Raising suspicion of maltreatment from burns: derivation and validation of the BuRN-Tool

Alison M. Kemp*, Linda Hollén, Alan M. Emond, Diane Nuttall, David Rea, Sabine Maguire

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

9 Citations (Scopus)
291 Downloads (Pure)


Background: 10–25% of childhood burns arise from maltreatment. Aim: To derive and validate a clinical prediction tool to assist the recognition of suspected maltreatment. Methods: Prospectively collected data from 1327 children with burns were analyzed using logistic regression. Regression coefficients for variables associated with ‘referral for child maltreatment investigation’ (112 cases) in multivariable analyses were converted to integers to derive the BuRN-Tool, scoring each child on a continuous scale. A cut-off score for referral was established from receiver operating curve analysis and optimal sensitivity and specificity values. We validated the BuRN-Tool on 787 prospectively collected novel cases. Results: Variables associated with referral were: age <5 years, known to social care, concerning explanation, full thickness burn, uncommon body location, bilateral pattern and supervision concern. We established 3 as cut-off score, resulting in a sensitivity and specificity for scalds of 87.5% (95% CI:61.7–98.4) and 81.5% (95% CI:77.1–85.4) respectively and for non-scalds sensitivity was 82.4% (95%CI:65.5–93.2) and specificity 78.7% (95% CI:73.9–82.9) when applied to validation data. Area under the curve was 0.87 (95% CI:0.83–0.90) for scalds and 0.85 (95% CI:0.81–0.88) for non-scalds. Conclusion: The BuRN-Tool is a potential adjunct to clinical decision-making, predicting which children warrant investigation for child maltreatment. The score is simple and easy to complete in an emergency department setting.

Original languageEnglish
Pages (from-to)335-343
Number of pages9
Issue number2
Early online date14 Sep 2017
Publication statusPublished - 1 Mar 2018


  • Maltreatment
  • Clinical prediction tool
  • Child
  • Burn

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