English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Timely recognition and treatment of specific critical care syndromes are the key determinants of outcomes of critical illness, regardless of the underlying cause. Acute kidney injury (AKI) is a prototypical example of a major health problem for which the efficacy of treatment is already limited once the condition is fully established. The condition is rarely present at the time of hospital admission, but develops within hours or days after an initial insult. Due to this delay in diagnosis, both therapeutic and experimental interventions are currently instituted late in the course of the illness, which limits the potential for therapeutic impact. Timely recognition of patients at risk of AKI would allow for novel and potentially more meaningful therapeutic and preventative strategies. Advances in medical informatics and widespread implementation of electronic medical records provide the opportunity to improve early recognition and treatment of specific critical care syndromes. In this study, we plan to develop a predictive model of AKI using probabilistic modeling. If successfully validated, this model will allow for early recognition of patients at risk of AKI for the purpose of diagnosis, prognosis, and future prevention trials.

international journal of nephrology and kidney failure

Towards Prevention of Acute Syndromes: Development and Validation of an Automated Electronic Kidney Injury Clinical Prediction Score (Ecklips), Population-Based Protocol