Modelling Risk of Cardio-Respiratory Instability as a Heterogeneous Process

Cardio-respiratory instability (CRI) occurs frequently in acutely ill. If not identified and treated early, it leads to significant morbidity and mortality. Current practice primarily relies on vigilance of the clinical personnel for early recognition of CRI. Given limited monitoring resources available in critical care environment, it can be suboptimal. Thus, an "Early Warning Scoring" mechanism is desirable to alert medical team when a patient is approaching instability. It is widely recognized that critically ill may show subtle changes prior to the onset of CRI, but it is not well known how their risk evolves before the onset. Using large amounts of physiological data routinely gathered from continuous noninvasive monitoring of Step-Down Unit patients, we demonstrate a data-driven approach that: (1) Characterizes patient's individual CRI risk process; (2) Identifies groups of patients that progress along similar risk evolution trajectories; (3) Utilizes grouping information to help forecast the emergence of CRI.