  Background: Acute kidney injury (AKI) is a common complication in
hospitalized patients and a common cause for chronic kidney disease (CKD) and
increased hospital cost and mortality. By timely detection of AKI and AKI
progression, effective preventive or therapeutic measures could be offered.
This study aims to develop and validate an electronic phenotype to identify
patients with CKD and AKI. Methods: A database with electronic health records
data from a retrospective study cohort of 84,352 hospitalized adults was
created. This repository includes demographics, comorbidities, vital signs,
laboratory values, medications, diagnoses and procedure codes for all index
admission, 12 months prior and 12 months follow-up encounters. We developed
algorithms to identify CKD and AKI based on the Kidney Disease: Improving
Global Outcomes (KDIGO) criteria. To measure diagnostic performance of the
algorithms, clinician experts performed clinical adjudication of AKI and CKD on
300 selected cases. Results: Among 149,136 encounters, identified CKD by
medical history was 12% which increased to 16% using creatinine criteria. Among
130,081 encounters with sufficient data for AKI phenotyping 21% had AKI. The
comparison of CKD phenotyping algorithm to manual chart review yielded PPV of
0.87, NPV of 0.99, sensitivity of 0.99, and specificity of 0.89. The comparison
of AKI phenotyping algorithm to manual chart review yielded PPV of 0.99, NPV of
0.95 , sensitivity 0.98, and specificity 0.98. Conclusions: We developed
phenotyping algorithms that yielded very good performance in identification of
patients with CKD and AKI in validation cohort. This tool may be useful in
identifying patients with kidney disease in a large population, in assessing
the quality and value of care in such patients.
