The establishment of time since death (TSD) which is also known as the postmortem interval (PMI), is an important component of forensic investigation. Identifying TSD precisely leads investigators to pinpoint the period of death thus aiding criminal investigations apart from legal proceedings. The traditional estimation techniques for PMI such as rigor mortis, livor mortis, body cooling (algor mortis), and insect activity produce broad predictions. At the same time, their results depend on multiple external factors including ambient temperature and humidity as well as clothing conditions of the deceased [1, 2]. In recent years biochemical analysis of postmortem body fluids offers a more objective potentially precise method of estimating TSD. The vitreous humor found in the eye continues to be significant for forensic investigations because it exists in an isolated space within the body with slow self-decomposition while resisting decomposition even in cases of advanced body decomposition [3,4]. Progressive electrolyte changes in the gelatinous eye substance VH located in the posterior chamber following death make it a dependable method for determining PMI. Research on potassium (K⁺) concentration stands as among the most thoroughly studied biochemical aspects within Vitreous Humor (VH). Terminating active cellular transport mechanisms following death produces a passive potassium diffusion from cells into vitreous liquid [5]. The potassium levels in VH steadily rise as numerous studies confirm their direct connection to time since death for the initial death period of up to 100 hours [6,7]. Potassium changes at a consistent rate which remains unaffected by age demographic as well as sex differences or cause of death and environmental temperature variations thus serving as a reliable PMI indicator [8]. Multiple researchers have developed regression equations to estimate TSD using violent death potassium levels according to Madea, Sturner, and Coe among other proposed models [9]. The accuracy of using VH potassium levels for estimating time since death remains influenced by storage conditions and sampling techniques as well as analytical timing. The consistent reliability of this method demands both population-specific regression modeling and standardized collection protocols and analysis procedures according to [10]. The current study aimed to estimate the time since death using potassium levels in vitreous humor. The results of this analysis could help to reinforce the forensic application of VH potassium as a valuable tool in death investigation.

This cross-sectional study was conducted in the Department of Forensic Medicine, Rajiv Gandhi Institute of Medical Sciences (RIMS), Adilabad. Institutional Ethical approval was obtained for the study. The study was done on 100 cases of PME which included a Control Group (n = 50): Cases with a reliably known time of death. Study Group (n = 50): Cases with an unknown time of death.

Included Cases

1. Clear vitreous humor samples.
2. Cases with normal electrolyte balance prior to death, confirmed by a normal urea concentration in vitreous fluid (for unknown death time group).

Excluded Cases

1. Turbid or blood-contaminated vitreous samples.
2. Known electrolyte disturbances prior to death.
3. Decomposed bodies or those with ocular trauma.

Demographic and circumstantial data such as name, age, sex, presumed time of death, cause of death, and environmental conditions were obtained from hospital records, eyewitnesses, or accompanying relatives and were documented in a structured proforma. At the time of sample collection, ambient temperature (°C) and relative humidity (%) were recorded to account for environmental influences on postmortem changes. From each deceased individual, two samples were collected one from each eye just before the commencement of the postmortem examination. These were labeled as Sample 1 (right eye) and Sample 2 (left eye) and treated as matched pairs for internal comparison.

Vitreous Humor Collection Method: 

Vitreous humor was aspirated from the posterior chamber using a sterile 10 ml syringe with a 20-gauge needle, inserted approximately 5–6 mm posterior to the limbus to avoid damaging ocular structures. Care was taken to prevent mixing with tissue fragments or blood. The maximum volume possible was aspirated to ensure representation of the humor in close contact with the retina, where potassium changes first manifest. To preserve facial appearance, the aspirated volume was replaced with liquid paraffin gel. Samples were transferred into sterile, rubber-stoppered test tubes and immediately sent to the Central Laboratory, Department of Biochemistry for analysis. Biochemical Analysis: Vitreous potassium and urea levels were measured using an automated biochemistry analyzer.

Statistical Analysis:

All statistical analyses were uploaded to an MS Excel spreadsheet and analyzed by SPSS version 23.0 in Windows format. The continuous variables were represented as frequency, mean, standard deviation, and percentage, and the categorical variables were calculated by Pearson coefficient correlation and logistic regression analysis. The values of p (<0.05) were considered as significant.

A total of n=100 cases were included in the study. Table 1 shows the demographic distribution and causes of death in both control and study groups (n=50 each). The mean age in the control group was 45.2 years, and 47.6 years in the study group, with a slightly higher proportion of males in both groups. Cardiovascular conditions were the most common presumed cause of death (36% in control; 40% in the study), followed by trauma and respiratory failure. These baseline characteristics suggest a demographically comparable distribution, supporting the reliability of comparative potassium-based PMI analysis between the two groups.

Table 2 shows the assessment of Vitreous Potassium (K⁺) Levels vs. Time Since Death (Control Group). A critical analysis of the table shows that Potassium levels in vitreous humor increased progressively with postmortem interval (PMI). In the first 6 hours after death, the mean K⁺ concentration was 6.2 mmol/L. This rose steadily to 8.5 mmol/L at 6–12 hours, 12.7 mmol/L at 12–24 hours, and peaked at 18.4 mmol/L between 24–48 hours. The data confirms a strong positive relationship between PMI and vitreous potassium levels, supporting its use as a biochemical marker for estimating time since death in forensic investigations.

Table 3 shows the Regression Analysis for Time Since Death Estimation. This regression model evaluates the correlation between vitreous potassium concentration and PMI. The regression coefficient was 2.1 hours per mmol/L, with a strong correlation (R² = 0.89) and statistically significant p-value (<0.001). The regression equation is PMI = (2.1 × K⁺) – 3.5 enables practical estimation of PMI based on K⁺ concentration. The model's tight confidence intervals further validate the precision of this formula, making it a potentially valuable forensic tool in time-of-death estimations.

                      *Significant

Table 4 shows the validation of the Model in the Control Group this Model's accuracy was assessed using error metrics and correlation analysis. The Mean Absolute Error (MAE) was 1.2 hours, and the Root Mean Square Error (RMSE) was 1.8 hours, indicating low prediction error. The model demonstrated an excellent correlation (r = 0.93) between predicted and actual PMI. The 95% prediction interval of ±3.5 hours provides a reliable estimate range for forensic applications. These results reinforce the robustness of the potassium-based regression model for time of death estimation.

Table 5 depicts the estimated time since death in the study group. The application of the regression equation to the study group (with unknown time of death) resulted in estimated PMIs across four intervals: 16% of cases fell within 0–6 hours, 28% within 6–12 hours, 36% within 12–24 hours, and 20% within 24–48 hours. These estimations closely paralleled investigative findings, validating the practical utility of the regression model. The alignment of potassium-derived and investigative estimates further affirms the relevance of vitreous potassium levels as a dependable postmortem indicator.

The following are important outcomes in the results of the study. Control Group: Potassium levels in vitreous humor showed a strong linear relationship with time since death (R² = 0.89). Study Group: The regression model estimated the time of death within a mean error of 1.2 hours compared to investigative estimates. Limitations: Variability increased beyond 24 hours due to environmental factors (e.g., temperature).

The exact estimation of postmortem interval (PMI) is an important consideration in forensic investigations. Apart from other markers studied recently study of biochemical markers, like vitreous humor potassium concentration has emerged as a practical and reliable indicator because of its relative stability and linear increase following death. The results of the present study reinforced the utility of potassium levels for PMI estimation and the findings align with other similar studies and further validate its forensic relevance. The results of this study showed that on regression analysis there was a strong linear relationship between vitreous potassium concentration and time since death. The derived equation was PMI = (2.1 × K⁺) – 3.5. The findings of our study are in concordance with other prior studies done in this field such as by Madea et al. [11] who reported a correlation coefficient (r) of 0.84 between vitreous potassium and PMI showing a similar predictive reliability. Our study found a stronger correlation (R2 = 0.89) and correlation coefficient of 0.93 in validation showing a strong association. The progressive rise in potassium levels with increasing PMI as depicted in Table 2 validates the pathophysiological understanding that postmortem there is cellular breakdown and loss of integrity of cell membrane consequently there will be leakage of intracellular potassium into the extracellular and vitreous compartments [12]. We observed a level of 6.2 mmol/L in the first 6 hours postmortem. It increased to 18.4 mmol/L in 48 hours. These findings align with previous studies such as by Jashnani et al. [13] where they observed levels ranging from 5.5 to 19.0 mmol/L over a similar postmortem period. The main strength of our model originates from its validation process with genuine control data. The model demonstrates clinical and forensic values as shown through its low Mean Absolute Error of 1.2 hours and RMSE of 1.8 hours and its 95% prediction interval ranged from -3.5 to +3.5 hours. Biochemical PMI estimation models in the literature exhibit error ranges that the research findings match easily [14]. The model yielded outstanding agreement with independent investigative time-of-death assessments when tested on cases with unidentified death times. Our regression model proves valuable both statistically and works effectively under operational conditions in the field. Zilg et al. reached similar conclusions when they established that PMI predictions from vitreous biochemistry regression models remained accurate despite changing environmental factors [15]. However, the limitations of this study should be acknowledged. The model assumes a relatively consistent rate of potassium accumulation, which may be influenced by environmental factors such as temperature and humidity. Although we controlled for extreme sample deviations (e.g., excluding cases with electrolyte imbalances or contaminated samples), factors like sepsis, trauma, and hypothermia can still affect postmortem biochemical changes [16]. In the end, our findings confirm that vitreous humor potassium concentration is a reliable biomarker for PMI estimation. With proper standardization and environmental consideration, this biochemical method offers a non-invasive, cost-effective, and accurate approach for forensic time-of-death determination.

In conclusion, our study confirmed the utility of vitreous humor potassium concentration as a reliable biochemical marker for estimating time since death (PMI). We found a strong linear correlation between the two was present. This enables the derivation of a predictive formula with high accuracy and minimal error. The predictive formula accurately estimated unknown death times by matching the model results with independent investigator estimates. This analytical approach provides beneficial features, such as stable sample collection and easy laboratory examination, which makes it useful for forensic investigations. Further research incorporating environmental and pathological variables may enhance its accuracy and broaden its applicability to diverse forensic scenarios.

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