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: 10598082
• 负责人: Stephanie Ruest
• 金额: $ 40.57万
• 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/10598082
• 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; Institution; Investigation; Literature; Machine Learning; Methodology; Methods; Modeling; Multicenter Studies; Natural Language Processing; Outcome; Patient Admission; Patients; Pattern; Pediatric Hospitals; Population; Provider; Public Health; Radiology Specialty; Recording of previous events; Recurrence; Reference Standards; Registries; Reporting; Rhode Island; Risk Assessment; Skin Tissue; Soft Tissue Injuries; Specialist; Specificity; Structure; Techniques; Text; Time; Training; Trauma; Validation; child protection; clinical decision-making; clinical predictors; cohort; deep neural network; demographics; design; disparity reduction; 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.

项目概要 虐待和忽视儿童是最严重的儿科公共卫生危机之一，几乎影响到 七分之一的儿童患有骨折，是继皮肤和软组织损伤之后第二大常见的虐待性伤害。 由虐待和无意机制引起的骨折类型之间有很多重叠。 虐待儿童的情况很复杂，需要准确了解儿童中典型的儿童伤害模式 许多研究评估了历史背景、机制、社会人口统计和发展能力。 骨折和虐待之间的关系集中于特定的骨折类型，仅限于患有以下疾病的儿童 预先定义的虐待性伤害或仅包括入院患者和/或相对较小的队列，从而限制 结论并引起对频谱偏差的担忧此外，先前的文献已经表明了隐性和显性。 在识别和评估虐待行为时与社会人口因素相关的偏见，可能导致过度 此外，超过 75% 的儿童寻求急诊治疗。 一般而言，急诊科由未接受儿童发展和虐待方面专门培训的服务提供者提供，多达五分之一的儿童 尽管虐待性骨折很常见，但在一般急诊室中可能会漏掉虐待性骨折的情况。 诊断的潜在局限性和偏差，没有经过验证的临床决策规则（CDR） 协助实时识别因虐待而出现骨折的儿童。 我们的长期目标是开发一种经过验证的 CDR，可用于评估受伤儿童 我们的主要目标是利用梯度增强来协助识别滥用性骨折表现。 决策树集成开发 CDR，该 CDR 将识别与滥用高度相关的骨折表现 就诊于急诊室 (ED) 护理的 ≤5 岁患者 机构儿童保护。 包含全面的虐待儿童专家调查结果的数据库将用作参考 标准。研究目标将通过以下方式实现：1）分析电子健康中的结构化变量。 在孩之宝儿童医院 (HCH) ED 和 HCH Child 评估的骨折患者记录 (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) 技术来识别预测变量以推导和迭代完善 CDR，以及 4) 使用不同的 HCH 患者队列验证该 CDR 该项目的预期直接结果是。 开发完善的 CDR 来识别与滥用行为高度相关的骨折表现 ≤ 5 岁的儿童将有助于设计前瞻性多中心随访研究以进行广泛验证。 CDR 识别高风险患者表现的能力，改进潜在潜在风险的实时临床检测 虐待性伤害，并减少临床决策中的差异。