aCenter for Critical Care and Departments of General Anesthesiology and Outcomes Research, Cleveland Clinic, Cleveland, OH, United StatesbRoper St Francis Health System, Charleston, SC, United StatescMedtronic Minimally Invasive Therapies Group, Boulder, CO, United StatesdMedtronic Regional Clinical Center, Rome, ItalyeDepartment of Anesthesiology and Pain Therapy, University Medical Center Maastricht, the Netherlands
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Predicting episodes or severity of cardiorespiratory decompensation has proved to be challenging in patients with stable surgical or medical conditions, recovering on the general care floor (ward). Critical cardiorespiratory events on hospital floors may be prevented by early detection of deterioration using continuous, electronic cardiorespiratory monitoring (CEM). The PRediction of Opioid-induced Respiratory Depression In Patients Monitored by capnoGraphY (PRODIGY) trial investigates CEM using pulse oximetry and capnography in 1650 patients at 16 centers in North America, Europe, and Asia (ClinicalTrials.gov Identifier: NCT02811302). The primary goal of the study is to derive a risk prediction score for respiratory depression (RD) on the ward. The validation-derivation cohort design will derive this score from RD detected by continuous, blinded, multiparameter cardiorespiratory (heart rate, respiratory rate, end tidal carbon dioxide, and pulse oximetry) monitoring of patients on the ward receiving parenteral (including epidural) opioids for primary analgesia. This review provides a comprehensive synopsis on respiratory compromise in lower acuity hospital settings (ward) and describes the protocol of the PRODIGY trial as a means to enable prediction and early response to these events.HighlightsOpioid induced respiratory depression is common on the nursing floor.Currently no tool reliably predicts respiratory depression in these settings.Continuous cardiorespiratory monitoring may be a solution.PRODIGY is an ongoing trial designed to examine the value of continuous monitoring.Trial results will be used to derive a respiratory depression risk prediction tool.