Assessing the accuracy of ICD-10 coding for measuring rates of and mortality from acute kidney injury and the impact of electronic alerts: an observational cohort study

Rachael Logan; Peter Davey; Nicosha De Souza; David Baird; Bruce Guthrie; Samira Bell

doi:10.1093/ckj/sfz117

Assessing the accuracy of ICD-10 coding for measuring rates of and mortality from acute kidney injury and the impact of electronic alerts: an observational cohort study

Clin Kidney J. 2019 Oct 19;13(6):1083-1090. doi: 10.1093/ckj/sfz117. eCollection 2020 Dec.

Authors

Rachael Logan¹, Peter Davey¹, Nicosha De Souza¹, David Baird², Bruce Guthrie³, Samira Bell^{1

2}

Affiliations

¹ Division of Population Health and Genomics, Medical Research Institute, University of Dundee, Dundee, UK.
² Renal Unit, Ninewells Hospital and Medical School, NHS Tayside, Dundee, UK.
³ Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK.

Abstract

Background: The application of a uniform definition for acute kidney injury (AKI) is vital to advance understanding and management of AKI. International Classification of Diseases (Tenth Revision) (ICD-10) coding is frequently used to define AKI, but its accuracy is unclear. The aim of this study was to determine whether ICD-10 coding is a reliable method of monitoring rates and outcomes of AKI in inpatients compared with biochemically defined AKI, and whether electronic alerts (e-alerts) for AKI affect ICD-10 AKI coding.

Methods: An observational cohort study of all 505 662 adult admissions to acute hospitals in two Scottish Health Boards [National Health Service (NHS) Tayside and NHS Fife] from January 2013 to April 2017 was performed. AKI e-alerts were implemented in NHS Tayside in April 2015. Sensitivity, specificity, positive and negative predictive values of ICD-10 coding for AKI compared with biochemically defined AKI using the Kidney Disease: Improving Global Outcomes definition and relative risk of 30-day mortality in people with ICD-10 and biochemically defined AKI before and after AKI e-alert implementation were performed.

Results: Sensitivity of ICD-10 coding for identifying biochemically defined AKI was very poor in both health boards for all AKI (Tayside 25.7% and Fife 35.8%) and for Stages 2 and 3 AKI (Tayside 43.8% and Fife 53.8%). Positive predictive value was poor both for all AKI (Tayside 76.1% and Fife 45.5%) and for Stages 2 and 3 AKI (Tayside 45.5% and Fife 36.8%). Measured mortality fell following implementation of AKI e-alerts in the ICD-10-coded population but not in the biochemically defined AKI population, reflecting an increase in the proportion of Stage 1 AKI in ICD-10-coded AKI. There was no evidence that the introduction of AKI e-alerts in Tayside improved ICD-10 coding of AKI.

Conclusion: ICD-10 coding should not be used for monitoring of rates and outcomes of AKI for either research or improvement programmes.

Keywords: ICD-10 coding; acute kidney injury; electronic alerts; epidemiology.