The goal of predictive analytics monitoring is the early detection of patients at high risk
of subacute potentially catastrophic illnesses. A good example of a target illness is
respiratory failure leading to urgent unplanned intubation, where early detection might
lead to interventions that improve patient outcome. Previously, we identified signatures
of this illness in the continuous cardiorespiratory monitoring data of Intensive Care Unit
patients and devised algorithms to identify patients at rising risk. Here, we externally
validated 3 logistic regression models to estimate risk of emergency intubation that were
developed in Medical and Surgical ICUs at the University of Virginia. We calculated the
model outputs for more than 8000 patients in University of California San Francisco
ICUs, 240 of whom underwent emergency intubation as determined by individual chart
review. We found that the AUC of the models exceeded 0.75 in this external population,
and that the risk rose appreciably over the 12 hours prior to the event. We conclude
that abnormal signatures of respiratory failure in the continuous cardiorespiratory
monitoring are a generalizable phenomenon.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.