Development and validation of a deep learning algorithm for the prediction of serum creatinine in critically ill patients.

ABSTRACT:

Objectives

Serum creatinine (SCr) is the primary biomarker for assessing kidney function; however, it may lag behind true kidney function, especially in instances of acute kidney injury (AKI). The objective of the work is to develop Nephrocast, a deep-learning model to predict next-day SCr in adult patients treated in the intensive care unit (ICU).

Materials and methods

Nephrocast was trained and validated, temporally and prospectively, using electronic health record data of adult patients admitted to the ICU in the University of California San Diego Health (UCSDH) between January 1, 2016 and June 22, 2024. The model features consisted of demographics, comorbidities, vital signs and laboratory measurements, and medications. Model performance was evaluated by mean absolute er

SUBMITTER: Ghanbari G 

PROVIDER: S-EPMC11421473 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature