Challenges of studying and predicting chronic kidney disease progression and its complications using routinely collected electronic healthcare records
Lead Research Organisation:
London School of Hygiene & Tropical Medicine
Department Name: Epidemiology and Population Health
Abstract
Background: Chronic kidney disease is a worldwide public health problem affecting approximately 5-10% of adults in the UK. It is a progressive disease that is asymptomatic in the early stages but causes increasing morbidity as the disease progresses, leading to poor patient outcomes and quality of life. It can be detected in asymptomatic patients using blood and urine tests. Complications of CKD include increased cardiovascular risk, mortality, acute kidney injury and increased susceptibility to infection. Further, a minority of CKD patients progress to end stage renal disease requiring routine dialysis or a transplant which presents a heavy economic burden to public health services.
Chronic kidney disease may remain stable over many years, if managed successfully. However, in some cases, a rapid deterioration in kidney function sustained over a period of time can occur, termed progressive kidney disease. Progressive kidney disease is clearly an important health problem, posing a high risk to patient welfare and a burden to public health services. Further research is needed to improve knowledge of the nature, burden and consequences of progressive kidney disease. Early identification of patients at high risk of progressive kidney disease and identification of sub-phenotypes of progressive CKD would allow better individualised care and would be useful for resource management with potential implications for policy.
Large datasets of electronic healthcare records (EHRs) are available for chronic kidney disease patients managed in primary care in the UK. As well as capturing detailed patient characteristics, longitudinal data on kidney function are captured regularly over time through routine testing, allowing investigations in to the trajectory of kidney function over time in individual patients. However, the frequency of routine testing varies by individual general practitioners, and testing tends to occur in those the medical team is most concerned about, which means that detection of progressive disease is varied. This is what is termed testing bias where those who are tested tend to represent a sicker group of people and those who do not have a test represent a mix of sick people not engaging with the health service and people who are in good health. Moreover, those who have fast progressive kidney disease are referred to secondary specialist care, which means that these patients will be informatively missing from the data.
Aims: This research project will study chronic kidney disease progression and complications using routinely collected electronic healthcare records, focussing on data quality and appropriate study designs and methods to address these issues.
Key objectives:
1) Evaluate data completeness for renal function tests in routinely collected EHRs in UK primary care (National chronic kidney disease audit database) and the impact of data quality issues on accuracy of estimation of rate of decline in renal function
2) Conduct a comprehensive review of previous research using EHRs to study the progression of chronic kidney disease, in particular evaluating: reporting of data completeness and/or missing data; statistical methods used to overcome issues caused by missing data; and how authors interpreted study results in the presence of missing data issues
3) Investigate the impact of variability in general practitioner (GP) practice behaviour (in particular, electronic recording/coding of CKD on the patient health record) on adverse events known to be associated with CKD progression (National chronic kidney disease audit database). [This analysis deals with patient-level confounding by studying exposure at the practice level.]
4) Develop predictive models for CKD progression that is not biased by renal function testing behaviours, utilising data which is routinely available in EHRs in the population of CKD patients. (This analysis will use the SCREAM ddatabase, an external database owned by our rese
Chronic kidney disease may remain stable over many years, if managed successfully. However, in some cases, a rapid deterioration in kidney function sustained over a period of time can occur, termed progressive kidney disease. Progressive kidney disease is clearly an important health problem, posing a high risk to patient welfare and a burden to public health services. Further research is needed to improve knowledge of the nature, burden and consequences of progressive kidney disease. Early identification of patients at high risk of progressive kidney disease and identification of sub-phenotypes of progressive CKD would allow better individualised care and would be useful for resource management with potential implications for policy.
Large datasets of electronic healthcare records (EHRs) are available for chronic kidney disease patients managed in primary care in the UK. As well as capturing detailed patient characteristics, longitudinal data on kidney function are captured regularly over time through routine testing, allowing investigations in to the trajectory of kidney function over time in individual patients. However, the frequency of routine testing varies by individual general practitioners, and testing tends to occur in those the medical team is most concerned about, which means that detection of progressive disease is varied. This is what is termed testing bias where those who are tested tend to represent a sicker group of people and those who do not have a test represent a mix of sick people not engaging with the health service and people who are in good health. Moreover, those who have fast progressive kidney disease are referred to secondary specialist care, which means that these patients will be informatively missing from the data.
Aims: This research project will study chronic kidney disease progression and complications using routinely collected electronic healthcare records, focussing on data quality and appropriate study designs and methods to address these issues.
Key objectives:
1) Evaluate data completeness for renal function tests in routinely collected EHRs in UK primary care (National chronic kidney disease audit database) and the impact of data quality issues on accuracy of estimation of rate of decline in renal function
2) Conduct a comprehensive review of previous research using EHRs to study the progression of chronic kidney disease, in particular evaluating: reporting of data completeness and/or missing data; statistical methods used to overcome issues caused by missing data; and how authors interpreted study results in the presence of missing data issues
3) Investigate the impact of variability in general practitioner (GP) practice behaviour (in particular, electronic recording/coding of CKD on the patient health record) on adverse events known to be associated with CKD progression (National chronic kidney disease audit database). [This analysis deals with patient-level confounding by studying exposure at the practice level.]
4) Develop predictive models for CKD progression that is not biased by renal function testing behaviours, utilising data which is routinely available in EHRs in the population of CKD patients. (This analysis will use the SCREAM ddatabase, an external database owned by our rese
People |
ORCID iD |
| Dorothea Nitsch (Primary Supervisor) | |
| Faye Cleary (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| MR/N013638/1 | 01/10/2016 | 30/09/2025 | |||
| 1923114 | Studentship | MR/N013638/1 | 01/10/2017 | 25/12/2023 | Faye Cleary |