Effects of postmortem interval and cause of death on organ weights

Yosuke Usumoto; Keiko Kudo; Akiko Tsuji; Yoko Ihama; Noriaki Ikeda

doi:10.4103/sjfms.sjfms_12_18

ORIGINAL ARTICLE

Year : 2018 | Volume : 1 | Issue : 3 | Page : 55-60

Effects of postmortem interval and cause of death on organ weights

Yosuke Usumoto¹, Keiko Kudo², Akiko Tsuji², Yoko Ihama³, Noriaki Ikeda²
¹ Department of Legal Medicine, Graduate School of Medicine, Yokohama City University, Yokohama-Shi, Kanagawa; Department of Forensic Pathology and Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka-Shi, Fukuoka, Japan
² Department of Forensic Pathology and Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka-Shi, Fukuoka, Japan
³ Department of Legal Medicine, Graduate School of Medicine, Yokohama City University, Yokohama-Shi, Kanagawa, Japan

Date of Submission	09-Jul-2018
Date of Decision	08-Nov-2018
Date of Acceptance	02-Dec-2019
Date of Web Publication	19-Dec-2019

Correspondence Address:
Dr. Yosuke Usumoto
Department of Legal Medicine, Graduate School of Medicine, Yokohama City University, 3.9, Fuku.Ura, Kanazawa.Ku, Yokohama.Shi, Kanagawa 236-0004
Japan

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/sjfms.sjfms_12_18

Abstract

Context: Visceral congestion is a characteristic autopsy finding of some causes of death, and the congestion changes the organ weight. Therefore, comparing the measured organ weights against standards can provide useful information. Aim: We sought to generate accurate organ weight standards according to the postmortem interval (PMI) and the cause of death, with particular focus on brain and lung weights. Settings and Design: Retrospective study. Cadavers and Methods: We included data from 489 (320 males and 169 females) forensic autopsy cases with known PMIs; within 4 days. We considered gender, age, height, body weight and PMI in our organ weight estimations. In addition, we used longitudinal (243 males and 131 females) and transverse (243 males and 130 females) skull diameters in constructing an equation for brain weight estimation. Statistical Analysis Used: Chi-square test, Welch's t-test and stepwise regression analysis. Results: Causes of death such as intracranial injury and bleeding, intoxication and drowning tended to increase lung weight; other causes tended to decrease lung weight. When we focused on cases with 1-day PMI, the brain and lung weights increased with longer PMIs, probably due to the brain oedema and pulmonary congestion and oedema in the early post-mortem period. Conclusion: Ours was the first report on the increase of brain weight with the increase of PMI in the early postmortem period. Further studies on the effects of PMI and the cause of death on organ weight are required to expand our understanding on the mechanisms of death.

Keywords: Cause of death, organ weight, postmortem change, postmortem interval, statistical approach

How to cite this article:
Usumoto Y, Kudo K, Tsuji A, Ihama Y, Ikeda N. Effects of postmortem interval and cause of death on organ weights. Saudi J Forensic Med Sci 2018;1:55-60

How to cite this URL:
Usumoto Y, Kudo K, Tsuji A, Ihama Y, Ikeda N. Effects of postmortem interval and cause of death on organ weights. Saudi J Forensic Med Sci [serial online] 2018 [cited 2023 Mar 24];1:55-60. Available from: https://www.sjfms.org/text.asp?2018/1/3/55/273577

Introduction

Visceral congestion is detected during autopsies after acute deaths. Organ findings and weights help pinpoint the cause of death. Although organ weights can be determined only during autopsy, the weight differs from that at the time of death, even though the organ weight at the time of autopsy reflects the cause of death. Thus, imaging techniques have been used to detect organ changes after death.^{[1],[2],[3],[4],[5]} We have used statistical techniques to study the effect of the postmortem interval (PMI) on organ weights. Our first estimates suggested increase in lung weight during the first day of the PMI.^[6] Here, we analysed the effect of PMI and the cause of death on organ weights, and generated improved equations for estimating the standard organ weights.

Cadavers and Methods

Cadavers

We analysed data from 489 (320 males and 169 females) forensic autopsy cases with ages over 19 years and PMIs within 4 days, examined at the Kyushu University between May 1997 and April 2012. A board-certified forensic pathologist performed most of the autopsies and determined the cause of death whereas, the remaining autopsies were performed by three board-certified forensic pathologists. The PMIs were determined after full autopsy examinations and complete police investigations. We excluded cases that had ambiguous PMIs, charred bodies, cadavers missing internal organs due to putrefaction, skeletonisation, or encroachment.^[6] We collected the following information from autopsy records: the cause of death, gender, age, height, body weight, PMI, longitudinal (243 males and 131 females) and transverse (243 males and 130 females) skull diameters as well as the organ weights of the brain, heart, left and right lungs, left and right kidneys, spleen, pancreas and liver. We measured the maximum longitudinal and transverse calvarial diameters after opening the skull at autopsy by visual observation. We could measure the maximum longitudinal and transverse calvarial diameters in some of our cases but not in all because of fractures such as those caused by trauma and fire.

Statistical analysis

We used JMP^®, Version 12.2, Japanese Edition (SAS Institute, Cary, NC, USA) to perform all the statistical analyses.

We calculated body mass indices (BMI) and body surface areas (BSA) as follows:

BMI (kg/m²) = body weight (kg)/height² (m²)^[7]

BSA (cm²) = 100.315 × body weight^0.383 (kg) × height^0.693 (cm)^[8]

We expressed all these variables as means ± standard deviation, and the numbers in parentheses show minimum and maximum values. We used a Chi-square test to compare the male to female ratios. We determined differences between the two groups based on the Welch's t-test results. Our stepwise regression analyses (P = 0.25, backward and forward) established the predictive equations for ascertaining organ weights according to selected factors such as gender, age, BMI, BSA and PMI. In addition, we added longitudinal and transverse skull diameters to the equation for brain weights (based on data from 243 male and 130 female cases). To evaluate the effect of PMI on brain and lung weights in cases with PMIs within 1 day, we generated equations for estimating brain (based on data from 137 male and 77 female cases) and lung (based on data from 194 male and 103 female cases) weights within that 1-day PMI. We checked the variance inflation factors (VIFs) to avoid multi-collinearity. We excluded the variable showing VIF values that exceeded 10. P values lower than 0.05 were considered statistically significant.

We handled data according to the privacy policy (2006) of the Japanese Society of Legal Medicine. The Kyushu University Institutional Review Board for Clinical Research approved this study (No. 29-360).

Results

Statistical difference between male and female cases

We calculated descriptive statistics for our variables [Table 1]. We found no significant difference in PMI between male and female cases but found statistically significant gender differences in the ratios of male to female, age, height, weight, BMI, BSA, longitudinal and skull transverse diameter as well as in organ weights.

Table 1: Descriptive statistics

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Equation for estimating organ weight and effect of postmortem interval on organ weight

[Table 2] showed the coefficients of equations for estimating the various organ weights. Longitudinal and transverse skull diameters were used in the equation for estimating brain weight. PMIs were used in the estimations for all organs except that for the heart. In brain and left and right lungs, the PMI regression coefficients were positive values (coefficients for the brain, left and right lung, 16.81 [95% confidence interval [CI], −0.4309–34.05], 26.31 [1.837–50.79] and 33.00 [4.729–61.27], respectively). The equation for estimating heart weight was as follows:

Table 2: Coefficients of equations for estimating organ weights

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Heart weight (g) = −571.1 + 7.56 × sex (male: 1, female: −1) + 2.36 × age (years) + 3.5 × height (cm) +9.99 × BMI (kg/m²)

The effect of the causes of death on lung weight

[Figure 1] shows the observed versus the predicted left and right lung weights. According to these figures, we checked the causes of death of the cases in which the residual error was large. We included the intracranial injury and bleeding causes such as brain contusion and subarachnoid haemorrhage (intoxication due to methamphetamine, psychotropic drug and hydrogen sulfide), drowning and sudden cardiac death from the large positive residual error cases. In addition, we included the haemorrhagic shock and death from cold causes from the large negative residual error cases. Thus, we categorised the causes of death of large positive residual cases as “heavy” and those of the large negative residual cases as “light,” added these factors and created the equation for estimating lung weight again [Table 3], [Table 4] and [Figure 2]. Except for gender, the same factors were adopted for the equations. The “heavy” and “light” causes of death were used in the equations, and the coefficients of these values were positive in “heavy” and negative in “light” cases [Table 3] and [Table 4].

Figure 1: Observed versus predicted left (a) and right (b) lung weights without consideration for the cause of death. White square, cases with causes of death thought to cause organ congestion (heavy); white triangle, cases with causes of death not thought to cause organ congestion (light); black circle, others

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Table 3: Coefficients of the equation for estimating left lung weight considering the causes of death

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Table 4: Coefficients of the equation for estimating right lung weight considering the causes of death

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Figure 2: Observed versus predicted left (a) and right (b) lung weights considering the cause of death. White square, cases with causes of death thought to cause organ congestion (heavy); white triangle, cases with causes of death not thought to cause organ congestion (light); black circle, others

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The effects of postmortem interval on brain and lung weights in the cases with postmortem intervals within 1 day

[Table 5], [Table 6], [Table 7] show the regression coefficients of equations for estimating the brain and lung weights in the cases with PMIs within 1 day. The same factors were used in the equation for the left and right lung weight, including PMI and “heavy” and “light” cases. Those coefficients' values were positive in PMI and “heavy” cases, and negative in “light” cases [Table 6] and [Table 7].

Table 5: Coefficients of the equation for estimating brain weight in the cases with postmortem intervals within 1 day

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Table 6: Coefficients of the equation for estimating left lung weight in cases with postmortem intervals within 1 day

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Table 7: Coefficients of the equation for estimating right lung weight in the cases with postmortem intervals within 1 day

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The equation for estimating left lung weight was as follows:

Left lung weight (g) = −836.6 + 25.33 × sex (male: 1, female: −1) + 158.2 × PMI (days) + 7.021 × height (cm) + 4.346 × BMI (kg/m²) + 81.13 × heavy (yes: 1, no: 0) −173.6 × light (yes: 1, no: 0)

The equation for estimating right lung weight was as follows:

Right lung weight (g) = −879.3 + 38.44 × sex (male: 1, female: −1) + 130.4 × PMI (days) + 7.461 × height (cm) + 7.401 × BMI (kg/m²) + 61.25 × heavy (yes: 1, no: 0) − 207.7 × light (yes: 1, no: 0)

In addition, PMI was used in the equation for the brain weight, and the PMI coefficients were positive [Table 5].{Table 5}

The equation for estimating brain weight was as follows:

Brain weight (g) = −429.4 + 19.81 × sex (male: 1, female: −1) −2.32 × age (years) +73.44 × PMI (days) + 5.139 × height (cm) + 2.899 × BMI (kg/m²) + 22.9 × longitudinal diameter of the skull (cm) + 39.12 × transverse diameter of the skull (cm).

Discussion

The existing studies about Japanese organ weights^{[9],[10],[11],[12],[13],[14],[15],[16],[17]} have not focused on the association between organ weights and PMIs. Organ weights usually decrease as the PMI increases due to dehydration. To estimate the organ weight at the time of death, the effect of PMI and the cause of death need to be clarified.

Here, we created equations for estimating organ weights using PMIs [Table 2]. For the sake of simplicity, we did not use natural logarithms for each factor. Therefore, the effect of factors was easy to understand, but we could not easily compare the results of this study to those of our previous study.^[6] However, like in our previous study,^[6] the adjusted R²s of heart and liver showed higher values, and those of lung and spleen showed the lower values. This was the first study to use longitudinal and transverse skull diameters to estimate brain weights. We chose these factors for the equation because they directly limit the brain size. In our previous study,^[6] the PMI coefficients were negative probably because we did not consider these factors. In this study, the PMI coefficients showed positive values, which suggested that the brain weight increases with the PMI increments. Brain oedema occurs during the early post-mortem period and the brain weight probably increases with the PMI during the 1^st day.^[1],[18] The PMI coefficients of the equations for left and right lungs also showed positive values; therefore, lung weights increased with the PMI increments probably due to pulmonary congestion and oedema.^{[3],[19],[20]} The PMI was used in the equation for estimating organ weight except for the heart weight because the heart mostly comprises muscles and dehydration of these muscles may not occur as quickly as that of others.

As [Figure 1] shows, many cases had large positive and negative residual errors on the observed versus predicted left and right lung weights. Therefore, we checked the cases and found that the causes of death (intracranial injury and bleeding, intoxication, drowning and sudden cardiac death) tended to be those that result in heavier lung weights.^[21] The cases with large negative residual errors included haemorrhagic shock and death from cold as the causes of death, causes that are associated with lighter lung weights.^[21] Therefore, we included these causes of death in the equation for estimating the lung weight and the coefficients of these values were positive in “heavy” and negative in “light” cases. We obtained better adjusted R² and RMSE after considering the causes of death (without considering the causes of death 0.1701 and 193.6 for the left lung, and 0.1780 and 223.6 for the right lung, respectively; and after considering the causes of death 0.3174 and 175.5 for the left lung, and 0.3186 and 203.6 for the right lung, respectively). We focused on the cases with PMIs within 1 day because brain oedema and pulmonary congestion and oedema are thought to occur during the early post-mortem period; and because the PMI coefficient on the equation for estimating brain weight showed a positive value without statistical significance, and the equation for estimating lung weights did not include PMIs when we added the causes of death. Consequently, the effects of PMI and the cause of death on brain and lung weights became clear. As discussed above, the PMIs had positive coefficient values in the equations for estimating brain and lung weights, which suggested increases in the brain and lung weights with the PMI increments within the first post-mortem day. In contrast, we did not include PMIs in the equation for estimating brain and lung weights in the cases of with PMIs longer than 1 day (data not shown). We think that brain oedema and pulmonary congestion and oedema occur during the early post-mortem period, within the first day, although we were not able to pinpoint the moment in time accurately. In addition to PMI, we included “heavy” and “light” causes of death in the equation for estimating the lung weights, observed that the coefficients of “heavy” values were positive and those of “light” values were negative confirming our considerations.

In conclusion, we generated equations for estimating organ weights using the PMI. Longitudinal and transverse skull diameters were useful for making the equation for estimating brain weight more reliable. From our results on the observed versus predicted lung weights, causes of death such as intracranial injury and bleeding, intoxication, drowning, and sudden cardiac death make the lung weight heavier, and haemorrhagic shock and death from cold make it lighter. During the first PMI day, the brain and lung weights increased with time. To the best of our knowledge, this was the first statistical report showing increases in brain weight during the early post-mortem period. Although the number of cases in this study was small, our uniform data obtained from autopsies conducted by a single forensic pathologist resulted in interesting statistical facts. All the factors discussed in this study showed statistically significant differences between male and female cases except for the PMI [Table 1]. The unevenness in the number of cases between gender, and the age differences may have affected the results; however, we tried to avoid these biases as much as possible by considering gender and age as factors for estimating organ weights. Further statistical investigations about the effects of PMI and the causes of death on organ weight are required for better understanding of the mechanisms of death and post-mortem changes.

Acknowledgment

The authors would like to thank Naomi Sameshima (Kyushu University) for her assistance with the autopsies and Enago (www.enago.jp) for the English language review.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

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