Abstract: PO0001
AKI Identification: Use of Electronic AKI Alerts vs. Electronic Health Records in Hospital Episode Statistics
Session Information
- AKI Epidemiology, Risk Factors, and Prevention: Clinical Research
October 22, 2020 | Location: On-Demand
Abstract Time: 10:00 AM - 12:00 PM
Category: Acute Kidney Injury
- 101 AKI: Epidemiology, Risk Factors, and Prevention
Authors
- Savino, Manuela, UK Renal Registry, Bristol, Bristol, United Kingdom
- Casula, Anna, UK Renal Registry, Bristol, Bristol, United Kingdom
- Wong, Esther Ha Sum, UK Renal Registry, Bristol, Bristol, United Kingdom
- Kolhe, Nitin V., Royal Derby Hospital, Derby, Derby, United Kingdom
- Medcalf, James, UK Renal Registry, Bristol, Bristol, United Kingdom
- Nitsch, Dorothea, UK Renal Registry, Bristol, Bristol, United Kingdom
Background
Acute Kidney Injury (AKI) refers to an abrupt decline in the glomerular filtration rate (GFR) potentially associated with significant morbidity and mortality. Since April 2015, an automated real-time electronic (e)-alert system for AKI has been introduced and progressively implemented in England, with alert data being sent to the UK Renal Registry (UKRR) for collection into a master patient index (MPI). Historically, the only way to routinely measure AKI incidence in hospital was to analyse the Hopital Episode Statistics (HES). This project aims to determine whether episodes of AKI identified in the UKRR MPI correspond to coded diagnoses on the discharge record held in HES.
Methods
The UKRR MPI of all AKI e-alerts (stages 1, 2 and 3) in patients aged ≥18 years, between 01/01/2017 and 31/12/2017 were linked to HES data to identify a hospitalised AKI population. Descriptive analyses were conducted to describe the demographics and to investigate whether those with an AKI e-alert also had an International Classification of Diseases (ICD)-10 code for AKI (N17) in HES.
Results
From 01/01/2017 to 31/12/2017, 301,504 hospitalised adults received an AKI e-alert. AKI severity was positively associated with the percentage of AKI alerts coded in HES. There was a significant variation in HES coding between hospitals, most pronounced for AKI stage 1 (mean 48.2% SD 14) versus AKI stage 3 (mean 83.3 % SD 7.3) (figures 1). Younger adults with AKI e-alerts were less often coded in HES for all three AKI stages (33% people aged 18-29 years versus 64% people aged ≥ 85 years).
Conclusion
In 2017, earlier stages of AKI e-alerts were poorly coded in HES. There was also high degree of inter-hospital variability, particularly for AKI stage 1, reflecting potentially poor clinical recognition and documentation in medical records and subsequent clinical coding. AKI e-alerts were poorly captured in HES for younger adults in comparison to those of older age. Use of HES to identify cases of AKI is likely to underestimate the incidence of AKI, especially for AKI stage 1, though a high proportion of the most severe cases will be captured.