• Profile
Close

Real-time prediction of acute kidney injury in hospitalized adults: Implementation and proof of concept

American Journal of Kidney Diseases Jun 10, 2020

Ugwuowo U, Yamamoto Y, Arora T, et al. - Given that algorithms that predict high risk of acute kidney injury (AKI) are of great interest, but no investigations have included such an algorithm into the electronic health record (EHR) to help with clinical care, so researchers performed this prospective observational cohort analysis to report the experience of implementing such an algorithm. This study included 2,856 hospitalized adults with an algorithm-predicted risk of AKI in the next 24 hours exceeding 15%. AKI within 24 hours of pre-AKI alert (AKI24) was the outcome. The development of AKI 24 was reported in 18.9% of patients. This population had generally poor outcomes. In those who developed AKI 24 and in those who did not, the inpatient mortality was identified to be 29% and 14%, respectively. Experts noted that those who encountered AKI 24 more often had systolic BP < 100 mmHg, heart rate > 100 bpm and oxygen saturation <92%. In those who did vs did not develop AKI 24, a difference was evident in only hyaline casts on urine microscopy as well as fractional excretion of urea nitrogen, of all biomarkers measured. Overall, successful integration of a real-time AKI risk model into the EHR was done.

Go to Original
Only Doctors with an M3 India account can read this article. Sign up for free or login with your existing account.
4 reasons why Doctors love M3 India
  • Exclusive Write-ups & Webinars by KOLs

  • Nonloggedininfinity icon
    Daily Quiz by specialty
  • Nonloggedinlock icon
    Paid Market Research Surveys
  • Case discussions, News & Journals' summaries
Sign-up / Log In
x
M3 app logo
Choose easy access to M3 India from your mobile!


M3 instruc arrow
Add M3 India to your Home screen
Tap  Chrome menu  and select "Add to Home screen" to pin the M3 India App to your Home screen
Okay