Opioid-associated cardiac arrest: A systematic review of intra-arrest naloxone and other opioid-specific advanced life-support therapies
Bibliographic record
Abstract
Aim: Cardiac arrest due to opioid toxicity is a leading cause of life-years lost in many countries. Since the pathophysiology of cardiac arrest from opioid toxicity is different than primary cardiac etiologies, we sought to identify opioid-specific resuscitative interventions demonstrating benefit. Methods: We searched Medline, EMBASE, CENTRAL, and the Web of Science (September 2024) for randomized or observational studies examining the benefit of opioid-specific advanced life support-level therapies for cardiac arrest. The primary and secondary outcomes were favourable neurological outcomes and survival at 30-days or hospital discharge, respectively. Risk of Bias and Certainty of Evidence were assessed with the ROBINS-I tool and GRADE methodology, respectively. Results: We reviewed 1051 studies; six observational studies met criteria for analysis. Five studies examined the association of naloxone and outcomes (three included undifferentiated cases, one included non-shockable initial rhythm cases, and two included cases with "drug overdose"): two reported that naloxone was associated with improved outcomes, and three did not detect an association. One additional study examined the association of bicarbonate and outcomes, reporting that bicarbonate was associated with decreased survival at hospital discharge. All studies were limited by serious risk of bias and indirectness, with the certainty of evidence judged to be very low. No studies exclusively examined opioid-related cases. Conclusions: There is currently no evidence demonstrating benefit for any advanced life support interventions specific to treating cardiac arrest from opioid toxicity. Data examining naloxone for undifferentiated or "drug-related" cardiac arrest are heterogenous with high risk of bias and low certainty of evidence.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".