Evaluation of clinical and inflammatory profile in opioid addiction patients with comorbid pain: results from a multicenter investigation
Bibliographic record
Abstract
Brittany B Dennis,1 M Constantine Samaan,2 Monica Bawor,3 James Paul,4 Carolyn Plater,5 Guillaume Pare,1 Andrew Worster,6 Michael Varenbut,5 Jeff Daiter,5 David C Marsh,5,7 Dipika Desai,8 Lehana Thabane,1,9,10 Zainab Samaan1,8,11 1Department of Clinical Epidemiology and Biostatistics, 2Department of Pediatrics, Division of Pediatric Endocrinology, 3McMaster Integrative Neuroscience Discovery and Study Program, 4Department of Anesthesia, McMaster University, Hamilton, 5Ontario Addiction Treatment Centres, Richmond Hill, 6Department of Medicine, Hamilton General Hospital, Hamilton, 7Northern Ontario School of Medicine, Sudbury, 8Population Genomics Program, Chanchlani Research Centre, McMaster University, Hamilton, 9Centre for Evaluation of Medicine, 10System Linked Research Unit, Hamilton, 11Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada Background: Chronic pain is the most commonly reported comorbidity among patients with opioid addiction receiving methadone maintenance treatment (MMT), with an estimated prevalence ranging between 30% and 55%. Evidence suggests that patients with comorbid pain are at high risk for poor treatment response, including continued illicit substance use. Due to the important relationship between the presence of pain and illicit substance abuse within the MMT setting, it is imperative that we target our efforts toward understanding the characteristics of this patient population.Methods: The primary objective of this study was to explore the clinical and inflammatory profile of MMT patients reporting comorbid pain. This multicenter study enrolled patients (n=235) on MMT for the treatment of opioid dependence. Clinical history and blood and urine data were collected. Blood samples were obtained for estimating the serum levels of inflammatory markers (tumor necrosis factor [TNF]-α, interleukin-1 receptor antagonist [IL-1ra], IL-6, IL-8, IL-10, interferon [IFN]-γ and chemokine (C–C motif) ligand 2 [CCL2]). The study objectives were addressed using a descriptive statistical summary and a multivariable logistic regression model constructed in STATA version 12.Results: Among the participants eligible for inclusion (n=235), serum IFN-γ level and substance abuse behavior proved to be important delineating characteristics for the detection of comorbid pain. Analysis of inflammatory profile showed IFN-γ to be significantly elevated among patients reporting comorbid pain (odds ratio [OR]: 2.02; 95% confidence interval [CI]: 1.17, 3.50; P=0.01). Patients reporting comorbid pain were also found to have an increase in positive opioid urine screens (OR: 1.02; 95% CI: 1.00, 1.03; P=0.01), indicating an increase in illicit opioid consumption.Conclusion: MMT patients with comorbid pain were shown to have elevated IFN-γ and higher rates of continued opioid abuse. The ability to objectively distinguish between patients with comorbid pain may help to both improve the prediction of poor responders to MMT as well as identify treatment approaches such as anti-inflammatory medications as safe alternatives for MMT patients with comorbid pain. Keywords: methadone maintenance treatment, inflammatory markers, TNF-α, IFN-γ, interleukins, CCL2, Brief Pain Inventory, opioid dependence
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".