Literature DB >> 28918905

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

Alison M Kemp1, Linda Hollén2, Alan M Emond2, Diane Nuttall3, David Rea4, Sabine Maguire3.   

Abstract

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 <5years, 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.
Copyright © 2017. Published by Elsevier Ltd.

Entities:  

Keywords:  Burn; Child; Clinical prediction tool; Maltreatment

Mesh:

Year:  2017        PMID: 28918905     DOI: 10.1016/j.burns.2017.08.018

Source DB:  PubMed          Journal:  Burns        ISSN: 0305-4179            Impact factor:   2.744


  4 in total

1.  Development and Validation of a Natural Language Processing Tool to Identify Injuries in Infants Associated With Abuse.

Authors:  Gunjan Tiyyagura; Andrea G Asnes; John M Leventhal; Eugene D Shapiro; Marc Auerbach; Wei Teng; Emily Powers; Amy Thomas; Daniel M Lindberg; Justin McClelland; Carol Kutryb; Thomas Polzin; Karen Daughtridge; Virginia Sevin; Allen L Hsiao
Journal:  Acad Pediatr       Date:  2021-11-12       Impact factor: 2.993

2.  Evaluation of the efficacy and impact of a clinical prediction tool to identify maltreatment associated with children's burns.

Authors:  Linda Hollen; Verity Bennett; Dianne Nuttall; Alan M Emond; Alison Kemp
Journal:  BMJ Paediatr Open       Date:  2021-02-12

3.  A natural language processing and deep learning approach to identify child abuse from pediatric electronic medical records.

Authors:  Akshaya V Annapragada; Marcella M Donaruma-Kwoh; Ananth V Annapragada; Zbigniew A Starosolski
Journal:  PLoS One       Date:  2021-02-26       Impact factor: 3.240

4.  Identifying children exposed to maltreatment: a systematic review update.

Authors:  Jill R McTavish; Andrea Gonzalez; Nancy Santesso; Jennifer C D MacGregor; Chris McKee; Harriet L MacMillan
Journal:  BMC Pediatr       Date:  2020-03-07       Impact factor: 2.125

  4 in total

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