Prediction of mortality risk in victims of violent crimes. (December 2017)

• Record Type: Journal Article
• Authors: Gedeborg, Rolf; Svennblad, Bodil; Byberg, Liisa; Michaëlsson, Karl; Thiblin, Ingemar
• Is Part Of: Forensic science international. Volume 281(2017)
• Page Start: 92
• Page End: 97
• Publication Date: 2017-12
• Subjects: Bayesian inference -- Mortality -- Violent crime -- Forensic medicine; Medical jurisprudence -- Periodicals; Chemistry, Forensic -- Periodicals; Forensic Medicine -- Periodicals; Médecine légale -- Périodiques; Chimie légale -- Périodiques; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; Chemistry, Forensic; Medical jurisprudence; Electronic journals; Periodicals; Electronic journals; 614.1
• DOI: 10.1016/j.forsciint.2017.10.015
• Languages: English
• ISSNs: 0379-0738

Abstract

Highlights: Mortality risk after violent crimes can be accurately estimated using administrative data. Bayesian regression models increase the usability of injury data at the individual level. The generalizability of Bayesian models to different countries needs to be confirmed. Abstract: Background: To predict mortality risk in victims of violent crimes based on individual injury diagnoses and other information available in health care registries. Methods: Data from the Swedish hospital discharge registry and the cause of death registry were combined to identify 15, 000 hospitalisations or prehospital deaths related to violent crimes. The ability of patient characteristics, injury type and severity, and cause of injury to predict death was modelled using conventional, Lasso, or Bayesian logistic regression in a development dataset and evaluated in a validation dataset. Results: Of 14, 470 injury events severe enough to cause death or hospitalization 3.7% (556) died before hospital admission and 0.5% (71) during the hospital stay. The majority (76%) of hospital survivors had minor injury severity and most (67%) were discharged from hospital within 1 day. A multivariable model with age, sex, the ICD-10 based injury severity score (ICISS), cause of injury, and major injury region provided predictions with very good discrimination (C-index = 0.99) and calibration. Adding information on major injury interactions further improved model performance. Modeling individual injury diagnoses did not improve predictions over the combined ICISS score. Conclusions: Mortality risk after violent crimes can be accurately estimated using administrative data. The use of Bayesian regression models provides meaningful risk assessment with more straightforward interpretation of uncertainty of the prediction, potentially also on the individual level. This can aid estimation of incidence trends over time and comparisons of outcome of violent crimes for injury surveillance and in forensic medicine.