Temporal Differential Expression of Physiomarkers Predicts Sepsis in Critically Ill Adults.

ABSTRACT:

Background

Sepsis is a life-threatening condition with high mortality rates. Early detection and treatment are critical to improving outcomes. Our primary objective was to develop artificial intelligence capable of predicting sepsis earlier using a minimal set of streaming physiological data in real time.

Methods and findings

A total of 29,552 adult patients were admitted to the intensive care unit across five regional hospitals in Memphis, Tenn, over 18 months from January 2017 to July 2018. From these, 5,958 patients were selected after filtering for continuous (minute-by-minute) physiological data availability. A total of 617 (10.4%) patients were identified as sepsis cases, using the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) criteri

SUBMITTER: Mohammed A 

PROVIDER: S-EPMC8352046 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature