Quality indicators for the detection and management of chronic kidney disease in primary care

Project Details

This project explores and compares outcomes for the detection of chronic kidney disease (CKD) and compare quality indicator data for the management of CKD in four large general practice datasets in Australia (Patron), Singapore (NUHS Data Mart), Canada (UTOPIAN) and Sweden. Chronic kidney disease (CKD) is a common but under-recognised disease. General practitioners (GPs) are ideally placed to identify patients at risk of, or in the early stages of, chronic kidney disease (CKD) and to implement prevention and early intervention efforts to reduce disease progression and subsequent hospitalisation. This project is aligned with Future Health Today, a program of work that is developing technological solutions for early detection, intervention and management of chronic illness.

Patron ID: PAT015

Project Lead:

A/Professor Jo-Anne Manski-Nankervis

Chronic kidney disease (CKD) is a common, but under-recognised condition globally and is associated with diabetes and an increased risk of cardiovascular disease (CVD) and mortality across Australian and international contexts. The evidence around CKD and its associated burden provides an impetus to prioritise strategies which focus on CKD detection and management. On average, 85% of Australians visit their GP at least once every year, and two million visit a GP each week. GPs are therefore well positioned to identify patients with CKD and implement early intervention efforts to reduce disease progression and prevent the development of CVD. 
Given that GPs are integral to the care provided to patients with diagnosed and undiagnosed CKD, this project will use de-identified data embedded in general practice electronic medical records (EMRS) to evaluate processes of care against a set of indicators for CKD. This use of EMR data will provide an opportunity to examine the practice of using EMR data as a source for research (as EMRs are increasing being seen as a valid and feasible way to evaluate health care), assist in early detection of disease, assess clinical outcomes and highlight inequities.


In 2017, Tu and colleagues developed a set of primary care quality indicators for CKD in the Canadian setting, to be used to assess the current state of detection and management of CKD using data extracted from general practice EMR. The aim of this project is to use Tu's CKD indicators to explore and compare CKD detection and management in four large general practice repositories from Australia (Patron), Singapore (NUHS Datahub), Sweden (NVH Region Uppsala) and Canada (UTOPIAN). Through this process, we will explore differences in general practices datasets, barriers to international comparative studies and methods to overcome these barriers.
Investigators from each country will seek funding for their own component of the study.  Each country will analyse their respective dataset, generate a summary of patient demographics to describe the participant cohort and then assess the EMR data against 17 quality indicators for CKD.

These indicators relate to the prevalence and the incidence of CKD in each country’s repository, the proportion of patients who have been referred to a kidney specialist, and the proportion of patients with CKD who died between 30 June 2017 to 30 June 2019. Number, percentage and 95% confidence intervals will be reported on for each indicator. CKD indicator measurements include those around pathology testing to detect CKD progression in patients with renal impairmentÍž pathology testing to detect CKD in patients with diabetes and hypertensionÍž the prescribing of anti-hypertensives and statin medications for patients with CKD, diabetes and/or albuminuria.


The aim of this project is to explore and compare CKD detection and management in four large general practice repositories from Australia (Patron), Singapore (NUHS Datahub), Sweden (NVH Region Uppsala) and Canada (UTOPIAN). Through the application of Tu’s CKD indicators to each of the above countries EMR databases, we will compare differences in each general practice datasets, identify barriers to international comparative studies, and report on methods to overcome these barriers.

Research Outcomes

As of 23/10/2023

  • Abstract-Australasian Association for Academic Primary Care (AAAPC) - 15/08/2023
  • Poster Presentation - Australasian Association for Academic Primary Care (AAAPC) - 15/08/2023

Research Group

Data Driven Quality Improvement Data for Decisions




Key Contact

For further information about this research, please contact the research group leader.

Department / Centre

General Practice and Primary Care

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