Australian Institute of Health Innovation, Faculty of Medicine and Health Sciences, Macquarie University, 75 Talavera Road, North Ryde, Sydney, NSW, 2113, Australia.
Centre for Big Data Research in Health, Faculty of Medicine, University of New South Wales, Lowy Cancer Research Centre, Level 4, Cnr High &, Botany St, Kensington, Sydney, NSW, 2052, Australia.
BMC Emerg Med. 2019 Jun 14;19(1):35. doi: 10.1186/s12873-019-0249-y.
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.
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.
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.
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.
许多临床预测工具已被开发出来，用于诊断儿童创伤性脑损伤，并指导在急诊科使用计算机断层扫描。由于其开发方法、临床变量、目标人群和预测性能的多样性，比较这些工具并不总是可行的。本研究的目的是使用新的循证方法对儿科头部损伤预测工具进行分级和评估，并为急诊临床医生提供关于预测工具的标准化客观信息，以支持他们寻找和选择有效的工具。
通过对文献的重点回顾，确定了儿科头部损伤预测工具。基于对预测性能、可用性、潜在效果和实施后影响的已发表证据的批判性评估，使用新的预测工具分级和评估框架（GRASP）对工具进行评估。进行了全面的分析，以解释为什么某些工具更成功。
确定并评估了 14 种工具。等级最高的工具是 PECARN；唯一一种在实施后影响研究中进行评估的工具。对 PECARN 和 CHALICE 进行了潜在效果评估，以评估其对医疗保健的影响，而其余 12 种工具仅评估了预测性能。有三种工具；CATCH、NEXUS II 和 Palchak，进行了外部验证。有三种工具；Haydel、Atabaki 和 Buchanich，仅进行了内部验证。其余六种工具；Da Dalt、Greenes、Klemetti、Quayle、Dietrich 和 Güzel，在临床实践中使用时没有显示出足够的内部有效性。
GRASP 框架为临床医生提供了一种高级、基于证据、全面但简单可行的方法来分级、比较和选择有效的预测工具。将三个主要的、等级最高的工具（PECARN、CHALICE 和 CATCH）与其余 11 个工具进行比较，我们发现工具开发研究的质量、作者的经验和可信度，以及由资金充足的研究计划提供的支持，与工具的循证等级分配相关，并且比单一的高预测性能更具影响力，这些因素促成了工具的广泛接受和成功实施。工具的简单性和可行性，就所需资源、技术要求和培训而言，也是其成功的关键因素。
