Abstract: TH-PO0699
Using Data Analytics to Affect AKI Trends
Session Information
- AKI: Prevention, Diagnostics, and Management
October 22, 2026 | Location: Exhibit Hall A, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Acute Kidney Injury
- 101 AKI: Epidemiology, Risk Factors, and Prevention
Authors
- Samad, Nasreen, Barts Health NHS Trust, London, England, United Kingdom
- Stickland, Mary A., Barking Havering and Redbridge University Hospitals NHS Trust, Romford, England, United Kingdom
- Hewa Wellalage, Dharshi, Barts Health NHS Trust, London, England, United Kingdom
- Yadav, Satender Kumar, Barking Havering and Redbridge University Hospitals NHS Trust, Romford, England, United Kingdom
Background
Acute Kidney Injury(AKI) is a significant cause of morbidity, mortality and healthcare cost.
Barking Havering and Redbridge University Hospitals NHS Trust(BHRUT) in Romford UK uses laboratory based AKI alerts which flag potential AKI cases (stage1-3) based on changes in serum creatinine levels. A dedicated AKI nurse targets patients with stage 2 and 3 AKI.
An impact of intervention on AKI trend was assessed via data analytics in BHRUT.
Methods
15 months data of patients who presented with AKI to BHRUT from December 2024 to February 2026 was reviewed to predict future trends in AKI and impact of intervention. All cases of AKI stage 2 and 3 presenting to the hospital and those developing AKI while in the hospital were included using AKI alert from pathology lab. The AKI nurse reviewed the numbers on daily basis and provided advice on management and arranged early referral to nephrologist as needed. At the same time revised AKI policy was made available on hospital intranet.
We looked into the trend of AKI since start of data collection and proactive intervention and its impact on AKI prevalence using Trendline and Time series forecasting. Linear trendline was used due to steady reduction in AKI rate over time both for stage 2 and 3 AKI. Forecasting model also predicted a continued decline in AKI numbers based on current data.
Results
Data from December-24 to February-26 showed total AKI cases peaked in January-25 at 474 and then fell steadily to 104 cases in Feb-26
The downward AKI trend shown in our analysis is contrary to the global and the UK national trends. The highest prevalence of AKI in January 2025 correlated to the UK national AKI incidence that year.
The reasons for reduction in AKI prevalence was likely secondary to the improved AKI detection via AKI alert system which was then picked up by AKI specialist nurse who provided daily review of the cases and advised management and prompted early referral and management support from the renal services.
Conclusion
We used descriptive analytics to summarize AKI prevalence, applied predictive analytics to show the declining trend using linear trendline and time series forecast for future AKI cases. The prescriptive analytics using updated AKI policy and dedicated AKI nurse showed the impact in the form of reduction of AKI cases.
This approach allowed to understand historical AKI patterns, anticipate future incidence, support proactive planning and provide clinical support in the form of intervention.