Delayed Diagnosis of Serious Pediatric Emergency Conditions

严重儿科紧急情况的延迟诊断

• 批准号: 10246799
• 负责人: Kenneth Michelson
• 金额: $ 15.34万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10246799
• 关键词: Delayed; Diagnosis; Serious; Pediatric; Emergency

PROJECT SUMMARY Delays in diagnosis represent a major cause of morbidity in the emergency department (ED) setting. Children are likely at increased risk of delayed diagnosis in the ED because symptoms and signs of serious disease may be nonspecific, and because as many as 95% of children visit EDs that primarily evaluate adults. Rates of delayed diagnosis among children in the ED are unknown, but likely vary substantially across settings. Because delayed diagnosis represents a major threat to patient safety, focused research is needed to determine its incidence, risk factors, and outcomes in children visiting EDs. The major objective of this project is to determine patient and hospital attributes that increase the risk for delayed diagnosis of serious conditions among children visiting EDs, and to determine the differences in patient outcomes when delayed diagnosis occurs. The project’s specific aims are (1) to refine and validate an algorithm for accurately detecting delayed diagnosis of three representative serious conditions (appendicitis, new-onset diabetic ketoacidosis, and sepsis) in billing claims data; (2) to determine the incidence, between-hospital variability, and risk factors for delayed diagnosis; and (3) to determine condition-specific outcomes of delay. Aim 1 will refine the claims detection algorithm and compare its performance with manual record review; it will be conducted at multiple collaborating centers. Aims 2 and 3 will use the Agency for Healthcare Research and Quality (AHRQ) Statewide ED and Inpatient Databases for multiple states to apply the algorithm to identify delayed diagnoses. Multilevel models will be constructed to assess predictors of delay and related health and utilization outcomes including need for surgical procedures, critical care interventions, prolonged hospital stays, and death. This study will directly address a top AHRQ research priority, improving health care patient safety through identification of risks, hazards, and harms. We will focus exclusively on children, an AHRQ priority population. The results of this research will directly allow more widespread detection and surveillance of diagnostic delays for critical high-risk conditions, and hone methods to expand to others. This approach utilizing a claims detection method would allow the kind of monitoring of error that is a fundamental property of a learning health system. The Principal Investigator, Dr. Kenneth Michelson, is an early career physician-scientist with a strong clinical background in Pediatrics and Emergency Medicine. This award will foster his development as a researcher with content expertise in diagnostic error, sophisticated outcomes analysis using multilevel modeling, large database analytics, and implementation science. A strong mentorship team composed of experienced biostatistical, quantitative, emergency medicine, and diagnostic error experts support the project and foster Dr. Michelson’s career development toward independent research.

项目摘要 延误诊断是急诊科（艾德）发病的主要原因。儿童 在艾德，由于严重疾病的症状和体征， 可能是非特异性的，因为多达95%的儿童访问ED，主要评估成人。率 艾德儿童中的延迟诊断尚不清楚，但可能在不同环境中差异很大。 由于延迟诊断对患者安全构成重大威胁，因此需要进行重点研究， 确定其发病率，危险因素，并在儿童访问ED的结果。本项目的主要目标是 该项目旨在确定增加延迟诊断风险的患者和医院属性， 严重的情况下，儿童访问ED，并确定患者的结果的差异 当诊断延迟时。该项目的具体目标是（1）完善和验证算法， 准确检测三种代表性严重疾病（阑尾炎、新发 糖尿病酮症酸中毒和脓毒症）在账单索赔数据;（2）确定医院间的发病率 变异性和延迟诊断的风险因素;（3）确定延迟的特定条件结果。 Aim 1将改进索赔检测算法，并将其性能与手动记录审查进行比较;它将 在多个合作中心开展。目标2和3将利用保健研究机构， 质量（AHRQ）全州艾德和多个州的住院患者数据库，以应用算法识别 延迟诊断将建立多层次模型，以评估延误和相关健康的预测因素， 使用结果，包括需要外科手术、重症监护干预、住院时间延长 停留，死亡。这项研究将直接解决AHRQ的首要研究重点，改善医疗保健患者 通过识别风险、危害和伤害来确保安全。我们将专门关注儿童， AHRQ优先人群。这项研究的结果将直接允许更广泛的检测 和监测诊断延误的关键高风险条件，并磨练方法，以扩大到 他人这种利用索赔检测方法的方法将允许对错误的监控， 学习健康系统的基本属性。首席研究员Kenneth Michelson博士是一名 具有儿科和急诊医学临床背景的早期职业医生-科学家。这 该奖项将促进他作为一个研究人员的发展，在诊断错误，复杂的内容专业知识， 使用多层次建模、大型数据库分析和实施科学进行结果分析。一个强大 导师团队由经验丰富的生物统计，定量，急诊医学和诊断 错误专家支持该项目，并促进迈克尔逊博士的职业发展，走向独立研究。