External Validation of Persistent Severe Acute Kidney Injury Prediction With Machine Learning Model.

ABSTRACT:

Objective

To externally validate the persistent electronic alert (PersEA) machine learning model for predicting persistent severe acute kidney injury (psAKI), addressing the scarcity of validated prediction models.

Patients and methods

We included adult patients (18 years or older) admitted to intensive care unit with at least stage 2 acute kidney injury (AKI) at a tertiary medical center, using retrospective data collected between January 1st, 2017 and December 31st, 2022. The data were accessed and analyzed during the period from March 1st, 2023, through July 28th, 2023. The psAKI was defined as AKI stage 3 lasting for ≥72 hours or AKI leading to death in 48 hours or kidney replacement therapy in 1 day. The performance of the PersEA model, a boosted tree algorithm fed by h

SUBMITTER: Zappala S 

PROVIDER: S-EPMC12190941 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature