The accuracy of diagnostic coding for acute kidney injury in England - a single centre study

BMC Nephrol. 2013 Mar 13;14:58. doi: 10.1186/1471-2369-14-58.

Abstract

Background: Acute kidney injury (AKI) is an independent risk factor for mortality and is responsible for a significant burden of healthcare expenditure, so accurate measurement of its incidence is important. Administrative coding data has been used for assessing AKI incidence, and shows an increasing proportion of hospital bed days attributable to AKI. However, the accuracy of coding for AKI and changes in coding over time have not been studied in England.

Methods: We studied a random sample of admissions from 2005 and 2010 where ICD-10 code N17 (acute renal failure) was recorded in the administrative coding data at one acute NHS Foundation Trust in England. Using the medical notes and computerised records we examined the demographic and clinical details of these admissions.

Results: Against a 6.3% (95% CI 4.8-7.9%) increase in all non-elective admissions, we found a 64% increase in acute renal failure admissions (95% CI 41%-92%, p < 0.001) in 2010 compared to 2005. Median age was 78 years (IQR 72-87), 11-25% had a relevant pre-admission co-morbidity and 64% (55-73%) were taking drugs known to be associated with AKI. Over both years, 95% (91-99%) of cases examined met the Kidney Disease: Improving Global Outcomes criteria for AKI.

Conclusions: Patients with hospital admissions where AKI has been coded are elderly with multiple co-morbidities. Our results demonstrate a high positive predictive value of coding data for a clinical diagnosis of AKI, with no suggestion of marked changes in coding of AKI between 2005 and 2010.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Acute Kidney Injury / classification*
  • Acute Kidney Injury / diagnosis
  • Acute Kidney Injury / mortality*
  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • England / epidemiology
  • Female
  • Hospitalization / statistics & numerical data*
  • Humans
  • Incidence
  • International Classification of Diseases / statistics & numerical data*
  • Male
  • Middle Aged
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Survival Analysis
  • Survival Rate
  • Young Adult