Derivation and Validation of the Pediatric Community-Acquired Pneumonia Severity (PedCAPS) Score

儿科社区获得性肺炎严重程度 (PedCAPS) 评分的推导和验证

基本信息

批准号：
10587951
负责人：
Todd Adam Florin
金额：
$ 108.51万
依托单位：
LURIE CHILDREN'S HOSPITAL OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-02-01 至 2028-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10587951
关键词：
18 year old Accident and Emergency department Address Adult Antibiotics Anxiety Applied Research Area Atelectasis Attention Behavior Binding Biological Markers Biometry Blood Pressure Blood capillaries C-reactive protein Calibration Caring Chest Child Child Care Childhood Clinical Clinical Research Communicable Diseases Complement Confidence Intervals Decision Making Derivation procedure Deterioration Development Diagnostic tests Disease Disease Progression Early Intervention Emergency Care Emergency research Enrollment Ensure Epidemiology Evaluation Goals Guidelines Health Hospitalization Hospitalized Child Hospitals Hour Infection Infrastructure Inpatients Institution Intervention Judgment Knowledge Lung diseases Machine Learning Medical Medical Errors Medicine Mentored Patient-Oriented Research Career Development Award National Heart, Lung, and Blood Institute Nosocomial Infections Observational Study Outcome Outpatients Patients Pediatric Hospitals Performance Phase Pleural effusion disorder Pneumonia Pneumonia Severity Index Prospective, cohort study Provider ROC Curve Recommendation Research Resources Risk Risk Estimate Risk Factors Schools Severities Severity of illness Societies Standardization Targeted Research Testing Thoracic Radiography Time Traction United States Validation Variant Viral Work adverse outcome biomarker selection clinical decision-making clinical risk cohort community acquired pneumonia cost evidence base experience high risk hospitalization rates implementation research improved innovation mortality multidisciplinary novel novel diagnostics novel therapeutic intervention pediatric emergency personalized approach predictive modeling predictive tools prevent procalcitonin prognostic tool prognostication prospective respiratory risk prediction risk stratification success tool viral detection

项目摘要

PROJECT SUMMARY Although community-acquired pneumonia (CAP) is one of the most common serious infections in children and a leading reason that children seek emergency care, no validated tools exist to predict CAP severity in children. Without objective tools, management decisions are inefficient and potentially inaccurate, resulting in unnecessary testing, treatment, and hospitalization in low-risk children or delays in critically important therapies in those at high risk of severe CAP. The long-term goal of this research is to improve risk stratification of children with CAP. In adults with CAP, the use of risk prediction rules decreases mortality and guides antibiotic decisions, while minimizing hospitalizations for those at low risk. No validated risk prediction rules exist for children presenting to the emergency department (ED) with CAP. We previously derived a 7-variable risk prediction rule in 1128 children 3 months to 18 years old who presented to a single pediatric ED with suspected CAP. To overcome limitations inherent in a rule derived in a single center, multicenter derivation and external validation of a pediatric CAP risk prediction rule is necessary to ensure generalizability and inform subsequent widespread implementation. We also found that biomarkers, including c-reactive protein, procalcitonin, proadrenomedullin, and viral detection, are associated with severe outcomes in children with CAP. It is unknown if the addition of these biomarkers to a clinical risk prediction rule will improve rule performance. Led by a multidisciplinary team of experts in CAP, pediatric emergency and hospital medicine, infectious diseases, biomarkers, epidemiology and biostatistics, prediction modeling, and machine learning, the proposed research will address these important knowledge and research gaps through the following specific aims: (1) To derive a severity risk prediction rule in a multicenter cohort of children presenting to the ED with CAP; (2) To externally validate the derived prediction rule in children with CAP; and (3) To evaluate the ability of biomarkers to improve predictive accuracy of a purely clinical risk prediction rule. This study will leverage the robust infrastructure, experience, and expertise of the Pediatric Emergency Care Applied Research Network (PECARN). We will accomplish the study aims by conducting a prospective multicenter observational study in two phases. First, we will enroll 2000 children with CAP presenting to one of 7 PECARN EDs to derive the rule over 2 years. We will then enroll 2000 children with CAP in 7 different PECARN EDs over the following 2 years to externally validate the rule. A risk prediction rule in children with CAP will be significant in (a) advancing our understanding of risk factors of CAP severity, (b) improving evidence-based management and clinical outcomes by guiding and standardizing clinical decision making, and (c) facilitating future research. This proposal is innovative as it will shift the paradigm of ED management of CAP, moving from subjective decisions toward a novel, objective approach where individualized, evidence-based risk estimates can augment and improve accuracy of clinical decision making.

项目概要尽管社区获得性肺炎 (CAP) 是儿童和儿童中最常见的严重感染之一儿童寻求紧急护理的一个主要原因是，目前尚无经过验证的工具可以预测 CAP 的严重程度孩子们。如果没有客观的工具，管理决策就会效率低下并且可能不准确，从而导致对低风险儿童进行不必要的检查、治疗和住院治疗或延误至关重要的治疗患有严重 CAP 的高风险人群。本研究的长期目标是改善风险分层患有 CAP 的儿童。在患有 CAP 的成人中，使用风险预测规则可降低死亡率并指导抗生素治疗做出决定，同时最大限度地减少低风险人群的住院治疗。不存在经过验证的风险预测规则患有 CAP 到急诊室 (ED) 就诊的儿童。我们之前得出了 7 个变量的风险 1128 名 3 个月至 18 岁儿童的预测规则，这些儿童就诊于儿科急诊室，患有以下疾病：疑似CAP。克服单中心、多中心推导规则固有的局限性儿科 CAP 风险预测规则的外部验证对于确保普遍性和提供信息是必要的随后广泛实施。我们还发现生物标志物，包括 C 反应蛋白，降钙素原、肾上腺髓质素原和病毒检测与儿童的严重后果相关帽。目前尚不清楚将这些生物标志物添加到临床风险预测规则中是否会改善规则表现。由 CAP、儿科急诊和医院医学领域的多学科专家团队领导，传染病、生物标志物、流行病学和生物统计学、预测模型和机器学习，拟议的研究将通过以下具体措施解决这些重要的知识和研究差距目标：(1) 在向急诊科就诊的多中心儿童队列中得出严重风险预测规则帽; (2) 外部验证所得出的 CAP 儿童预测规则； (3) 评估能力生物标记物以提高纯粹临床风险预测规则的预测准确性。这项研究将利用儿科紧急护理应用研究网络强大的基础设施、经验和专业知识（佩卡恩）。我们将通过在以下地区进行前瞻性多中心观察研究来实现研究目标两个阶段。首先，我们将招募 2000 名患有 CAP 的儿童，向 7 个 PECARN ED 之一进行演示，以得出规则超过2年。接下来的 2 年内，我们将在 7 个不同的 PECARN ED 中招收 2000 名 CAP 儿童从外部验证规则。 CAP 儿童的风险预测规则对于 (a) 推进我们的研究具有重要意义了解 CAP 严重程度的危险因素，(b) 改善循证管理和临床通过指导和标准化临床决策来取得成果，以及 (c) 促进未来的研究。这该提案具有创新性，因为它将改变 CAP 的 ED 管理范式，从主观转向制定一种新颖、客观的方法，其中个性化、基于证据的风险评估可以增强和提高临床决策的准确性。