Objective In 2006 the apnea of prematurity (AOP) consensus group identified inaccurate counting of apnea episodes as a major barrier to progress in AOP research. We compare nursing records of AOP to events detected by a clinically validated computer algorithm that detects apnea from standard bedside monitors. Study Design Waveform, vital sign, and alarm data were collected continuously from all very low-birth-weight infants admitted over a 25-month period, analyzed for central apnea, bradycardia, and desaturation (ABD) events, and compared with nursing documentation collected from charts. Our algorithm defined apnea as > 10 seconds if accompanied by bradycardia and desaturation. Results Of the 3,019 nurse-recorded events, only 68% had any algorithm-detected ABD event. Of the 5,275 algorithm-detected prolonged apnea events > 30 seconds, only 26% had nurse-recorded documentation within 1 hour. Monitor alarms sounded in only 74% of events of algorithm-detected prolonged apnea events > 10 seconds. There were 8,190,418 monitor alarms of any description throughout the neonatal intensive care unit during the 747 days analyzed, or one alarm every 2 to 3 minutes per nurse. Conclusion An automated computer algorithm for continuous ABD quantitation is a far more reliable tool than the medical record to address the important research questions identified by the 2006 AOP consensus group.

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In 2011, a multicenter group spearheaded at the University of Virginia demonstrated reduced mortality from real-time continuous cardiorespiratory monitoring in the neonatal ICU using what we now call Artificial Intelligence, Big Data, and Machine Learning. The large, randomized heart rate characteristics trial made real, for the first time that we know of, the promise that early detection of illness would allow earlier and more effective intervention and improved patient outcomes. Currently, though, we hear as much of failures as we do of successes in the rapidly growing field of predictive analytics monitoring that has followed. This Perspective aims to describe the principles of how we developed heart rate characteristics monitoring for neonatal sepsis and then applied them throughout adult ICU and hospital medicine. It primarily reflects the work since the 1990s of the University of Virginia group: the theme is that sudden and catastrophic deteriorations can be preceded by subclinical but measurable physiological changes apparent in the continuous cardiorespiratory monitoring and electronic health record.

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.