Mapping opioid exposure through prescription data and postmortem analysis of opioid drugs in multiple tissues
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Mapping opioid exposure through prescription data and postmortem analysis of opioid drugs in multiple tissues

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Abstract

BACKGROUND AND PURPOSE: Although opioids are central to end of life (EoL) care, tissue-level opioid exposure remains poorly understood. The objective of this study was to characterize the relationship between prescription-derived morphine equivalent daily dose (MEDD) and measured morphine concentrations across multiple organs. EXPERIMENTAL APPROACH: We analysed data from the Last Gift cohort, a community-centred HIV research rapid autopsy programme. Cumulative MEDD for the final 7 and 30 days before death (MEDD-7, MEDD-30) was calculated using prescription data. Postmortem samples from multiple organs were analysed using ultra-high-performance liquid chromatography with tandem mass spectrometry to quantify opioids. Mixed-effects regression models and Pearson correlations evaluated relationships between MEDD and tissue morphine concentrations. KEY RESULTS: Among 261 samples from 27 participants (median age 65 years), 96% had ≥1 detectable opioid. Morphine was most frequently prescribed (78%), followed by fentanyl (41%), hydromorphone (37%), and oxycodone (33%). Median MEDD-7 and MEDD-30 were 940 (IQR 158-3481) and 3430 (IQR 563-9972), respectively. Morphine concentrations were highest in the ascending colon, kidney, duodenum, and liver, and lowest in adipose tissue and cerebrospinal fluid. Tissue morphine concentrations correlated with both MEDD-7 and MEDD-30 (e.g., medulla r = .80; spinal cord r = .77; parietal cortex r = .72; all p < .01). In adjusted mixed-effects models, each 10-fold increase in MEDD-7/-30 predicted a 3.7- to 3.8-fold increase in tissue morphine concentration. CONCLUSION AND IMPLICATIONS: Prescription-based morphine exposure was strongly associated with morphine tissue concentrations across multiple organs, providing a quantitative framework for integrating pharmacologic data into EoL research.

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