RESUMEN
BACKGROUND: Postsurgical patients experiencing opioid-related adverse drug events have 55% longer hospital stays, 47% higher costs associated with their care, 36% increased risk of 30-day readmission, and 3.4 times higher risk of inpatient mortality compared to those with no opioid-related adverse drug events. Most of the adverse events are preventable. GENERAL AIM: This study explored three types of electronic monitoring devices (pulse oximetry, capnography, and minute ventilation [MV]) to determine which were more effective at identifying the patient experiencing respiratory compromise and, further, to determine whether algorithms could be developed from the electronic monitoring data to aid in earlier detection of respiratory depression. MATERIALS AND METHODS: A study was performed in the postanesthesia care unit (PACU) in an inner city. Sixty patients were recruited in the preoperative admissions department on the day of their surgery. Forty-eight of the 60 patients wore three types of electronic monitoring devices while they were recovering from back, neck, hip, or knee surgery. Machine learning models were used for the analysis. RESULTS: Twenty-four of the 48 patients exhibited sustained signs of opioid-induced respiratory depression (OIRD). Although the SpO2 values did not change, end-tidal CO2 levels increased, and MV decreased, representing hypoventilation. A machine learning model was able to predict an OIRD event 10 min before the actual event occurred with 80% accuracy. LINKING EVIDENCE TO ACTION: Electronic monitoring devices are currently used as a tool to assess respiratory status using thresholds to distinguish when respiratory depression has occurred. This study introduces a potential paradigm shift from a reactive approach to a proactive approach that would identify a patient at high risk for OIRD. Capnography and MV were found to be effective tools in detecting respiratory compromise in the PACU.