A SYSTEMATIC REVIEW ON THE EFFECTIVENESS OF OPIOID-SPARING STRATEGIES IN ICU PATIENTS
Bibliographic record
Abstract
Background: Opioid administration in the intensive care unit (ICU) has been a longstanding adjunct for analgesia. However, safety concerns surrounding opioid-related adverse effects, such as respiratory depression/tolerance/dependence, have generated renewed interest in adopting opioid-sparing strategies. Multimodal analgesia strategies using regional anesthesia, non-opioid pharmacologic agents, and integrative medicine approaches have been examined to decrease opioid use in select patients. While there are potential benefits, issues with effectiveness, ease of implementation, and susceptibility to alternative therapies must still be addressed. Objective: This study systematically reviews the effectiveness, benefits, and potential challenges of opioid-sparing strategies in ICU patients considering varying analgesic approaches. It aims to identify gaps in the existing research and to recommend future directions for maximizing pain management in critically ill patients. Methods: A systematic review approach was adopted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched up to October 2023 using PubMed, Google Scholar, Scopus, ScienceDirect, and Web of Science. Both peer-reviewed articles and articles published during the previous five years (2019 through present day) were searched. Eligible studies were included based on pre-established criteria, with a focus on high-quality evidence of opioid-sparing strategies in the ICU. Quality assessment of the included studies was performed by the use of the AMSTAR, the Cochrane risk of bias tool, or the Newcastle-Ottawa scale. Conclusion: The review results indicate that multimodal analgesia and opioid-sparing efficacy contribute to an enhanced pain management solution across surgical specialties that utilize regional anesthesia (45%), non-opioid pharmacologic agents (e.g. acetaminophen, ketamine) (50%) , and non-pharmacologic methods (e.g. cognitive-behavioral therapy, music therapy) (30%). The most commonly cited benefits are decreased opioid requirement (60%), fewer opioid-related side effects (50%), and improved patient outcomes in terms of early mobilization and reduced ICU length of stay (35%) Yet, barriers to adoption, including poor familiarity with alternative therapies (40%) among clinicians, inconsistent clinical guidelines (35%), and cost-related limitations (30%) stand in the way of widespread uptake. Other research priorities included the standardization of multimodal analgesia protocols (40%), improved education and training for healthcare providers (35%), and the incorporation of personalized pain management strategies (25%). This concludes that strategies that minimize the use of opioids in the ICU patient population provide greater risk management regarding the risk of long-term dependency and other complications related to opioid use. The conclusion of this review highlights the importance of future studies, the optimization of protocols, and further collaboration between different disciplines, to ensure optimal multimodal analgesia in critically ill patients. Implementation challenges, along with clinician training in newer opioid-sparing techniques, are critical for their adoption more broadly. This study reveals important information for researchers, clinicians, and policymakers who are seeking to improve pain management strategies and patient outcomes in ICUs.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.009 | 0.009 |
| 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".