Histological quantification of decomposed human livers: a potential aid for estimation of the post-mortem interval?

The objective of this study was to determine if a novel scoring-based model for histological quantification of decomposed human livers could improve the precision of post-mortem interval (PMI) estimation for bodies from an indoor setting. The hepatic decomposition score (HDS) system created consists of five liver scores (HDS markers): cell nuclei and cell structure of hepatocytes, bile ducts, portal triad, and architecture. A total of 236 forensic autopsy cases were divided into a training dataset (n = 158) and a validation dataset (n = 78). All cases were also scored using the total body score (TBS) method. We specified a stochastic relationship between the log-transformed accumulated degree-days (log(10)ADD) and the taphonomic findings, using a multivariate regression model to compute the likelihood function. Three models were applied, based on (i) five HDS markers, (ii) three partial body scores (head, trunk, limbs), or (iii) a combination of the two. The predicted log(10)ADD was compared with the true log(10)ADD for each case. The fitted models performed equally well in the training dataset and the validation dataset. The model comprising both scoring methods had somewhat better precision than either method separately. Our results indicated that the HDS system was statistically robust. Combining the HDS markers with the partial body scores resulted in a better representation of the decomposition process and might improve PMI estimation of decomposed human remains.

Automated summary

By developing a novel scoring-based model, this study found that combining the HDS markers with partial body scores can improve the precision of post-mortem interval estimation for decomposed human remains. The HDS system was shown to be statistically robust and the model comprising both scoring methods had better accuracy than each method separately.