ABSTRACT
Although the past few years have seen a significant increase in the use of synthetic cannabinoids, very few fatalities have been reported whereby synthetic cannabinoids have contributed or were solely responsible for the death of an individual. We report a rapid death of an individual following ingestion of 5â¯F-PB-22 and 5â¯F-AKB-48. Case information and autopsy findings are presented. Post-mortem blood samples were obtained and 5â¯F-PB-22 and 5â¯F-AKB-48 were detected along with 311 mg/100â¯ml alcohol. The cause of death was certified as the effects of a combination of alcohol and synthetic cannabinoids (5â¯F-PB-22 and 5â¯F-AKB-48).
Subject(s)
Cannabinoids/poisoning , Death, Sudden/etiology , Designer Drugs/poisoning , Substance-Related Disorders/complications , Adult , Cannabinoids/blood , Central Nervous System Depressants/analysis , Central Nervous System Depressants/poisoning , Designer Drugs/analysis , Ethanol/analysis , Ethanol/poisoning , Humans , MaleABSTRACT
The challenge of interpreting post-mortem drug concentrations is well documented and relies on appropriate sample collection, knowledge of case circumstances as well as reference to published tables of data, whilst taking into account the known issues of post-mortem drug redistribution and tolerance. Existing published data has evolved from simple data tables to those now including sample origin and single to poly drug use, but additional information tends to be specific to those reported in individual case studies. We have developed a Bayesian network framework to assign a likelihood of fatality based on the contribution of drug concentrations whilst taking into account the pathological findings. This expert system has been tested against casework within the coronial jurisdiction of Sunderland, UK. We demonstrate in this pilot study that the Bayesian network can be used to proffer a degree of confidence in how deaths may be reported in cases when drugs are implicated. It has also highlighted the potential for deaths to be reported according to the pathological states at post-mortem when drugs have a significant contribution that may have an impact on mortality statistics. The Bayesian network could be used as complementary approach to assist in the interpretation of post-mortem drug concentrations.