Retrospective Analysis of Opioid Medication Incidents Requiring Administration of Naloxone
Bibliographic record
Abstract
Background: Opioid analgesics are high-alert medications known to cause adverse drug events.Objectives: The purpose of this study was to determine the cause of opioid incidents requiring administration of naloxone, an opioid reversal agent. The specific objectives were to determine the number of opioid incidents and the proportion of incidents documented through occurrence reporting and to characterize the incidents by phase in the medication-use process, by type of incident, and by drug responsible for toxic effects.Methods: A retrospective chart analysis was conducted using records from 2 acute care centres in the Regina Qu’Appelle Health Region. The study included inpatients who received naloxone for reversal of opioid toxicity resulting from licit, in-hospital opioid use. Cases were classified as preventable or nonpreventable. Preventable cases were analyzed to determine the phase of the medication-use process during which the incident occurred. These cases were also grouped thematically by the type of incident. The drug most likely responsible for opioid toxicity was determined for each case. The proportion of cases documented by occurrence reporting was also noted.Results: Thirty-six cases involving administration of naloxone were identified, of which 29 (81%) were deemed preventable. Of these 29 preventable cases, the primary medication incident occurred most frequently in the prescribing phase (23 [79%]), but multiple phases were often involved. The cases were grouped into 6 themes according to the type of incident. Morphine was the drug that most frequently resulted in toxic effects (18 cases [50%]). Only two of the cases (5.6%) were documented by occurrence reports.Conclusion: Preventable opioid incidents occurred in the acute care centres under study. A combination of medication safety initiatives involving multiple disciplines may be required to decrease the incidence of these events and to better document their occurrence.RÉSUMÉContexte : Les analgésiques opioïdes sont des médicaments qui commandent une vigilance élevée, connus pour les événements indésirables qu’ils entraînent.Objectifs : Le but de cette étude était de déterminer la cause des incidents attribuables aux opioïdes nécessitant l’administration de naloxone, un antidote des opioïdes. Les objectifs précis étaient de déterminer le nombre d’incidents attribuables aux opioïdes et la proportion d’incidents constatés par comptes rendus d’événements et de caractériser les incidents selon la phase dans le processus de distribution des médicaments, le type d’incident et l’agent responsable des effets toxiques.Méthodes : Une analyse rétrospective des dossiers médicaux des patients a été menée dans deux centres de soins de courte durée de la Regina Qu’Appelle Health Region. L’analyse incluait les patients hospitalisés ayant reçu de la naloxone pour neutraliser la toxicité opioïde attribuable à l’utilisation intrahospitalière licite d’opioïdes. Les cas d’incidents ont été classés comme étant évitables ou non évitables. Les cas évitables ont été analysés afin de déterminer la phase du processus de distribution des médicaments durant laquelle l’incident est survenu. Ces cas ont également été regroupés par type d’incident. Le médicament le plus susceptible d’avoir causé une toxicité opioïde a été déterminé pour chaque cas. La proportion de cas constatés par comptes rendus d’événements a aussi été notée.Résultats : On a relevé 36 cas nécessitant l’administration de naloxone, dont 29 (81 %) ont été jugés évitables. De ces 29 cas évitables, le principal incident lié au médicament est survenu le plus souvent dans la phase de prescription (23 [79 %]), mais plusieurs phases étaient souvent en cause. Les cas ont été regroupés en six types d’incidents. La morphine était l’agent ayant le plus souvent entraîné des effets toxiques (18 cas [50 %]). Seulement deux des cas (5,6 %) ont été constatés par comptes rendus d’événements.Conclusion : Des incidents évitables liés aux opioïdes sont survenus dans les deux centres de soins de courte durée faisant l’objet de la présente analyse. Une combinaison de mesures faisant appel à plusieurs disciplines pour améliorer la sécurité des médicaments pourrait être nécessaire afin de réduire l’incidence de tels événements et de constater leur occurrence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".