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Diagnostic accuracy of administrative codes for autosomal dominant polycystic kidney disease in clinic patients with cystic kidney disease

Abstract

Background

The ability to identify patients with autosomal dominant polycystic kidney disease (ADPKD) and distinguish them from patients with similar conditions in healthcare administrative databases is uncertain. We aimed to measure the sensitivity and specificity of different ADPKD administrative coding algorithms in a clinic population with non-ADPKD and ADPKD kidney cystic disease.

Methods

We used a dataset of all patients who attended a hereditary kidney disease clinic in Toronto, Ontario, Canada between 1 January 2010 and 23 December 2014. This dataset included patients who met our reference standard definition of ADPKD or other cystic kidney disease. We linked this dataset to healthcare databases in Ontario. We developed eight algorithms to identify ADPKD using the International Classification of Diseases, 10th Revision (ICD-10) codes and provincial diagnostic billing codes. A patient was considered algorithm positive if any one of the codes in the algorithm appeared at least once between 1 April 2002 and 31 March 2015.

Results

The ICD-10 coding algorithm had a sensitivity of 33.7% [95% confidence interval (CI) 30.0–37.7] and a specificity of 86.2% (95% CI 75.7–92.5) for the identification of ADPKD. The provincial diagnostic billing code had a sensitivity of 91.1% (95% CI 88.5–93.1) and a specificity of 10.8% (95% CI 5.3–20.6).

Conclusions

ICD-10 coding may be useful to identify patients with a high chance of having ADPKD but fail to identify many patients with ADPKD. Provincial diagnosis billing codes identified most patients with ADPKD and also with other types of cystic kidney disease.