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July 26, 2026Nephrology

Evaluation of a Chronic Kidney Disease e‐Phenotype: Identification and Characterisation by Electronic Health Record Data

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Authors

CSChristopher Noel SparksThe Royal Melbourne HospitalASAdam SteinbergThe Royal Melbourne HospitalTFTimothy FazioThe Royal Melbourne Hospital

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Overview

Randomized trial evaluates a chronic kidney disease e-phenotype for risk assessment in patients, indicating potential for improved early detection.

Key Points

  • Develop and evaluate an electronic phenotype for chronic kidney disease (CKD) using electronic health record data.
  • Extracted patient encounter and laboratory data over four years from a large Australian hospital EHR.
  • Excluded kidney transplant recipients and dialysis patients in determining CKD.
  • Classified CKD based on ICD-10 codes, eGFR, and albuminuria for a minimum of 3 months.
  • Identified 17,908 likely CKD cases (5.2%) from a cohort of 342,146 patients.
  • Sensitivity for CKD detection by ICD-10 codes was 0.85 and specificity was 0.83; for eGFR, sensitivity was 0.62 and specificity was 0.98.
  • The proportion of patients coded for CKD significantly increased with higher G-stage (OR 3.05) and A-stage (OR 2.31), p < 0.001.

Cite This Study

Sparks et al. (2026) studied this question.

synapsesocial.com/papers/6a65a962d3aea3239cd793aahttps://doi.org/10.1111/nep.70248
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Also Consider

Synapse has enriched 2 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Identifying and Characterising a Chronic Kidney Disease Electronic‐Phenotype Using Electronic Health Record‐Derived Data: A Narrative Review of Strategies and Applications2025 · 3 citations
  2. 2A New Equation to Estimate Glomerular Filtration Rate2009 · 26,127 citations