Opioid deaths involving concurrent benzodiazepine use: Assessing risk factors through the analysis of prescription drug monitoring data and postmortem toxicology.

BACKGROUND: A high proportion of opioid drug deaths involve concurrent benzodiazepine use. To reduce the risk of drug overdose, various prescription drug monitoring programs have been implemented. This study examined the impact of concurrent benzodiazepine use on opioid-related deaths, and the utility of the Michigan Automated Prescription System (MAPS) in predicting risk of opioid death. METHODS: Wayne County Medical Examiner's Office cases from 2018 were examined in terms of MAPS data and MAPS-derived drug risk scores, as well as postmortem toxicology. Opioid death cases with concurrent benzodiazepine use were compared to non-drug deaths. RESULTS: For cases with a MAPS history for 6 months preceding death, the incidence of opioid prescriptions filled did not differ between groups. In contrast, significantly more opioid death cases had filled a benzodiazepine prescription; alprazolam prescription was the single best predictor of opioid drug death. Groups differed in MAPS-calculated drug risk scores, though these were less predictive of opioid death than some individual measures of prescription drug use. In terms of postmortem toxicology, fentanyl was the best discriminator between cohorts, with significant associations seen for morphine, benzodiazepine, or cocaine use. Similar results were obtained in the subset of subjects filling a prescription within a month of death, except that MAPS risk scores no longer predicted drug deaths. CONCLUSION: MAPS scores did not adequately predict risk of opioid-related death. Contrary to expectations, prescription opioid use was not correlated with opioid-related death, whereas concurrent use of opioids and benzodiazepines represented a highly significant risk factor.

Differentially expressed gene networks, biomarkers, long noncoding RNAs, and shared responses with cocaine identified in the midbrains of human opioid abusers.

Opioid abuse is now the most common cause of accidental death in the US. Although opioids and most other drugs of abuse acutely increase signaling mediated by midbrain dopamine (DA)-synthesizing neurons, little is known about long-lasting changes in DA cells that may contribute to continued opioid abuse, craving, and relapse. A better understanding of the molecular and cellular bases of opioid abuse could lead to advancements in therapeutics. This study comprises, to our knowledge, the first unbiased examination of genome-wide changes in midbrain gene expression associated with human opioid abuse. Our analyses identified differentially expressed genes and distinct gene networks associated with opioid abuse, specific genes with predictive capability for subject assignment to the opioid abuse cohort, and genes most similarly affected in chronic opioid and cocaine abusers. We also identified differentially expressed long noncoding RNAs capable of regulating known drug-responsive protein-coding genes. Opioid-regulated genes identified in this study warrant further investigation as potential biomarkers and/or therapeutic targets for human substance abuse.

Opioid Deaths: Trends, Biomarkers, and Potential Drug Interactions Revealed by Decision Tree Analyses.

Opioid abuse is now the primary cause of accidental deaths in the United States. Studies over several decades established the cyclical nature of abused drugs of choice, with a current resurgence of heroin abuse and, more recently, fentanyl's emergence as a major precipitant of drug-related deaths. To better understand abuse trends and to explore the potential lethality of specific drug-drug interactions, we conducted statistical analyses of forensic toxicological data from the Wayne County Medical Examiner's Office from 2012-2016. We observed clear changes in opioid abuse over this period, including the rapid emergence of fentanyl and its analogs as highly significant causes of lethality starting in 2014. We then used Chi-square Automatic Interaction Detector (CHAID)-based decision tree analyses to obtain insights regarding specific drugs, drug combinations, and biomarkers in blood most predictive of cause of death or circumstances surrounding death. The presence of the non-opioid drug acetaminophen was highly predictive of drug-related deaths, likely reflecting the abuse of various combined acetaminophen-opioid formulations. The short-lived cocaine adulterant levamisole was highly predictive of a short post-cocaine survival time preceding sudden non-drug-related death. The combination of the opioid methadone and the antidepressant citalopram was uniformly linked to drug death, suggesting a potential drug-drug interaction at the level of a pathophysiological effect on the heart and/or drug metabolism. The presence of fentanyl plus the benzodiazepine midazolam was diagnostic for in-hospital deaths following serious medical illness and interventions that included these drugs. These data highlight the power of decision tree analyses not only in the determination of cause of death, but also as a key surveillance tool to inform drug abuse treatment and public health policies for combating the opioid crisis.