BMC Nephrology (Sep 2022)

Development and validation of algorithms to identify patients with chronic kidney disease and related chronic diseases across the Northern Territory, Australia

  • Winnie Chen,
  • Asanga Abeyaratne,
  • Gillian Gorham,
  • Pratish George,
  • Vijay Karepalli,
  • Dan Tran,
  • Christopher Brock,
  • Alan Cass

DOI
https://doi.org/10.1186/s12882-022-02947-9
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 12

Abstract

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Abstract Background Electronic 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. Methods The 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. Results For CKD defined as CKD stage 1 or higher (eGFR of any level with albuminuria or persistent eGFR 99% sensitivity and specificity. For related comorbidities – algorithm sensitivity and specificity results were 75% and 97% for diabetes; 85% and 88% for hypertension; and 79% and 96% for cardiovascular disease. Conclusions We 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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