Multivariate linear regression modelling of lung weight in 24,056 Swedish medico-legal autopsy cases
2017 (English)In: Journal of Forensic and Legal Medicine, ISSN 1752-928X, E-ISSN 1878-7487, Vol. 46, 20-22 p.Article in journal (Refereed) Published
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 R-2 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.
Place, publisher, year, edition, pages
ELSEVIER SCI LTD , 2017. Vol. 46, 20-22 p.
Lungs, Autopsy, Weight, Linear regression
IdentifiersURN: urn:nbn:se:umu:diva-133445DOI: 10.1016/j.jflm.2016.12.001ISI: 000397021900004PubMedID: 28040667OAI: oai:DiVA.org:umu-133445DiVA: diva2:1088662