Effectiveness of Using Potassium Levels in Vitreous Humour for Estimating Postmortem Interval - A Systematic Review - Jagadish Ramasamy, (International Journal of Medical Toxicology and Legal Medicine, 2024)
Background: Estimating the Postmortem Interval (PMI) is a requirement in medico-legal autopsy, and often, it poses challenges due to environmental factors and body conditions. A biochemical approach, especially using potassium levels in vitreous humor (VH), is a widely proposed method to estimate PMI. Hence, this systematic review is done to analyse the effectiveness of using vitreous humor potassium levels in estimating PMI. Methodology: We searched three databases (MEDLINE, Scopus, and ProQuest) to identify studies analysing vitreous potassium concentration for PMI estimation. Data extracted from included studies encompassed analytical methods employed, the range of vitreous potassium, and the impact of temperature and humidity on PMI. Results: The electronic search identified 471 articles that were subsequently screened based on the inclusion criteria, and 53 studies were found eligible for qualitative synthesis. Forty studies have reported the actual PMI for the subjects they studied, and it ranged from 0 hours to 408 hours. 5 studies (12.5%) had PMI < 24 hours and 15 studies (37.5 %) included subjects with PMI > 72 hours. Among the eligible studies, 25 studies proposed regression equations to estimate PMI using vitreous humor potassium levels. The majority of them used only potassium (n=21), and few studies (n=4) have used vitreous levels of chloride, uric acid, hypoxanthine, albumin, sodium along with potassium to derive a regression equation to estimate PMI. Conclusions: Most of the studies have validated their proposed regression equations in the same subjects from which they were derived. Advanced statistical methods like generalized additive modelling and artificial neural networks have been shown to predict PMI much better than simple regression equations. The reporting of the standard error of the regression coefficient is recommended to enable quantitative analysis of the data, i.e., meta-analysis.