Personalized risk communication and opioid prescribing in association with nonprescribed opioid use: A secondary analysis of a randomized controlled trial
Abstract
BACKGROUND: To determine the impact of personalized risk communication and opioid prescribing on nonprescribed opioid use, we conducted a secondary analysis of randomized controlled trial participants followed prospectively for 90 days after an emergency department (ED) visit for acute back or kidney stone pain.
METHODS: A total of 1301 individuals were randomized during an encounter at four academic EDs into a probabilistic risk tool (PRT) arm, a narrative-enhanced PRT arm, or a general risk information arm (control). In this secondary analysis, both risk tool arms were combined and compared with the control arm. We used logistic regressions to determine associations between receiving personalized risk information, receiving an opioid prescription in the ED, and nonprescribed opioid use in general and by race.
RESULTS: Complete follow-up data were available for 851 participants; 23.3% (n = 198) were prescribed opioids (34.2% of White vs. 11.6% of Black participants, p < 0.001). Fifty-six (6.6%) participants used nonprescribed opioids. Participants in the personalized risk communication arms had lower nonprescribed opioid use odds (adjusted odds ratio [aOR] 0.58, 95% confidence interval [CI] 0.4-0.83). Black versus White participants had greater nonprescribed opioid use odds (aOR 3.47, 95% CI 2.05-5.87, p < 0.001). Black participants who were prescribed opioids had a lower marginal probability of using nonprescribed opioids versus those who were not (0.06, 95% CI 0.04-0.08,…
How strong is the evidence?
Evidence Hub reads this as a secondary / pooled analysis, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 1,301 participants or records.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.