Saad Manal H, Savonen Candace L, Rumschlag Matthew, Todi Sokol V, Schmidt Carl J, Bannon Michael J
Department of Pharmacology, Wayne State University School of Medicine, Detroit, MI, United States.
Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, United States.
Front Neurosci. 2018 Oct 23;12:728. doi: 10.3389/fnins.2018.00728. eCollection 2018.
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.
阿片类药物滥用如今是美国意外死亡的主要原因。几十年来的研究确立了滥用药物选择的周期性本质，当前海洛因滥用再度兴起，且最近芬太尼成为药物相关死亡的主要诱因。为了更好地理解滥用趋势并探究特定药物 - 药物相互作用的潜在致死性，我们对韦恩县法医办公室2012 - 2016年的法医毒理学数据进行了统计分析。我们观察到在此期间阿片类药物滥用有明显变化，包括从2014年起芬太尼及其类似物迅速成为极具致死性的重要原因。然后我们使用基于卡方自动相互作用检测器（CHAID）的决策树分析来深入了解最能预测死亡原因或死亡相关情况的特定药物、药物组合以及血液中的生物标志物。非阿片类药物对乙酰氨基酚的存在高度预示着药物相关死亡，这可能反映了各种对乙酰氨基酚 - 阿片类复方制剂的滥用。短效可卡因掺杂物左旋咪唑高度预示着在突然的非药物相关死亡前可卡因后的短生存时间。阿片类药物美沙酮和抗抑郁药西酞普兰的组合始终与药物死亡相关，这表明在对心脏的病理生理效应和/或药物代谢水平上可能存在药物 - 药物相互作用。芬太尼与苯二氮䓬类药物咪达唑仑同时存在可诊断为在严重疾病及包括这些药物的干预后的院内死亡。这些数据凸显了决策树分析的作用，不仅在于确定死亡原因，还在于作为一种关键监测工具，为药物滥用治疗及应对阿片类药物危机的公共卫生政策提供信息。
