  The acuity state of patients in the intensive care unit (ICU) can quickly
change from stable to unstable. Early detection of deteriorating conditions can
result in providing timely interventions and improved survival rates. In this
study, we propose APRICOT-M (Acuity Prediction in Intensive Care Unit-Mamba), a
150k-parameter state space-based neural network to predict acuity state,
transitions, and the need for life-sustaining therapies in real-time in ICU
patients. The model uses data obtained in the prior four hours in the ICU and
patient information obtained at admission to predict the acuity outcomes in the
next four hours. We validated APRICOT-M externally on data from hospitals not
used in development (75,668 patients from 147 hospitals), temporally on data
from a period not used in development (12,927 patients from one hospital from
2018-2019), and prospectively on data collected in real-time (215 patients from
one hospital from 2021-2023) using three large datasets: the University of
Florida Health (UFH) dataset, the electronic ICU Collaborative Research
Database (eICU), and the Medical Information Mart for Intensive Care
(MIMIC)-IV. The area under the receiver operating characteristic curve (AUROC)
of APRICOT-M for mortality (external 0.94-0.95, temporal 0.97-0.98, prospective
0.96-1.00) and acuity (external 0.95-0.95, temporal 0.97-0.97, prospective
0.96-0.96) shows comparable results to state-of-the-art models. Furthermore,
APRICOT-M can predict transitions to instability (external 0.81-0.82, temporal
0.77-0.78, prospective 0.68-0.75) and need for life-sustaining therapies,
including mechanical ventilation (external 0.82-0.83, temporal 0.87-0.88,
prospective 0.67-0.76), and vasopressors (external 0.81-0.82, temporal
0.73-0.75, prospective 0.66-0.74). This tool allows for real-time acuity
monitoring in critically ill patients and can help clinicians make timely
interventions.
