Prediction model for anabolic androgenic steroid positivity in forensic autopsy cases - a new tool to the autopsy room.

ABSTRACT

Non-prescription use of anabolic androgenic steroids (AAS) is associated with an increased risk of premature death. However, these substances are seldom screened in connection with forensic cause-of-death investigation, unless the forensic pathologist specifically suspects use, often based on a positive AAS use history. Since AAS use is often concealed from others, this practice may lead to mistargeting of these analyses and significant underestimation of the true number of AAS positive cases undergoing forensic autopsy. Thus, more accurate diagnostic tools are needed to identify these cases. The main objective of this study was to determine, whether a multivariable model could predict AAS urine assay positivity in forensic autopsies. We analyzed retrospectively the autopsy reports of all cases that had been screened for AAS during forensic cause-of-death investigation between 2016-2019 at the Finnish Institute for Health and Welfare forensic units (n = 46). Binary logistic regression with penalized maximum likelihood estimation was used to generate a nine-variable model combining circumferential and macroscopic autopsy-derived variables. The multivariable model predicted AAS assay positivity significantly better than a "conventional" model with anamnestic information about AAS use only (area under the receiver operating characteristic curve [AUC] = 0.968 vs. 0.802, p = 0.005). Temporal validation was conducted in an independent sample of AAS screened cases between 2020-2022 (n = 31), where the superiority of the multivariable model was replicated (AUC = 0.856 vs. 0.644, p = 0.004). Based on the model, a calculator predicting AAS assay positivity is released as a decision-aiding tool for forensic pathologists working in the autopsy room.