Early Detection of Respiratory Compromise to Prevent Harm of the Hospitalized Opioid Treated Patient

Imagine a hospital or ambulatory surgical work environment where clinicians could look at an electronic respiratory monitoring device and observe the patient's data over time, and be cued by the monitor before the patient exhibits dangerous opioid induced respiratory depression/respiratory compromise. Currently, clinicians use electronic monitoring data for real-time assessment of respiratory status. Alarms set at thresholds alert a clinician when the patient is currently experiencing respiratory compromise. Adverse events secondary to opioid induced respiratory compromise (OIRC) continue to occur in 0.5-4.2% of hospitalized patients receiving opioids for acute pain. Opioids continue to be a staple for acute pain management. In this environment of litigation around adequate pain management and the use of opioids, clinicians need a more sensitive and specific way to determine which patients are at risk of severe respiratory depression when using opioids for acute pain management in the hospital setting. This study proposes to evaluate algorithms preliminarily developed in the computer laboratory. This translational research will compare and test replication of our algorithms in a new sample of patients. Patients' electronic monitor data will be used to further develop our algorithms for identifying patients who exhibit OIRC and predicting OIRC events. Explicitly, we will monitor post-operative patients using pulse oximetry, capnography, minute ventilation, and transcutaneous PCO2 during recovery from anesthesia (in PACU), and on the general care floor for up to 72 hours. This data, along with covariates collected from the electronic medical record and environment will be used in machine learning models to develop our algorithms in an iterative process. Future studies will involve instituting these algorithms into a monitoring interface and testing in simulation and in real-time on patients. Please see AHRQ summary sheets from a submission that occurred earlier this