Derivation of a Clinical Prediction Rule for Pediatric Abusive Fractures

儿童虐待性骨折临床预测规则的推导

• 批准号: 10331949
• 负责人: Stephanie Ruest
• 金额: $ 40.7万
• 依托单位: RHODE ISLAND HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10331949
• 关键词: 5 year old; Accident and Emergency department; Accounting; Affect; Calibration; Centers of Research Excellence; Characteristics; Child; Child Abuse; Child Abuse and Neglect; Child Development; Childhood; Childhood Injury; Clinical; Clinical Data; Complex; Databases; Decision Trees; Derivation procedure; Detection; Development; Diagnosis; Electronic Health Record; Emergency Care; Emotional; Evaluation; Family; Follow-Up Studies; Fracture; Frequencies; Generations; Goals; Hospitals; Injury; Institutionalized Child; Investigation; Literature; Machine Learning; Methodology; Methods; Modeling; Multicenter Studies; Natural Language Processing; Outcome; Patients; Pattern; Pediatric Hospitals; Population; Provider; Public Health; Radiology Specialty; Recording of previous events; Recurrence; Reference Standards; Registries; Reporting; Rhode Island; Risk; Sensitivity and Specificity; Skin Tissue; Soft Tissue Injuries; Specialist; Structure; Techniques; Text; Time; Training; Trauma; Validation; child protection; clinical decision-making; cohort; deep neural network; demographics; design; explicit bias; gradient boosting; high risk; implicit bias; improved; patient health information; programs; prospective; radiological imaging; sociodemographic factors; sociodemographics; statistics; tool

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.

项目摘要 虐待和忽视儿童是最严重的儿科公共卫生危机之一， 七个孩子中有一个。骨折是继皮肤和软组织损伤之后第二常见的虐待性损伤，并且有 滥用和非故意机制造成的骨折类型之间存在很大重叠。诊断 虐待儿童是复杂的，需要准确了解典型的儿科伤害模式， 历史背景、机制、社会人口和发展能力。许多研究评估 骨折和虐待之间的关系集中在特定的骨折类型，仅限于儿童， 预先定义的虐待性损伤或仅包括入院患者和/或相对较小的队列，因此限制了 结论和提高频谱偏差的关注。此外，先前的文献已经显示了隐式和显式 在确定和评价虐待行为时与社会人口因素有关的偏见，可能导致过度的 以及对某些人群的虐待诊断不足。此外，超过75%的儿童寻求艾德护理， 一般来说，艾德的提供者没有接受过儿童发展和虐待方面的专门培训， 在一般艾德情况下可能会漏诊。尽管经常发生虐待性骨折， 诊断中的潜在局限性和偏倚，没有经过验证的临床决策规则（CDR） 帮助临床医生实时识别与虐待相关的骨折儿童。 我们的长期目标是开发一种经过验证的CDR，可供临床医生评估受伤儿童， 协助识别滥用骨折介绍。我们的主要目标是利用梯度提升 决策树集合，以开发一个CDR，该CDR将识别与滥用高度相关的骨折表现 在急诊科（艾德）治疗的≤5岁患者中。儿童保护机构 一个数据库，其中包括彻底的专家虐待儿童调查的结果将被用作参考 标准本研究的目的将借由以下方式达成：1）分析电子健康照护系统中的结构变数 在Hasbro儿童医院（HCH）艾德和HCH Child中评价的骨折患者的EHR记录 保护计划（CPP）使用描述性统计，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）采用 机器学习（ML）技术，用于识别预测变量以导出和迭代地改进CDR，以及4） 用不同的六氯环己烷患者队列验证这一CDR。该项目的预期直接成果是 开发一个完善的CDR，以确定骨折介绍，这是高度关注的滥用， 儿童≤5岁。这将为广泛验证的前瞻性多中心随访研究的设计提供信息 CDR识别高风险患者表现的能力，改善潜在的实时临床检测， 虐待性伤害，并减少临床决策的差异。