervals for all-cause and cause-specific infections will be calculated for eGFR category using Poisson regression and presented as incidence rate ratios (IRR) with eGFR >90ml/min/1.73m2/year as the reference. Time-dependent Cox-proportional hazard models will be used to estimate risk and account for potentially confounding factors, incorporating eGFR and albuminuria staging, diabetes status, prior co-morbid events and baseline characteristics and therapies. Other regressions models will be run as a sensitivity analysis, adjusting for covariates at baseline.
Craig Currie - Chief Investigator - Cardiff University
Ellen Hubbuck - Corresponding Applicant - Pharmatelligence Limited t/a Human Data Sciences
Laura Scott - Collaborator - Pharmatelligence Limited t/a Human Data Sciences
Meena Jain - Collaborator - Napp Pharmaceutical Group
Sara Jenkins-Jones - Collaborator - Pharmatelligence Limited t/a Human Data Sciences
Sara Jenkins-Jones - Collaborator - Pharmatelligence Limited t/a Human Data Sciences
HES Admitted Patient Care;ONS Death Registration Data