ArticleThe AAPS journal2016
A Model-Based Approach for Joint Analysis of Pain Intensity and Opioid Consumption in Postoperative Pain.
Article in The AAPS journal, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Pain when it "counts": hurdle analysis of clinical pain ratings improves data model performance.Pain reports · 2025Article
- Current Status and Future Directions of Pain-Related Outcome Measures for Post-Surgical Pain Trials.Canadian journal of pain = Revue canadienne de la douleur · 2019Review
- Acute postoperative pain after arthroscopic rotator cuff surgery: A review of methods of pain assessment.SICOT-J · 2018Article
- Quantification of the Pharmacodynamic Interaction of Morphine and Gabapentin Using a Response Surface Approach.The AAPS journal · 2017Article
- Analysis of opioid consumption in clinical trials: a simulation based analysis of power of four approaches.Journal of pharmacokinetics and pharmacodynamics · 2017Article
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Joint analysis of pain intensity and opioid consumption is encouraged in trials of postoperative pain. However, previous approaches have not appropriately addressed the complexity of their interrelation in time. In this study, we applied a non-linear mixed effects model to simultaneously study pain intensity and opioid consumption in a 4-h postoperative period for 44 patients undergoing percutaneous kidney stone surgery. Analysis was based on 748 Numerical Rating Scale (NRS) scores of pain intensity and 51 observed morphine and oxycodone dosing events. A joint model was developed to describe the recurrent pattern of four key phases determining the development of pain intensity and opioid consumption in time; (A) Distribution of pain intensity scores which followed a truncated Poisson distribution with time-dependent mean score ranging from 0.93 to 2.45; (B) Probability of transition to threshold pain levels (NRS ≥ 3) which was strongly dependent on previous pain levels ranging from 2.8-15.2% after NRS of 0-2; (C) Probability of requesting opioid when allowed (NRS ≥ 3) which was strongly correlated with the number of previous doses, ranging from 89.8% for requesting the first dose to 26.1% after three previous doses; (D) Reduction in pain scores after opioid dosing which was significantly related to the pain intensity at time of opioid request (P < 0.001). This study highlights the importance of analyzing pain intensity and opioid consumption in an integrated manner. Non-linear mixed effects modeling proved a valuable tool for analysis of interventions that affect pain intensity, probability of rescue dosing or the effect of opioids in the postoperative pain period.
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