Derivation of a Clinical Prediction Rule for Pediatric Abusive Fractures

项目总结 虐待和忽视儿童是最严重的儿科公共卫生危机之一，影响到近 每7个孩子中就有1个。骨折是仅次于皮肤和软组织损伤的第二种最常见的虐待伤害，还有 滥用和无意机制造成的骨折类型有许多重叠之处。的诊断。 虐待儿童是复杂的，需要准确地了解典型的儿科伤害模式 历史、机制、社会人口结构和发展能力的背景。许多研究评估 骨折和虐待之间的关系侧重于特定的骨折类型，仅限于患有以下疾病的儿童 预先定义的虐待伤害或仅包括住院患者和/或相对较小的队列，因此限制 结论和对光谱偏差的关注。此外，先前的文献表明，隐含和明确的 在确定和评价虐待行为时与社会人口因素有关的偏见，可能导致过度-- 以及在一些人群中对虐待的诊断不足。此外，超过75%寻求急诊科护理的儿童 一般情况下，没有接受过儿童发展和虐待专门培训的机构提供的精神分裂症，每5名儿童中就有1人 在一般的急诊情况下，可能会遗漏有虐待性骨折的病例。尽管经常发生侵袭性骨折和 在做出诊断时的潜在限制和偏见，没有经过验证的临床决策规则(CDR) 协助临床医生实时识别因虐待而出现骨折症状的儿童。 我们的长期目标是开发一种有效的CDR，可供临床医生评估受伤儿童 协助识别虐待性骨折表现。我们的主要目标是利用梯度增强 决策树集成以开发CDR，该CDR将识别高度关注滥用的骨折表现 在急诊科(ED)就诊的患者中，≤为5年。制度性的儿童保护 将使用包含全面专家虐待儿童调查结果的数据库作为参考 标准的。研究目标将通过1)分析电子健康中的结构变量来实现 在孩之宝儿童医院(HCH)急诊科和儿童医院评估的骨折患者的记录(EHR) 使用描述性统计的保护计划(CPP)，2)应用自然语言处理(NLP)技术 从临床叙述和放射学报告中提取数据以生成文本衍生变量，3)使用 机器学习(ML)技术，用于识别预测变量，以推导和迭代改进CDR，以及4) 在不同的六氯环己烷患者队列中验证这一CDR。这个项目的预期直接结果是 开发了一种改进的CDR，以识别高度关注滥用的骨折表现 儿童≤5岁。这将为设计一项用于广泛验证的前瞻性多中心随访研究提供信息 CDR识别高危患者表现的能力，改进潜在的临床实时检测 虐待伤害，并减少临床决策中的差异。

PROJECT SUMMARY Child abuse and neglect represent one of the most serious pediatric public health crises, affecting nearly 1 in 7 children. Fractures are the 2nd most common abusive injury after skin and soft tissue injuries and there is much overlap between the types of fractures caused by abuse and unintentional mechanisms. The diagnosis of child abuse is complex and necessitates an accurate understanding of typical pediatric injury patterns within the context of history, mechanism, socio-demographics, and developmental capabilities. Many studies evaluating the relationship between fractures and abuse focused on specific fracture types, were restricted to children with a pre-defined abusive injury or included only admitted patients, and/or relatively small cohorts, thus limiting conclusions and raising concerns of spectrum bias. Additionally, prior literature has shown implicit and explicit biases related to socio-demographic factors in the identification and evaluation of abuse, likely resulting in over- and underdiagnosis of abuse in some populations. Furthermore, over 75% of children seeking ED care are seen in general ED’s by providers without specialized training in child development and abuse, and up to 1 in 5 children with abusive fractures may be missed in a general ED setting. Despite the frequency of abusive fractures and the potential limitations and biases in making the diagnosis, there are no validated clinical decision rules (CDRs) to assist clinicians in the real-time identification of children with fracture presentations associated with abuse. Our long-term goal is to develop a validated CDR that can be used by clinicians evaluating injured children to assist in the identification of abusive fracture presentations. Our primary objective is to utilize gradient boosted decision tree ensembles to develop a CDR that will identify fracture presentations highly concerning for abuse among patients ≤5 years presenting for emergency department (ED) care. An institutional child protection database that includes outcomes of thorough expert child abuse investigations will be used as a reference standard. The study objectives will be accomplished by 1) analyzing structured variables in the electronic health record (EHR) of patients with fractures evaluated in the Hasbro Children’s Hospital (HCH) ED and HCH Child Protection Program (CPP) using descriptive statistics, 2) applying natural language processing (NLP) techniques to extract data from clinical narratives and radiology reports to generate text-derived variables, 3) employing machine learning (ML) techniques to identify predictor variables to derive and iteratively refine a CDR, and 4) validating this CDR with a different HCH cohort of patients. The expected immediate outcome of this project is the development of a refined CDR to identify fracture presentations that are highly concerning for abuse among children ≤5 years old. This will inform the design of a prospective multi-center follow-up study for broad validation of CDR’s ability to identify high risk patient presentations, improve real-time clinical detection of potentially abusive injuries, and decrease disparities in clinical decision making.