TY - JOUR

T1 - Multivariate linear regression modelling of lung weight in 24,056 Swedish medico-legal autopsy cases

AU - Gustafsson, Torfinn

AU - Eriksson, Anders

AU - Clinical Sciences, Malmö, Lund University, Department

N1 - Copyright © 2016 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

PY - 2017/2

Y1 - 2017/2

N2 - Heavy combined lung weight at autopsy is a non-specific autopsy finding associated with certain causes of death such as intoxication. There is however no clear definition of what constitutes "heavy" lung weight. Different reference values have been suggested but previous studies have been limited by small select populations and only univariate regression has been attempted. The aim of this study was to create a model to estimate lung weight from decedent parameters. We identified all cases >18 years age autopsied at the Swedish National Board of Forensic Medicine from 2000 through 2013, excluding cases with a post-mortem interval >5 days as well as cases with extreme values, totalling 24,056 cases. We analysed body weight, body height, sex, age, BMI, BSA as well as untransformed and transformed lung weight. The analysis was stratified for sex. We evaluated the fit of the models and that the model assumptions were not violated. We set out to apply the model with the highest residual sum of squares to derive limits for heavy lungs. In univariate regression BSA and height showed best performance. The final model included height, weight and age group. After excluding large standardized residuals (>3, <-3) the final model achieved R2 of 0.132 and 0.106 for women and men respectively. While we managed to create a multivariate model its performance was poor, possibly a fact reflective of the physiological nature of the lungs and in turn its variability in fluid content. Linear regression is a poor model for estimating lung weight in an unselected population.

AB - Heavy combined lung weight at autopsy is a non-specific autopsy finding associated with certain causes of death such as intoxication. There is however no clear definition of what constitutes "heavy" lung weight. Different reference values have been suggested but previous studies have been limited by small select populations and only univariate regression has been attempted. The aim of this study was to create a model to estimate lung weight from decedent parameters. We identified all cases >18 years age autopsied at the Swedish National Board of Forensic Medicine from 2000 through 2013, excluding cases with a post-mortem interval >5 days as well as cases with extreme values, totalling 24,056 cases. We analysed body weight, body height, sex, age, BMI, BSA as well as untransformed and transformed lung weight. The analysis was stratified for sex. We evaluated the fit of the models and that the model assumptions were not violated. We set out to apply the model with the highest residual sum of squares to derive limits for heavy lungs. In univariate regression BSA and height showed best performance. The final model included height, weight and age group. After excluding large standardized residuals (>3, <-3) the final model achieved R2 of 0.132 and 0.106 for women and men respectively. While we managed to create a multivariate model its performance was poor, possibly a fact reflective of the physiological nature of the lungs and in turn its variability in fluid content. Linear regression is a poor model for estimating lung weight in an unselected population.

KW - Organ weight

KW - Forensic Pathology

KW - Lung/pathology

KW - Multivariate Analysis

KW - Organ Size

KW - Sweden

U2 - 10.1016/j.jflm.2016.12.001

DO - 10.1016/j.jflm.2016.12.001

M3 - Journal article

C2 - 28040667

VL - 46

SP - 20

EP - 22

JO - Journal of Forensic and Legal Medicine

JF - Journal of Forensic and Legal Medicine

SN - 1752-928X

ER -