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BMC Emergency Medicine
Published in: BMC Emergency Medicine 1/2019

Open Access 01-12-2019 | Central Nervous System Trauma | Research article

Grading and assessment of clinical predictive tools for paediatric head injury: a new evidence-based approach

Authors: Mohamed Khalifa, Blanca Gallego

Published in: BMC Emergency Medicine | Issue 1/2019

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Abstract

Background

Many clinical predictive tools have been developed to diagnose traumatic brain injury among children and guide the use of computed tomography in the emergency department. It is not always feasible to compare tools due to the diversity of their development methodologies, clinical variables, target populations, and predictive performances. The objectives of this study are to grade and assess paediatric head injury predictive tools, using a new evidence-based approach, and to provide emergency clinicians with standardised objective information on predictive tools to support their search for and selection of effective tools.

Methods

Paediatric head injury predictive tools were identified through a focused review of literature. Based on the critical appraisal of published evidence about predictive performance, usability, potential effect, and post-implementation impact, tools were evaluated using a new framework for grading and assessment of predictive tools (GRASP). A comprehensive analysis was conducted to explain why certain tools were more successful.

Results

Fourteen tools were identified and evaluated. The highest-grade tool is PECARN; the only tool evaluated in post-implementation impact studies. PECARN and CHALICE were evaluated for their potential effect on healthcare, while the remaining 12 tools were only evaluated for predictive performance. Three tools; CATCH, NEXUS II, and Palchak, were externally validated. Three tools; Haydel, Atabaki, and Buchanich, were only internally validated. The remaining six tools; Da Dalt, Greenes, Klemetti, Quayle, Dietrich, and Güzel did not show sufficient internal validity for use in clinical practice.

Conclusions

The GRASP framework provides clinicians with a high-level, evidence-based, comprehensive, yet simple and feasible approach to grade, compare, and select effective predictive tools. Comparing the three main tools which were assigned the highest grades; PECARN, CHALICE and CATCH, to the remaining 11, we find that the quality of tools’ development studies, the experience and credibility of their authors, and the support by well-funded research programs were correlated with the tools’ evidence-based assigned grades, and were more influential, than the sole high predictive performance, on the wide acceptance and successful implementation of the tools. Tools’ simplicity and feasibility, in terms of resources needed, technical requirements, and training, are also crucial factors for their success.
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Metadata
Title
Grading and assessment of clinical predictive tools for paediatric head injury: a new evidence-based approach
Authors
Mohamed Khalifa
Blanca Gallego
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Emergency Medicine / Issue 1/2019
Electronic ISSN: 1471-227X
DOI
https://doi.org/10.1186/s12873-019-0249-y

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