Early prediction of vasopressor initiation in ICU sepsis patients using an interpretable EHR-based ML model.

ABSTRACT:

Background

Early identification of septic patients who will require vasopressor support could provide a critical window for hemodynamic optimisation, yet current bedside cues often appear only when shock is imminent.

Objective

We aimed to develop and validate an interpretable electronic health record (EHR)-based machine-learning model that predicts vasopressor initiation several hours before therapy in intensive care unit (ICU) patients with sepsis.

Methods

We conducted a retrospective study using the MIMIC-IV v2.2 database (2008-2019). We screened adult Sepsis-3 ICU stays and labeled the patients who commenced continuous vasopressor infusions 6 to 48 hours after admission as cases; we defined controls as sepsis patients with ICU stays ≥48 hours and no vasopressor exp

SUBMITTER: Duval L 

PROVIDER: S-EPMC12706921 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature