Methods to Accurately Exclude Renal Dialysis Patients from The Welsh Acute Kidney Injury E-Alert System

Abstract

Introduction Acute Kidney Injury (AKI) effects up to 20% of emergency hospital admissions. It can be identified from serum creatinine (SCr) levels and as such an e-alert system has been introduced to hospitals in England and Wales to identify AKI. This system relies on manual review by pathology laboratory staff to prevent false alerts in dialysis patients. Objectives and Approach Our aim is to use routinely collected dialysis timeline and dialysis treatment data in order to create dialysis treatment spells using Structured Query Language (SQL), which can then be compared with dialysis prevalence results in the UK renal registry. Furthermore, we will link this data with biochemical results in Secure Anonymised Information Linkage (SAIL) Databank to create an AKI cohort. Through identifying when patients are on renal replacement therapy (RRT) at a specific time we are able to exclude false positive AKI Alerts. This is done in two ways – one from a timeline within the data which is used in the UK renal registry. The other using individual dialysis sessions to get accurate coverage, as short periods of acute dialysis are often not been recorded in UK renal registry data. Results Using the renal dataset in SAIL we were able identify more renal dialysis patients than the UK renal registry between (2009-17). By applying this to Swansea and Bridgend pathology data in SAIL we were able to identify 60,072 SCr tests in dialysis patients between 2011 and 2013 out of 1,776,101 SCr tests. In this cohort, without ruling out dialysis patients there would be 91,140 AKI alerts, however 19.6% (17,855) of the alerts are during dialysis treatments spells. Conclusion / Implications We were able to recreate the ‘e-Alert’ algorithm using SQL with SAIL and get an accurate picture of RRT in Wales, enabling more accurate AKI analysis.