| Autor: |
Gareth Davies, Tim Scale, Ashley Akbari, James Chess, Ronan A Lyons |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
International Journal of Population Data Science, Vol 5, Iss 5 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2399-4908 |
| DOI: |
10.23889/ijpds.v5i5.1624 |
| Popis: |
Introduction Acute Kidney Injury (AKI) is a common, serious condition effecting up to 20% of all hospital admissions in the UK. AKI has an agreed definition for its recognition, however there is no consensus for the duration of an AKI episode. Objectives and Approach We aimed to describe four different potential definitions of an AKI episode. AKI was identified using an SQL (Structured Query Language) based algorithm (an implementation of the NHS England eAlert algorithm) applied to serum creatinine (SCr) results from a South Wales population of ~518,000 people, held in the Secure Anonymised Information Linkage (SAIL) Databank. Using a person’s index AKI case, we applied four different rules to define an episode of AKI. These definitions are: ALERTS - until they no longer trigger an AKI eAlert, 90 DAYS - until 90 days post first AKI test and |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
