Validation of CKD and related conditions in existing data sets: A systematic review

Background: Accurate classification of individuals with kidney disease is vital to research and public health efforts aimed at improving health outcomes. Our objective is to identify and synthesize published literature evaluating the accuracy of existing data sources related to kidney disease.

Study design: A systematic review of studies seeking to validate the accuracy of the underlying data relevant to kidney disease.

Setting & population: US-based and international studies covering a wide range of both outpatient and inpatient study populations.

Selection criteria for studies: Any English-language study investigating the prevalence or cause of kidney disease, existence of comorbid conditions, or cause of death in patients with chronic kidney disease (CKD). All definitions and stages of CKD, including end-stage renal disease (ESRD), were accepted.

Index tests: Presence of a kidney disease-related variable in existing data sets, including administrative data sets and disease registries.

Reference tests: Presence of a kidney disease-related variable defined using laboratory criteria or medical record review.

Results: 30 studies were identified. Most studies investigated the accuracy of kidney disease reporting, comparing coded renal disease with that defined using estimated glomerular filtration rate. The sensitivity of coded renal disease varied widely (0.08-0.83). Specificity was higher, with all studies reporting values ≥0.90. Studies evaluating the cause of CKD, comorbid conditions, and cause of death in patients with CKD used ESRD or transplant populations exclusively, and accuracy was highly variable compared with ESRD registry data.

Limitations: Only English-language studies were evaluated.

Conclusions: Given the heterogeneous results of validation studies, a variety of attributes of existing data sources, including the accuracy of individual data items within these sources, should be considered carefully before use in research, quality improvement, and public health efforts.