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Identifying Homogeneous Patterns of Injury in Paediatric Trauma Patients to Improve Risk-Adjusted Models of Mortality and Functional Outcomes

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  • Joanna F. Dipnall

    (School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia
    School of Medicine, Deakin University, Geelong, Victoria 3220, Australia)

  • Belinda J. Gabbe

    (School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia
    Health Data Research UK, Swansea University Medical School, Swansea University, Swansea SA2 8PP, UK)

  • Warwick J. Teague

    (Trauma Service, The Royal Children’s Hospital, Melbourne, Victoria 3052, Australia
    Department of Paediatrics, University of Melbourne, Melbourne, Victoria 3052, Australia
    Surgical Research Group, Murdoch Children’s Research Institute, Melbourne, Victoria 3052, Australia)

  • Ben Beck

    (School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia
    Faculty of Medicine, Laval University, Quebec City, QC G1V 0A6, Canada)

Abstract

Injury is a leading cause of morbidity and mortality in the paediatric population and exhibits complex injury patterns. This study aimed to identify homogeneous groups of paediatric major trauma patients based on their profile of injury for use in mortality and functional outcomes risk-adjusted models. Data were extracted from the population-based Victorian State Trauma Registry for patients aged 0–15 years, injured 2006–2016. Four Latent Class Analysis (LCA) models with/without covariates of age/sex tested up to six possible latent classes. Five risk-adjusted models of in-hospital mortality and 6-month functional outcomes incorporated a combination of Injury Severity Score (ISS), New ISS (NISS), and LCA classes. LCA models replicated the best log-likelihood and entropy > 0.8 for all models (N = 1281). Four latent injury classes were identified: isolated head; isolated abdominal organ; multi-trauma injuries, and other injuries. The best models, in terms of goodness of fit statistics and model diagnostics, included the LCA classes and NISS. The identification of isolated head, isolated abdominal, multi-trauma and other injuries as key latent paediatric injury classes highlights areas for emphasis in planning prevention initiatives and paediatric trauma system development. Future risk-adjusted paediatric injury models that include these injury classes with the NISS when evaluating mortality and functional outcomes is recommended.

Suggested Citation

  • Joanna F. Dipnall & Belinda J. Gabbe & Warwick J. Teague & Ben Beck, 2020. "Identifying Homogeneous Patterns of Injury in Paediatric Trauma Patients to Improve Risk-Adjusted Models of Mortality and Functional Outcomes," IJERPH, MDPI, vol. 17(3), pages 1-20, January.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:3:p:892-:d:314860
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    References listed on IDEAS

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    1. Alexis Dinno, 2015. "Nonparametric pairwise multiple comparisons in independent groups using Dunn's test," Stata Journal, StataCorp LP, vol. 15(1), pages 292-300, March.
    2. Gilles Celeux & Gilda Soromenho, 1996. "An entropy criterion for assessing the number of clusters in a mixture model," Journal of Classification, Springer;The Classification Society, vol. 13(2), pages 195-212, September.
    3. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    4. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
    5. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
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