Risk factors for severe opioid-induced respiratory depression in hospitalized adults: A case–control study
Bibliographic record
Abstract
Background: Opioids are commonly prescribed to hospitalized adults to promote adequate pain relief, yet they can cause potentially fatal respiratory depression.Aim: The aim of this study was to examine the risk factors for the development of severe opioid-induced respiratory depression (OIRD) in hospitalized adults to ensure adequate monitoring of high-risk patients.Methods: A retrospective case–control study was conducted using data from the medical records of a university-affiliated hospital in Canada. Cases were eligible if they were adults (≥18 years old) and received opioid analgesia within 24 h of naloxone administration for respiratory depression. Controls had the same eligibility criteria, except for respiratory depression and naloxone administration. The case–control ratio was 1:1, and they were matched based on sex, type of unit, opioid molecule and the presence/absence of medication errors.Results: A total of 133 cases and 133 controls were included. Following cumulative risk factor analysis, renal failure (odds ratio [OR] = 2.176, 95% confidence interval [CI], 1.021–4.640, P = 0.044), the first 24 h of opioid administration (OR = 1.899, 95% CI, 1.090–3.309, P = 0.024), concomitant central nervous system (CNS) depressants (OR = 1.785, 95% CI, 1.023–3.113, P = 0.041), and increasing age (OR = 1.019, 95% CI, 1.002–1.035, P = 0.028) were positively associated with severe OIRD.Conclusions: Some adult hospitalized patients were at higher risk of experiencing severe OIRD, such as those with renal failure, those in their first 24 h of opioid administration, those receiving CNS depressants in addition to opioids, and those with an advanced age. These results will assist with the screening of patients at higher risk for severe OIRD, which is key to implementing appropriate monitoring and enhancing the safety of opioid use in hospital settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".