ArticleBMC nephrology2022
Development and validation of algorithms to identify patients with chronic kidney disease and related chronic diseases across the Northern Territory, Australia.
Article in BMC nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- Leveraging natural language processing and machine learning to identify chronic conditions from primary care electronic medical records.Scientific reports · 2026Article
- Climate effect on the incidence of kidney failure patients in Australia.BMC medicine · 2025Article
- Rapid chronic kidney disease progression in younger, First Nations patients in the Northern Territory.Internal medicine journal · 2025Article
- Identifying a cohort of hospitalized chronic kidney disease patients using electronic health records: lessons learnt and implications for future research and clinical practice guidelines.Clinical kidney journal · 2025Article
- Cost-Effectiveness of Clinical Decision Support to Improve CKD Outcomes Among First Nations Australians.Kidney international reports · 2025Article
- The computerized algorithm for renal assessment improves diagnostic accuracy of renal impairment in hospitalized patients.Scientific reports · 2025Article
- Design and validation of an automated healthcare-integrated biobanking algorithm for identification of advanced chronic kidney disease.Computational and structural biotechnology journal · 2025Article
- Costs and healthcare use of patients with chronic kidney disease in the Northern Territory, Australia.BMC health services research · 2024Article
- Developing an integrated clinical decision support system for the early identification and management of kidney disease-building cross-sectoral partnerships.BMC medical informatics and decision making · 2024Article
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundElectronic health records can be used for population-wide identification and monitoring of disease. The Territory Kidney Care project developed algorithms to identify individuals with chronic kidney disease (CKD) and several commonly comorbid chronic diseases. This study aims to describe the development and validation of our algorithms for CKD, diabetes, hypertension, and cardiovascular disease. A secondary aim of the study was to describe data completeness of the Territory Kidney Care database.
methodsThe Territory Kidney Care database consolidates electronic health records from multiple health services including public hospitals (n = 6) and primary care health services (> 60) across the Northern Territory, Australia. Using the database (n = 48,569) we selected a stratified random sample of patients (n = 288), which included individuals with mild to end-stage CKD. Diagnostic accuracy of the algorithms was tested against blinded manual chart reviews. Data completeness of the database was also described.
resultsFor CKD defined as CKD stage 1 or higher (eGFR of any level with albuminuria or persistent eGFR < 60 ml/min/1.73
conclusionsWe developed and validated algorithms to identify CKD and related chronic diseases within electronic health records. Validation results showed that CKD algorithms have a high degree of diagnostic accuracy compared to traditional administrative codes. Our highly accurate algorithms present new opportunities in early kidney disease detection, monitoring, and epidemiological research.
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