Multiple methodologies have been used to quantify adverse drug events in the perioperative period including voluntary reporting systems, manual chart review, and trigger tools. These methods are often retrospective, resource intensive, and require highly-trained reviewers. A multi-center perioperative safety initiative that used the IHI surgical trigger tool illustrated that narcotic-related harm constituted 6 of the 138 adverse events identified from 854 patient record reviews (0.7/100 patient records) . Another retrospective study that identified potential harm based on naloxone use and manual chart review reported a baseline oversedation rate of approximately 2 ADE's/1000 postoperative surgical patient discharges and required 20 hrs/week of clinical pharmacist effort . Using targeted computerized detection methodology, we identified 3.3 ADEs/1000 surgical inpatient encounters with 3 hours/week effort. An inpatient study that used automated dispensing system charges for naloxone combined with targeted chart review demonstrated a PPV of 87% for this type of alert . Our reported PPV was 68.3% in the postoperative population based on naloxone administration rather than dispensing. The difference in PPV may be attributable to a broader definition of an ADE compared to our focus only on oversedation/respiratory depression. In our review of the literature, we could not find any studies where the rate of opioid-related ambulatory ADEs was measured. Our ambulatory rate of 0.76 ADE's/1000 encounters is far less than the inpatient rate of 3.5 ADE's/1000 encounters, which may be reflective of the differences in the types of procedures performed, patients' comorbid conditions, acuity of presentation or other unidentified factors. We have expanded computerized ADE detection to reliably detect opioid-related oversedation in both the inpatient and ambulatory perioperative environment.
Sustainability of perioperative event detection is attributable to the incorporation of event evaluations into routine clinical pharmacist workflow. By expanding our methodology to the entire perioperative patient population, we were able to improve the overall detection of adverse events and compare surgical inpatients to overall inpatients. Our effort to cross reference other sources of ADE data supports the concept of using a broad approach to improve capture of events as part of an overall quality improvement strategy . This study has also identified a subpopulation of patients that are of particular clinical interest – those that experience repeat incidences of oversedation. This subset of patients who experience repeated harmful events is important because it reveals that there may be underlying factors predisposing these patients to harm that have yet to be identified, and may require a more in-depth risk factor analysis. We are considering adding an alert to the patient's electronic health record that would signal if that patient has had a history of opiate-related ADEs and extra caution is necessary in drug dosage and monitoring. This may help prevent future ADEs.
There are several limitations to this study. In order for the computerized surveillance rule engine to detect naloxone administration, it must have been manually entered into the DUH anesthesia information system. Therefore, there is a remote chance that naloxone administration was not documented and a small number of alerts may have been missed. The ability to replicate the technical development of sophisticated computerized adverse drug event surveillance may not be readily transferable to other health systems, however vended systems are currently available. Additionally, we have chosen to focus only on a rare yet serious ADE due to opioids, although the trigger alert does potentially identify other types of ADEs. Patients most commonly received fentanyl or hydromorphone, but also received several other non-opioid medications with sedative effects. We did not analyze other potential ADE categories, such as the 13 trigger alerts due to naloxone infusions for pruritus or the 11 alerts for low-dose naloxone infusions to accompany other indications (e.g. patient controlled analgesia or epidurals). Finally, we did not evaluate the cases of intraoperative naloxone use more closely due to the lack documentation of the rationale for the administration of naloxone in the anesthesia information system.
Future work is needed to explore contributing factors for the oversedation cases both postoperatively as well as in the subpopulation of patients who received intraoperative naloxone. Since the intraoperative patients experienced a higher number of subsequent events, they are of particular interest. This may include the development of a predictive risk model or pharmacogenomic screening that could be used to spur intervention strategies that prevent events or at least their repetition . As we implement quality improvement initiatives, such as special signaling for patients with a known history of oversedation, improved hand-off communications, increased monitoring for anyone experiencing an ADE postoperatively, and use of adjunctive opioid sparing non-sedating medications, we can use the computerized surveillance ADE rate as a quantitative measure to track longitudinal improvements. It is our hope that as electronic health records become mainstream that adverse drug event detection will evolve to creative point of care alerting and models will shift from detection toward mitigation of patient harm.